diff --git a/.gitignore b/.gitignore index 873fe16d..04522bdf 100644 --- a/.gitignore +++ b/.gitignore @@ -165,6 +165,9 @@ cython_debug/ outputs/ exports/ renders/ +*.mp4 +*.gif +*.zip # tensorboard log files events.out.* @@ -173,7 +176,8 @@ events.out.* # Misc old/ -temp* +temp/ +tmp/ !temporal* .nfs* external/ @@ -187,4 +191,5 @@ camera_paths/ ._.DS_Store */**/.DS_Store */**/._.DS_Store - +masks/ +*.sif diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bdb8ff4f..f29ba4f5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,14 +3,6 @@ default_language_version: python: python3 repos: -- repo: local - hooks: - - id: add-license-headers - name: add-license-headers - entry: nerfstudio/scripts/licensing/license_headers.sh - language: script - files: '.*' - pass_filenames: false - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 hooks: @@ -24,3 +16,10 @@ repos: args: [ --fix ] - id: ruff-format types_or: [ python, pyi, jupyter ] +- repo: local + hooks: + - id: check-copyright + name: Check Copyright + entry: python3 -m nerfstudio.scripts.licensing.check_copyright + language: python + types: [ python ] diff --git a/.vscode/launch.json b/.vscode/launch.json index 6e4dc7c4..6a8e2643 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -27,127 +27,96 @@ "justMyCode": true }, { - "name": "Python: train", + "name": "Python: NeuRAD", "type": "python", "request": "launch", "program": "nerfstudio/scripts/train.py", "console": "integratedTerminal", + "justMyCode": true, "args": [ - "instant_ngp", - "--viewer.no-enable", - "--logging.event-writer=wandb" + "neurad", + "--pipeline.model.eval_num_rays_per_chunk=2048", + "--pipeline.datamanager.num_processes=0", + "--pipeline.datamanager.train-num-lidar-rays-per-batch=1280", + "--pipeline.datamanager.eval-num-lidar-rays-per-batch=128", + "--pipeline.datamanager.train_num_rays_per_batch=4096", + "--pipeline.datamanager.eval_num_rays_per_batch=4096", + "pandaset-data", + "--data=./data/pandaset", + "--dataset_end_fraction=0.2", + "--cameras=front" ] }, { - "name": "Python: train ngp", - "type": "python", - "request": "launch", - "program": "nerfstudio/scripts/train.py", - "console": "integratedTerminal", - "args": ["instant_ngp"] - }, - { - "name": "Python: train ngp nerfstudio", + "name": "Python: NeuRAD tiny", "type": "python", "request": "launch", "program": "nerfstudio/scripts/train.py", "console": "integratedTerminal", + "justMyCode": true, "args": [ - "instant_ngp", - // "--experiment_name=nerfstudio_experiment", - "--logging.local-writer.max-log-size=0", - "--viewer.no-enable" - // viewer - // "--viewer.enable", - // "--viewer.zmq-url=tcp://127.0.0.1:6001", - // "--viewer.websocket-port=8014", - // "--pipeline.model.randomize-background", - // "--pipeline.model.cone-angle=0.00390625", - // "nerfstudio-data" + "neurad", + "--pipeline.model.eval_num_rays_per_chunk=2048", + "--pipeline.model.field.grid.static.hashgrid_dim=2", + "--pipeline.model.field.grid.static.num_levels=4", + "--pipeline.model.field.grid.static.log2_hashmap_size=15", + "--pipeline.model.field.grid.actor.hashgrid_dim=2", + "--pipeline.model.field.grid.actor.num_levels=2", + "--pipeline.model.field.grid.actor.log2_hashmap_size=11", + "--pipeline.datamanager.num_processes=0", + "--pipeline.datamanager.train-num-lidar-rays-per-batch=1280", + "--pipeline.datamanager.eval-num-lidar-rays-per-batch=128", + "--pipeline.datamanager.train-num-rays-per_batch=2048", + "--pipeline.datamanager.eval-num-rays_per_batch=2048", + "pandaset-data", + "--data=./data/pandaset", + "--dataset_end_fraction=0.2", + "--cameras=front", ] }, { - "name": "Python: train ngp-friends", - "type": "python", - "request": "launch", - "program": "nerfstudio/scripts/train.py", - "console": "integratedTerminal", - "args": [ - "instant_ngp", - "--viewer.enable", - "--viewer.zmq-url=tcp://127.0.0.1:6001", - "--viewer.websocket-port=8014", - "--steps_per_save=1000000", - "--steps_per_test=1000000", - "--pipeline.model.density-field-params.base-scale=4", - "--pipeline.model.density-field-params.num-cascades=1", - "pipeline.datamanager.train-dataparser:friends-data-parser-config", - "--pipeline.datamanager.train-dataparser.scene-scale=4" - ] - }, - { - "name": "Python: train semantic_nerfw", - "type": "python", - "request": "launch", - "program": "nerfstudio/scripts/train.py", - "console": "integratedTerminal", - "args": ["semantic_nerf"] - }, - { - "name": "Python: NeuS on Replica", + "name": "Python: NeuRAD CPU", "type": "python", "request": "launch", "program": "nerfstudio/scripts/train.py", "console": "integratedTerminal", "justMyCode": false, - "env": { "CUDA_VISIBLE_DEVICES": "0" }, + "env": {"PYTORCH_ENABLE_MPS_FALLBACK": "1"}, "args": [ - "neus", - "--pipeline.model.sdf-field.inside-outside=True", - "--pipeline.model.mono-depth-loss-mult=0.1", - "--pipeline.model.mono-normal-loss-mult=0.05", - "--vis=wandb", - "sdfstudio-data", - "--data=data/sdfstudio-demo-data/replica-room0", - "--include_mono_prior=True" + "neurad", + "--pipeline.model.eval_num_rays_per_chunk=2048", + "--pipeline.model.implementation=torch", + "--pipeline.model.sampling.proposal_field_1.grid.actor.use_4d_hashgrid=False", + "--pipeline.model.sampling.proposal_field_2.grid.actor.use_4d_hashgrid=False", + "--pipeline.model.field.grid.actor.use_4d_hashgrid=False", + "--machine.device-type=mps", + "--mixed_precision=False", + "--pipeline.datamanager.num_processes=0", + "--pipeline.datamanager.train-num-lidar-rays-per-batch=128", + "--pipeline.datamanager.eval-num-lidar-rays-per-batch=128", + "--pipeline.datamanager.train_num_rays_per_batch=2048", + "--pipeline.datamanager.eval_num_rays_per_batch=2048", + "--vis=tensorboard", + "pandaset-data", + "--data=./data/pandaset", + "--dataset_end_fraction=0.2", + "--cameras=front" ] }, { - "name": "Python: NeuS-facto on Replica", + "name": "Python: Splatfacto PandaSet", "type": "python", "request": "launch", "program": "nerfstudio/scripts/train.py", "console": "integratedTerminal", - "justMyCode": false, - "env": { "CUDA_VISIBLE_DEVICES": "0" }, + "justMyCode": true, "args": [ - "neus-facto", - "--pipeline.model.sdf-field.inside-outside=True", - "--pipeline.model.mono-depth-loss-mult=0.1", - "--pipeline.model.mono-normal-loss-mult=0.05", - "--vis=wandb", - "sdfstudio-data", - "--data=data/sdfstudio-demo-data/replica-room0", - "--include_mono_prior=True" + "splatfacto", + "pandaset-data", + "--sequence=028", + "--data=./data/pandaset", + "--dataset_end_fraction=1.0", ] }, - { - "name": "Python: NeuS-facto on DTU (no mono-prior)", - "type": "python", - "request": "launch", - "program": "nerfstudio/scripts/train.py", - "console": "integratedTerminal", - "justMyCode": false, - "env": { "CUDA_VISIBLE_DEVICES": "0" }, - "args": [ - "neus-facto", - "--pipeline.model.sdf-field.inside-outside=False", - "--vis=wandb", - "sdfstudio-data", - "--data=data/sdfstudio-demo-data/dtu-scan65", - "--include_mono_prior=False", - "--auto-orient=True" - ] - } ] } diff --git a/Dockerfile b/Dockerfile index 4c00a8fa..f4bfeca9 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,6 +1,5 @@ ARG CUDA_VERSION=11.8.0 ARG OS_VERSION=22.04 -ARG USER_ID=1000 # Define base image. FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION} ARG CUDA_VERSION @@ -15,8 +14,8 @@ LABEL org.opencontainers.image.base.name="docker.io/library/nvidia/cuda:${CUDA_V # Variables used at build time. ## CUDA architectures, required by Colmap and tiny-cuda-nn. -## NOTE: All commonly used GPU architectures are included and supported here. To speedup the image build process remove all architectures but the one of your explicit GPU. Find details here: https://developer.nvidia.com/cuda-gpus (8.6 translates to 86 in the line below) or in the docs. -ARG CUDA_ARCHITECTURES=90;89;86;80;75;70;61;52;37 +## NOTE: Most commonly used GPU architectures are included and supported here. To speedup the image build process remove all architectures but the one of your explicit GPU. Find details here: https://developer.nvidia.com/cuda-gpus (8.6 translates to 86 in the line below) or in the docs. +ARG CUDA_ARCHITECTURES=90;89;86;80;75 # Set environment variables. ## Set non-interactive to prevent asking for user inputs blocking image creation. @@ -53,18 +52,16 @@ RUN apt-get update && \ libqt5opengl5-dev \ libsqlite3-dev \ libsuitesparse-dev \ - nano \ protobuf-compiler \ python-is-python3 \ python3.10-dev \ python3-pip \ qtbase5-dev \ - sudo \ vim-tiny \ - wget && \ + wget \ + && \ rm -rf /var/lib/apt/lists/* - # Install GLOG (required by ceres). RUN git clone --branch v0.6.0 https://github.com/google/glog.git --single-branch && \ cd glog && \ @@ -102,22 +99,6 @@ RUN git clone --branch 3.8 https://github.com/colmap/colmap.git --single-branch cd ../.. && \ rm -rf colmap -# Create non root user and setup environment. -RUN useradd -m -d /home/user -g root -G sudo -u ${USER_ID} user -RUN usermod -aG sudo user -# Set user password -RUN echo "user:user" | chpasswd -# Ensure sudo group users are not asked for a password when using sudo command by ammending sudoers file -RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers - -# Switch to new uer and workdir. -USER ${USER_ID} -WORKDIR /home/user - -# Add local user binary folder to PATH variable. -ENV PATH="${PATH}:/home/user/.local/bin" -SHELL ["/bin/bash", "-c"] - # Upgrade pip and install packages. RUN python3.10 -m pip install --no-cache-dir --upgrade pip setuptools pathtools promise pybind11 # Install pytorch and submodules @@ -158,15 +139,10 @@ RUN git clone --recursive https://github.com/cvg/pixel-perfect-sfm.git && \ RUN python3.10 -m pip install --no-cache-dir omegaconf # Copy nerfstudio folder and give ownership to user. -ADD . /home/user/nerfstudio -USER root -RUN chown -R user /home/user/nerfstudio -USER ${USER_ID} +ADD . /nerfstudio # Install nerfstudio dependencies. -RUN cd nerfstudio && \ - python3.10 -m pip install --no-cache-dir -e . && \ - cd .. +RUN cd /nerfstudio && python3.10 -m pip install --no-cache-dir -e . # Change working directory WORKDIR /workspace diff --git a/README.md b/README.md index 95ba60a4..d5896662 100644 --- a/README.md +++ b/README.md @@ -1,119 +1,58 @@ -

- - - - - Documentation Status - - PyPI version - - - Test Status - - - License -

+

NeuRAD: Neural Rendering for Autonomous Driving

- - - - - - nerfstudio - - - + + + + + +

- - +

CVPR 2024 highlight

+
+ + + + tyro logo + + +
-

A collaboration friendly studio for NeRFs

+
-

- - documentation - - viewer - - colab -

- - +[Quickstart](#quickstart) Β· +[Learn more](#learn-more) Β· +[Planned Features](#planned-featurestodos) -- [Quickstart](#quickstart) -- [Learn more](#learn-more) -- [Supported Features](#supported-features) +
# About -_It’s as simple as plug and play with nerfstudio!_ - -Nerfstudio provides a simple API that allows for a simplified end-to-end process of creating, training, and testing NeRFs. -The library supports a **more interpretable implementation of NeRFs by modularizing each component.** -With more modular NeRFs, we hope to create a more user-friendly experience in exploring the technology. - -This is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other's contributions. -Nerfstudio initially launched as an opensource project by Berkeley students in [KAIR lab](https://people.eecs.berkeley.edu/~kanazawa/index.html#kair) at [Berkeley AI Research (BAIR)](https://bair.berkeley.edu/) in October 2022 as a part of a research project ([paper](https://arxiv.org/abs/2302.04264)). It is currently developed by Berkeley students and community contributors. - -We are committed to providing learning resources to help you understand the basics of (if you're just getting started), and keep up-to-date with (if you're a seasoned veteran) all things NeRF. As researchers, we know just how hard it is to get onboarded with this next-gen technology. So we're here to help with tutorials, documentation, and more! - -Have feature requests? Want to add your brand-spankin'-new NeRF model? Have a new dataset? **We welcome [contributions](https://docs.nerf.studio/reference/contributing.html)!** Please do not hesitate to reach out to the nerfstudio team with any questions via [Discord](https://discord.gg/uMbNqcraFc). - -Have feedback? We'd love for you to fill out our [Nerfstudio Feedback Form](https://forms.gle/sqN5phJN7LfQVwnP9) if you want to let us know who you are, why you are interested in Nerfstudio, or provide any feedback! +This is the official code release of the CVPR 2024 paper _NeuRAD: Neural Rendering for Autonomous Driving_, building on top of [Nerfstudio](https://github.com/nerfstudio-project/nerfstudio). Besides releasing the code for our NeuRAD model, we hope that this can lay the ground-work for research on applying neural rendering methods in autonomous driving. -We hope nerfstudio enables you to build faster :hammer: learn together :books: and contribute to our NeRF community :sparkling_heart:. +In line with Nerfstudio's mission, this is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other's contributions. -## Sponsors - -Sponsors of this work includes [Luma AI](https://lumalabs.ai/) and the [BAIR commons](https://bcommons.berkeley.edu/home). - -

- - - - - - - Luma AI - - - - - - - - - - - BAIR - - - - -

+Do you have feature requests or want to add **your** new AD-NeRF model? Or maybe provide structures for a new dataset? **We welcome [contributions](https://docs.nerf.studio/reference/contributing.html)!** # Quickstart -The quickstart will help you get started with the default vanilla NeRF trained on the classic Blender Lego scene. +The quickstart will help you get started with the NeuRAD model on a PandaSet sequence. For more complex changes (e.g., running with your own data/setting up a new NeRF graph), please refer to our [references](#learn-more). ## 1. Installation: Setup the environment ### Prerequisites -You must have an NVIDIA video card with CUDA installed on the system. This library has been tested with version 11.8 of CUDA. You can find more information about installing CUDA [here](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html) +Our installation steps largely follow Nerfstudio, with some added dataset-specific dependencies. You must have an NVIDIA video card with CUDA installed on the system. This library has been tested with version 11.8 of CUDA. You can find more information about installing CUDA [here](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html). ### Create environment -Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/miniconda.html) before proceeding. +NeuRAD requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/miniconda.html) before proceeding. ```bash -conda create --name nerfstudio -y python=3.8 -conda activate nerfstudio +conda create --name neurad -y python=3.8 +conda activate neurad pip install --upgrade pip ``` @@ -131,51 +70,47 @@ conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch ``` -See [Dependencies](https://github.com/nerfstudio-project/nerfstudio/blob/main/docs/quickstart/installation.md#dependencies) -in the Installation documentation for more. - -### Installing nerfstudio - -Easy option: - -```bash -pip install nerfstudio -``` - -**OR** if you want the latest and greatest: +We refer to [Nerfstudio](https://github.com/nerfstudio-project/nerfstudio/blob/v1.0.3/docs/quickstart/installation.md) for more installation support. +### Installing NeuRAD ```bash -git clone https://github.com/nerfstudio-project/nerfstudio.git -cd nerfstudio +git clone https://github.com/georghess/neurad.git +cd neurad pip install --upgrade pip setuptools pip install -e . ``` -**OR** if you want to skip all installation steps and directly start using nerfstudio, use the docker image: +**OR** if you want to skip all installation steps and directly start using NeuRAD, use the provided docker image or apptainer recipe: -See [Installation](https://github.com/nerfstudio-project/nerfstudio/blob/main/docs/quickstart/installation.md) - **Use docker image**. +[Dockerfile](Dockerfile) or [Apptainer recipe](apptainer_recipe). ## 2. Training your first model! -The following will train a _nerfacto_ model, our recommended model for real world scenes. +The following will train a _NeuRAD_ model, our recommended model for real world AD scenes. + +### Data preparation + +Begin by downloading [PandaSet](https://www.kaggle.com/datasets/usharengaraju/pandaset-dataset/data) and place it under ```data/pandaset```. + +### Training + +Training models is done the same way as in nerfstudio, i.e., ```bash -# Download some test data: -ns-download-data nerfstudio --capture-name=poster # Train model -ns-train nerfacto --data data/nerfstudio/poster +python nerfstudio/scripts/train.py neurad pandaset-data ``` If everything works, you should see training progress like the following:

- image + image

Navigating to the link at the end of the terminal will load the webviewer. If you are running on a remote machine, you will need to port forward the websocket port (defaults to 7007).

- image + image

### Resume from checkpoint / visualize existing run @@ -183,7 +118,7 @@ Navigating to the link at the end of the terminal will load the webviewer. If yo It is possible to load a pretrained model by running ```bash -ns-train nerfacto --data data/nerfstudio/poster --load-dir {outputs/.../nerfstudio_models} +pyhton nerfstudio/scripts/train.py neurad pandaset-data --load-dir {outputs/.../nerfstudio_models} ``` ## Visualize existing run @@ -191,171 +126,120 @@ ns-train nerfacto --data data/nerfstudio/poster --load-dir {outputs/.../nerfstud Given a pretrained model checkpoint, you can start the viewer by running ```bash -ns-viewer --load-config {outputs/.../config.yml} +python nerfstudio/scripts/viewer/run_viewer.py --load-config {outputs/.../config.yml} ``` ## 3. Exporting Results -Once you have a NeRF model you can either render out a video or export a point cloud. +Once you have a NeRF model you can render its output. There are multiple different renders, more info available using + +```bash +python nerfstudio/scripts/render.py --help +``` -### Render Video -First we must create a path for the camera to follow. This can be done in the viewer under the "RENDER" tab. Orient your 3D view to the location where you wish the video to start, then press "ADD CAMERA". This will set the first camera key frame. Continue to new viewpoints adding additional cameras to create the camera path. We provide other parameters to further refine your camera path. Once satisfied, press "RENDER" which will display a modal that contains the command needed to render the video. Kill the training job (or create a new terminal if you have lots of compute) and run the command to generate the video. +## 4. Advanced Options -Other video export options are available, learn more by running +### Training models other than NeuRAD + +Besides NeuRAD, we will provide a reimplementation of [UniSim](https://arxiv.org/abs/2308.01898) as well. Once this is released it can be trained using ```bash -ns-render --help +# Train model +python nerfstudio/scripts/train.py unisim pandaset-data ``` -### Generate Point Cloud +Further, as we build on top of nerfstudio, models such as _nerfacto_ or _splatfacto_ are available as well, see nerfstudio for details. However, note that these are made for static scenes. + +For a full list of included models run `python nerfstudio/scripts/train.py --help`. -While NeRF models are not designed to generate point clouds, it is still possible. Navigate to the "EXPORT" tab in the 3D viewer and select "POINT CLOUD". If the crop option is selected, everything in the yellow square will be exported into a point cloud. Modify the settings as desired then run the command at the bottom of the panel in your command line. +### Modify Configuration -Alternatively you can use the CLI without the viewer. Learn about the export options by running +Each model contains many parameters that can be changed, too many to list here. Use the `--help` command to see the full list of configuration options. ```bash -ns-export pointcloud --help +python nerfstudio/scripts/train.py neurad --help ``` -## 4. Using Custom Data +### Tensorboard / WandB / Comet / Viewer -Using an existing dataset is great, but likely you want to use your own data! We support various methods for using your own data. Before it can be used in nerfstudio, the camera location and orientations must be determined and then converted into our format using `ns-process-data`. We rely on external tools for this, instructions and information can be found in the documentation. +There are four different methods to track training progress, using the viewer, [tensorboard](https://www.tensorflow.org/tensorboard), [Weights and Biases](https://wandb.ai/site), and [Comet](https://comet.com/?utm_source=nerf&utm_medium=referral&utm_content=github). You can specify which visualizer to use by appending `--vis {viewer, tensorboard, wandb, comet viewer+wandb, viewer+tensorboard, viewer+comet}` to the training command. Simultaneously utilizing the viewer alongside wandb or tensorboard may cause stuttering issues during evaluation steps. -| Data | Capture Device | Requirements | `ns-process-data` Speed | -| --------------------------------------------------------------------------------------------- | -------------- | ----------------------------------------------------------------- | ----------------------- | -| πŸ“· [Images](https://docs.nerf.studio/quickstart/custom_dataset.html#images-or-video) | Any | [COLMAP](https://colmap.github.io/install.html) | 🐒 | -| πŸ“Ή [Video](https://docs.nerf.studio/quickstart/custom_dataset.html#images-or-video) | Any | [COLMAP](https://colmap.github.io/install.html) | 🐒 | -| 🌎 [360 Data](https://docs.nerf.studio/quickstart/custom_dataset.html#data-equirectangular) | Any | [COLMAP](https://colmap.github.io/install.html) | 🐒 | -| πŸ“± [Polycam](https://docs.nerf.studio/quickstart/custom_dataset.html#polycam-capture) | IOS with LiDAR | [Polycam App](https://poly.cam/) | πŸ‡ | -| πŸ“± [KIRI Engine](https://docs.nerf.studio/quickstart/custom_dataset.html#kiri-engine-capture) | IOS or Android | [KIRI Engine App](https://www.kiriengine.com/) | πŸ‡ | -| πŸ“± [Record3D](https://docs.nerf.studio/quickstart/custom_dataset.html#record3d-capture) | IOS with LiDAR | [Record3D app](https://record3d.app/) | πŸ‡ | -| πŸ“± [Spectacular AI](https://docs.nerf.studio/quickstart/custom_dataset.html#spectacularai) | IOS, OAK, [others](https://www.spectacularai.com/mapping#supported-devices) | [App](https://apps.apple.com/us/app/spectacular-rec/id6473188128) / [`sai-cli`](https://www.spectacularai.com/mapping) | πŸ‡ | -| πŸ–₯ [Metashape](https://docs.nerf.studio/quickstart/custom_dataset.html#metashape) | Any | [Metashape](https://www.agisoft.com/) | πŸ‡ | -| πŸ–₯ [RealityCapture](https://docs.nerf.studio/quickstart/custom_dataset.html#realitycapture) | Any | [RealityCapture](https://www.capturingreality.com/realitycapture) | πŸ‡ | -| πŸ–₯ [ODM](https://docs.nerf.studio/quickstart/custom_dataset.html#odm) | Any | [ODM](https://github.com/OpenDroneMap/ODM) | πŸ‡ | -| πŸ‘“ [Aria](https://docs.nerf.studio/quickstart/custom_dataset.html#aria) | Aria glasses | [Project Aria](https://projectaria.com/) | πŸ‡ | -| πŸ›  [Custom](https://docs.nerf.studio/quickstart/data_conventions.html) | Any | Camera Poses | πŸ‡ | +# Learn More +And that's it for getting started with the basics of NeuRAD. If you are missing some features, have a look at [Planned Features](#planned-featurestodos) to see if we have plans on implementing this. Otherwise, feel free to open an issue, or even better implement it yourself and open a PR! -## 5. Advanced Options +If you want to add a dataset, look [here](#adding-datasets). If you want to add a method, have a look [here](#adding-methods). -### Training models other than nerfacto +## Adding Datasets -We provide other models than nerfacto, for example if you want to train the original nerf model, use the following command +We have provided dataparsers for multiple autonomous driving dataset, see below for a complete list. However, your favorite AD dataset might still be missing. -```bash -ns-train vanilla-nerf --data DATA_PATH -``` - -For a full list of included models run `ns-train --help`. +To add a dataset, create `nerfstudio/data/dataparsers/mydataset.py` containing one dataparsers config class `MyADDataParserConfig` and one dataparser class `MyADData`. Preferrably, these inherit from `ADDataParserConfig` and `ADDataParser`, as these provide common functionality and streamline the expected format of AD data. For most datasets, it should then be sufficient to overwrite `_get_cameras`, `_get_lidars`, `_read_lidars`, `_get_actor_trajectories`, and `_generate_dataparser_outputs`. -### Modify Configuration +| Data | Cameras | Lidars | +| --------------------------------------------------------------------------------------------- | -------------- | ----------------------------------------------------------------- | +| πŸš— [nuScenes](https://www.nuscenes.org/) | 6 cameras | 32-beam lidar | +| πŸš— [ZOD](https://zod.zenseact.com/) | 1 camera | 128-beam + 2 x 16-beam lidars | +| πŸš— [Argoverse 2](https://www.argoverse.org/av2.html) | 7 ring cameras + 2 stereo cameras | 2 x 32-beam lidars | +| πŸš— [PandaSet](https://pandaset.org/) | 6 cameras | 64-beam lidar | +| πŸš— [KITTIMOT](https://www.cvlibs.net/datasets/kitti/eval_tracking.php) | 2 stereo cameras | 64-beam lidar -Each model contains many parameters that can be changed, too many to list here. Use the `--help` command to see the full list of configuration options. -```bash -ns-train nerfacto --help -``` -### Tensorboard / WandB / Viewer +## Adding Methods -We support four different methods to track training progress, using the viewer[tensorboard](https://www.tensorflow.org/tensorboard), [Weights and Biases](https://wandb.ai/site), and ,[Comet](https://comet.com/?utm_source=nerf&utm_medium=referral&utm_content=github). You can specify which visualizer to use by appending `--vis {viewer, tensorboard, wandb, comet viewer+wandb, viewer+tensorboard, viewer+comet}` to the training command. Simultaneously utilizing the viewer alongside wandb or tensorboard may cause stuttering issues during evaluation steps. The viewer only works for methods that are fast (ie. nerfacto, instant-ngp), for slower methods like NeRF, use the other loggers. +Nerfstudio has made it easy to add new methods, see [here](https://docs.nerf.studio/developer_guides/new_methods.html) for details. We plan to examplify this using our UniSim reimplementation, to be released soon. -# Learn More +# Key features +- Dataparser for multiple autonomous driving datasets including + - Dataparsing of lidar data (3D+intensity+time) + - Dataparsing of annotations +- Datamanager for lidar+image data +- Rolling shutter handling for ray generation +- Viewer improvements + - Lidar rendering + - Dynamic actor modifications +- NeuRAD - SOTA neural rendering method for dynamic AD scenes -And that's it for getting started with the basics of nerfstudio. - -If you're interested in learning more on how to create your own pipelines, develop with the viewer, run benchmarks, and more, please check out some of the quicklinks below or visit our [documentation](https://docs.nerf.studio/) directly. - -| Section | Description | -| ---------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | -| [Documentation](https://docs.nerf.studio/) | Full API documentation and tutorials | -| [Viewer](https://viewer.nerf.studio/) | Home page for our web viewer | -| πŸŽ’ **Educational** | -| [Model Descriptions](https://docs.nerf.studio/nerfology/methods/index.html) | Description of all the models supported by nerfstudio and explanations of component parts. | -| [Component Descriptions](https://docs.nerf.studio/nerfology/model_components/index.html) | Interactive notebooks that explain notable/commonly used modules in various models. | -| πŸƒ **Tutorials** | -| [Getting Started](https://docs.nerf.studio/quickstart/installation.html) | A more in-depth guide on how to get started with nerfstudio from installation to contributing. | -| [Using the Viewer](https://docs.nerf.studio/quickstart/viewer_quickstart.html) | A quick demo video on how to navigate the viewer. | -| [Using Record3D](https://www.youtube.com/watch?v=XwKq7qDQCQk) | Demo video on how to run nerfstudio without using COLMAP. | -| πŸ’» **For Developers** | -| [Creating pipelines](https://docs.nerf.studio/developer_guides/pipelines/index.html) | Learn how to easily build new neural rendering pipelines by using and/or implementing new modules. | -| [Creating datasets](https://docs.nerf.studio/quickstart/custom_dataset.html) | Have a new dataset? Learn how to run it with nerfstudio. | -| [Contributing](https://docs.nerf.studio/reference/contributing.html) | Walk-through for how you can start contributing now. | -| πŸ’– **Community** | -| [Discord](https://discord.gg/uMbNqcraFc) | Join our community to discuss more. We would love to hear from you! | -| [Twitter](https://twitter.com/nerfstudioteam) | Follow us on Twitter @nerfstudioteam to see cool updates and announcements | -| [Feedback Form](TODO) | We welcome any feedback! This is our chance to learn what you all are using Nerfstudio for. | - -# Supported Features - -We provide the following support structures to make life easier for getting started with NeRFs. - -**If you are looking for a feature that is not currently supported, please do not hesitate to contact the Nerfstudio Team on [Discord](https://discord.gg/uMbNqcraFc)!** - -- :mag_right: Web-based visualizer that allows you to: - - Visualize training in real-time + interact with the scene - - Create and render out scenes with custom camera trajectories - - View different output types - - And more! -- :pencil2: Support for multiple logging interfaces (Tensorboard, Wandb), code profiling, and other built-in debugging tools -- :chart_with_upwards_trend: Easy-to-use benchmarking scripts on the Blender dataset -- :iphone: Full pipeline support (w/ Colmap, Polycam, or Record3D) for going from a video on your phone to a full 3D render. -# Built On +# Planned Features/TODOs - - - - - - tyro logo - - - - +- [ ] UniSim plug-in +- [x] Release code -- Easy-to-use config system -- Developed by [Brent Yi](https://brentyi.com/) +# Built On - + - + - tyro logo + tyro logo -- Library for accelerating NeRF renders -- Developed by [Ruilong Li](https://www.liruilong.cn/) +- Collaboration friendly studio for NeRFs # Citation -You can find a paper writeup of the framework on [arXiv](https://arxiv.org/abs/2302.04264). +You can find our paper on [arXiv](https://arxiv.org/abs/2311.15260). -If you use this library or find the documentation useful for your research, please consider citing: +If you use this code or find our paper useful, please consider citing: -``` -@inproceedings{nerfstudio, - title = {Nerfstudio: A Modular Framework for Neural Radiance Field Development}, - author = { - Tancik, Matthew and Weber, Ethan and Ng, Evonne and Li, Ruilong and Yi, Brent - and Kerr, Justin and Wang, Terrance and Kristoffersen, Alexander and Austin, - Jake and Salahi, Kamyar and Ahuja, Abhik and McAllister, David and Kanazawa, - Angjoo - }, - year = 2023, - booktitle = {ACM SIGGRAPH 2023 Conference Proceedings}, - series = {SIGGRAPH '23} +```bibtex +@article{neurad, + title={NeuRAD: Neural Rendering for Autonomous Driving}, + author={Tonderski, Adam and Lindstr{\"o}m, Carl and Hess, Georg and Ljungbergh, William and Svensson, Lennart and Petersson, Christoffer}, + journal={arXiv preprint arXiv:2311.15260}, + year={2023} } ``` # Contributors - - + + diff --git a/apptainer_recipe b/apptainer_recipe new file mode 100644 index 00000000..0b75583d --- /dev/null +++ b/apptainer_recipe @@ -0,0 +1,78 @@ +Bootstrap: docker +From: nvidia/cuda:11.8.0-devel-ubuntu22.04 +Stage: spython-base + +%files +. /nerfstudio +%labels +org.opencontainers.image.version = "0.1.18" +org.opencontainers.image.source = "https://github.com/nerfstudio-project/nerfstudio" +org.opencontainers.image.licenses = "Apache License 2.0" +org.opencontainers.image.base.name = "docker.io/library/nvidia/cuda:11.8.0-devel-ubuntu22.04" +%post + +# Install required apt packages and clear cache afterwards. +DEBIAN_FRONTEND=noninteractive TZ=Europe/Berlin apt-get update && \ +apt-get install -y --no-install-recommends \ +build-essential \ +cmake \ +curl \ +ffmpeg \ +git \ +libatlas-base-dev \ +libboost-filesystem-dev \ +libboost-graph-dev \ +libboost-program-options-dev \ +libboost-system-dev \ +libboost-test-dev \ +libhdf5-dev \ +libcgal-dev \ +libeigen3-dev \ +libflann-dev \ +libfreeimage-dev \ +libgflags-dev \ +libglew-dev \ +libgoogle-glog-dev \ +libmetis-dev \ +libprotobuf-dev \ +libqt5opengl5-dev \ +libsqlite3-dev \ +libsuitesparse-dev \ +protobuf-compiler \ +python-is-python3 \ +python3.10-dev \ +python3-pip \ +qtbase5-dev \ +wget \ +&& \ +rm -rf /var/lib/apt/lists/* + +# Upgrade pip and install packages. +python3.10 -m pip install --no-cache-dir --upgrade pip setuptools pathtools promise pybind11 +# Install pytorch and submodules +python3.10 -m pip install --no-cache-dir torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 +# Install tinyCUDNN (we need to set the target architectures as environment variable first). +TCNN_CUDA_ARCHITECTURES="90;89;86;80;75" python3.10 -m pip install --no-cache-dir git+https://github.com/NVlabs/tiny-cuda-nn.git@v1.6#subdirectory=bindings/torch + +# Install nerfstudio dependencies. +cd /nerfstudio && python3.10 -m pip install --no-cache-dir -e . + +# Change working directory +mkdir -p /workspace +cd /workspace + +# Install nerfstudio cli auto completion +ns-install-cli --mode install + +# Bash as default entrypoint. +%environment +export DEBIAN_FRONTEND=noninteractive +export TZ=Europe/Berlin +export CUDA_HOME="/usr/local/cuda" +export TCNN_CUDA_ARCHITECTURES="90;89;86;80;75" +%runscript +cd /workspace +exec /bin/bash /bin/bash -l "$@" +%startscript +cd /workspace +exec /bin/bash /bin/bash -l "$@" diff --git a/colab/demo.ipynb b/colab/demo.ipynb deleted file mode 100644 index 96eaa25f..00000000 --- a/colab/demo.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SiiXJ7K_fePG" - }, - "source": [ - "

\n", - " \n", - " \n", - " \n", - " \"nerfstudio\"\n", - " \n", - "

\n", - "\n", - "\n", - "# Nerfstudio: A collaboration friendly studio for NeRFs\n", - "\n", - "\n", - "![GitHub stars](https://img.shields.io/github/stars/nerfstudio-project/nerfstudio?color=gold&style=social)\n", - "\n", - "This colab shows how to train and view NeRFs from Nerfstudio both on pre-made datasets or from your own videos/images.\n", - "\n", - "\\\\\n", - "\n", - "Credit to [NeX](https://nex-mpi.github.io/) for Google Colab format." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Yyx5h6kz5ga7" - }, - "source": [ - "## Frequently Asked Questions\n", - "\n", - "* **Downloading custom data is stalling (no output):**\n", - " * This is a bug in Colab. The data is processing, but may take a while to complete. You will know processing completed if `data/nerfstudio/custom_data/transforms.json` exists. Terminating the cell early will result in not being able to train.\n", - "* **Processing custom data is taking a long time:**\n", - " * The time it takes to process data depends on the number of images and its resolution. If processing is taking too long, try lowering the resolution of your custom data.\n", - "* **Error: Data processing did not complete:**\n", - " * This means that the data processing script did not fully complete. This could be because there were not enough images, or that the images were of low quality. We recommend images with little to no motion blur and lots of visual overlap of the scene to increase the chances of successful processing.\n", - "* **Training is not showing progress**:\n", - " * The lack of output is a bug in Colab. You can see the training progress from the viewer.\n", - "* **Viewer Quality is bad / Low resolution**:\n", - " * This may be because more GPU is being used on training that rendering the viewer. Try pausing training or decreasing training utilization.\n", - "* **WARNING: Running pip as the 'root' user...:**:\n", - " * This and other pip warnings or errors can be safely ignored.\n", - "* **Other problems?**\n", - " * Feel free to create an issue on our [GitHub repo](https://github.com/nerfstudio-project/nerfstudio).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "9oyLHl8QfYwP" - }, - "outputs": [], - "source": [ - "#@markdown

Install Nerfstudio and Dependencies (~8 min)

\n", - "\n", - "%cd /content/\n", - "!pip install --upgrade pip\n", - "!pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118\n", - "\n", - "# Installing TinyCuda\n", - "%cd /content/\n", - "!gdown \"https://drive.google.com/u/1/uc?id=1-7x7qQfB7bIw2zV4Lr6-yhvMpjXC84Q5&confirm=t\"\n", - "!pip install tinycudann-1.7-cp310-cp310-linux_x86_64.whl\n", - "\n", - "# Installing COLMAP\n", - "%cd /content/\n", - "!apt-get install colmap\n", - "\n", - "# Install nerfstudio\n", - "%cd /content/\n", - "!pip install git+https://github.com/nerfstudio-project/nerfstudio.git" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "msVLprI4gRA4" - }, - "outputs": [], - "source": [ - "#@markdown

Downloading and Processing Data

\n", - "#@markdown

Pick the preset scene or upload your own images/video

\n", - "import os\n", - "import glob\n", - "from google.colab import files\n", - "from IPython.core.display import display, HTML\n", - "\n", - "scene = '\\uD83D\\uDDBC poster' #@param ['πŸ–Ό poster', '🚜 dozer', 'πŸŒ„ desolation', 'πŸ“€ upload your images' , 'πŸŽ₯ upload your own video', 'πŸ”Ί upload Polycam data', 'πŸ’½ upload your own Record3D data']\n", - "scene = ' '.join(scene.split(' ')[1:])\n", - "\n", - "if scene == \"upload Polycam data\":\n", - " %cd /content/\n", - " !mkdir -p /content/data/nerfstudio/custom_data\n", - " %cd /content/data/nerfstudio/custom_data/\n", - " uploaded = files.upload()\n", - " dir = os.getcwd()\n", - " if len(uploaded.keys()) > 1:\n", - " print(\"ERROR, upload a single .zip file when processing Polycam data\")\n", - " dataset_dir = [os.path.join(dir, f) for f in uploaded.keys()][0]\n", - " !ns-process-data polycam --data $dataset_dir --output-dir /content/data/nerfstudio/custom_data/\n", - " scene = \"custom_data\"\n", - "elif scene == 'upload your own Record3D data':\n", - " display(HTML('

Zip your Record3D folder, and upload.

'))\n", - " display(HTML('

More information on Record3D can be found here.

'))\n", - " %cd /content/\n", - " !mkdir -p /content/data/nerfstudio/custom_data\n", - " %cd /content/data/nerfstudio/custom_data/\n", - " uploaded = files.upload()\n", - " dir = os.getcwd()\n", - " preupload_datasets = [os.path.join(dir, f) for f in uploaded.keys()]\n", - " record_3d_zipfile = preupload_datasets[0]\n", - " !unzip $record_3d_zipfile -d /content/data/nerfstudio/custom_data\n", - " custom_data_directory = glob.glob('/content/data/nerfstudio/custom_data/*')[0]\n", - " !ns-process-data record3d --data $custom_data_directory --output-dir /content/data/nerfstudio/custom_data/\n", - " scene = \"custom_data\"\n", - "elif scene in ['upload your images', 'upload your own video']:\n", - " display(HTML('

Select your custom data

'))\n", - " display(HTML('

You can select multiple images by pressing ctrl, cmd or shift and click.

'))\n", - " display(HTML('

Note: This may take time, especially on higher resolution inputs, so we recommend to download dataset after creation.

'))\n", - " !mkdir -p /content/data/nerfstudio/custom_data\n", - " if scene == 'upload your images':\n", - " !mkdir -p /content/data/nerfstudio/custom_data/raw_images\n", - " %cd /content/data/nerfstudio/custom_data/raw_images\n", - " uploaded = files.upload()\n", - " dir = os.getcwd()\n", - " else:\n", - " %cd /content/data/nerfstudio/custom_data/\n", - " uploaded = files.upload()\n", - " dir = os.getcwd()\n", - " preupload_datasets = [os.path.join(dir, f) for f in uploaded.keys()]\n", - " del uploaded\n", - " %cd /content/\n", - "\n", - " if scene == 'upload your images':\n", - " !ns-process-data images --data /content/data/nerfstudio/custom_data/raw_images --output-dir /content/data/nerfstudio/custom_data/\n", - " else:\n", - " video_path = preupload_datasets[0]\n", - " !ns-process-data video --data $video_path --output-dir /content/data/nerfstudio/custom_data/\n", - "\n", - " scene = \"custom_data\"\n", - "else:\n", - " %cd /content/\n", - " !ns-download-data nerfstudio --capture-name=$scene\n", - "\n", - "print(\"Data Processing Succeeded!\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "m_N8_cLfjoXD" - }, - "outputs": [], - "source": [ - "#@markdown

Start Training

\n", - "\n", - "%cd /content\n", - "!pip install colab-xterm\n", - "%load_ext colabxterm\n", - "%env TERM=xterm\n", - "from IPython.display import clear_output\n", - "clear_output(wait=True)\n", - "if os.path.exists(f\"data/nerfstudio/{scene}/transforms.json\"):\n", - " print(\"\\033[1m\" + \"Copy and paste the following command into the terminal window that pops up under this cell.\" + \"\\033[0m\")\n", - " print(f\"ns-train nerfacto --viewer.websocket-port 7007 --viewer.make-share-url True nerfstudio-data --data data/nerfstudio/{scene} --downscale-factor 4\")\n", - " print()\n", - " %xterm\n", - "else:\n", - " from IPython.core.display import display, HTML\n", - " display(HTML('

Error: Data processing did not complete

'))\n", - " display(HTML('

Please re-run `Downloading and Processing Data`, or view the FAQ for more info.

'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WGt8ukG6Htg3", - "outputId": "fa946890-c7d8-4e46-a54e-7231bc5a2059" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2;36m[19:48:48]\u001b[0m\u001b[2;36m \u001b[0mSkipping \u001b[1;36m0\u001b[0m files in dataset split train. \u001b]8;id=527413;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py\u001b\\\u001b[2mnerfstudio_dataparser.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=243595;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py#91\u001b\\\u001b[2m91\u001b[0m\u001b]8;;\u001b\\\n", - "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mSkipping \u001b[1;36m0\u001b[0m files in dataset split test. \u001b]8;id=109270;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py\u001b\\\u001b[2mnerfstudio_dataparser.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=464675;file:///content/nerfstudio/nerfstudio/data/dataparsers/nerfstudio_dataparser.py#91\u001b\\\u001b[2m91\u001b[0m\u001b]8;;\u001b\\\n", - "\u001b[2KLoading data batch \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h/usr/local/lib/python3.7/site-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", - " cpuset_checked))\n", - "\u001b[2KLoading data batch \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h/usr/local/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n", - " f\"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, \"\n", - "/usr/local/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "Loading latest checkpoint from load_dir\n", - "βœ… Done loading checkpoint from \n", - "outputs/data-nerfstudio-poster/nerfacto/\u001b[1;36m2022\u001b[0m-\u001b[1;36m10\u001b[0m-29_192844/nerfstudio_models/step-\u001b[1;36m000014000.\u001b[0mckpt\n", - "\u001b[1;32mCreating trajectory video\u001b[0m\n", - "\u001b[2KπŸŽ₯ Rendering πŸŽ₯ \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[31m0.14 fps\u001b[0m \u001b[33m11:47\u001b[0m\n", - "\u001b[2K\u001b[32m( ● )\u001b[0m \u001b[33mSaving video\u001b[0m\n", - "\u001b[1A\u001b[2K\u001b[92m────────────────────────────────────────────── \u001b[0m\u001b[32m πŸŽ‰ πŸŽ‰ πŸŽ‰ Success πŸŽ‰ πŸŽ‰ πŸŽ‰\u001b[0m\u001b[92m ──────────────────────────────────────────────\u001b[0m\n", - " \u001b[32mSaved video to renders/output.mp4\u001b[0m \n", - "\u001b[0m" - ] - } - ], - "source": [ - "#@title # Render Video { vertical-output: true }\n", - "#@markdown

Export the camera path from within the viewer, then run this cell.

\n", - "#@markdown
The rendered video should be at renders/output.mp4!
\n", - "\n", - "\n", - "base_dir = \"/content/outputs/unnamed/nerfacto/\"\n", - "training_run_dir = base_dir + os.listdir(base_dir)[0]\n", - "\n", - "from IPython.core.display import display, HTML\n", - "display(HTML('

Upload the camera path JSON.

'))\n", - "%cd $training_run_dir\n", - "uploaded = files.upload()\n", - "uploaded_camera_path_filename = list(uploaded.keys())[0]\n", - "\n", - "config_filename = training_run_dir + \"/config.yml\"\n", - "camera_path_filename = training_run_dir + \"/\" + uploaded_camera_path_filename\n", - "camera_path_filename = camera_path_filename.replace(\" \", \"\\\\ \").replace(\"(\", \"\\\\(\").replace(\")\", \"\\\\)\")\n", - "\n", - "%cd /content/\n", - "!ns-render camera-path --load-config $config_filename --camera-path-filename $camera_path_filename --output-path renders/output.mp4" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "include_colab_link": true, - "provenance": [] - }, - "gpuClass": "standard", - "kernelspec": { - "display_name": "Python 3.8.13 ('nerfstudio')", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.13" - }, - "vscode": { - "interpreter": { - "hash": "c59f626636933ef1dc834fb3684b382f705301c5306cf8436d2da634c2289783" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 5f832266..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -W --keep-going # build fail on warning -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_pygments/style.py b/docs/_pygments/style.py deleted file mode 100644 index 6231c736..00000000 --- a/docs/_pygments/style.py +++ /dev/null @@ -1,101 +0,0 @@ -"""Custom Pygments styles for the Sphinx documentation.""" - -from pygments.style import Style -from pygments.token import ( - Comment, - Error, - Generic, - Keyword, - Name, - Number, - Operator, - Punctuation, - String, - Token, - Whitespace, -) - - -class NerfstudioStyleLight(Style): - """ - A style based on the manni pygments style. - """ - - background_color = "#f8f9fb" - - styles = { - Whitespace: "#bbbbbb", - Comment: "italic #d34600", - Comment.Preproc: "noitalic #009999", - Comment.Special: "bold", - Keyword: "bold #006699", - Keyword.Pseudo: "nobold", - Keyword.Type: "#007788", - Operator: "#555555", - Operator.Word: "bold #000000", - Name.Builtin: "#336666", - Name.Function: "#CC00FF", - Name.Class: "bold #00AA88", - Name.Namespace: "bold #00CCFF", - Name.Exception: "bold #CC0000", - Name.Variable: "#003333", - Name.Constant: "#336600", - Name.Label: "#9999FF", - Name.Entity: "bold #999999", - Name.Attribute: "#330099", - Name.Tag: "bold #330099", - Name.Decorator: "#9999FF", - String: "#CC3300", - String.Doc: "italic", - String.Interpol: "#AA0000", - String.Escape: "bold #CC3300", - String.Regex: "#33AAAA", - String.Symbol: "#FFCC33", - String.Other: "#CC3300", - Number: "#FF6600", - Generic.Heading: "bold #003300", - Generic.Subheading: "bold #003300", - Generic.Deleted: "border:#CC0000 bg:#FFCCCC", - Generic.Inserted: "border:#00CC00 bg:#CCFFCC", - Generic.Error: "#FF0000", - Generic.Emph: "italic", - Generic.Strong: "bold", - Generic.Prompt: "bold #000099", - Generic.Output: "#AAAAAA", - Generic.Traceback: "#99CC66", - Error: "bg:#FFAAAA #AA0000", - } - - -class NerfstudioStyleDark(Style): - """ - A style based on the one-dark style. - """ - - background_color = "#282C34" - - styles = { - Token: "#ABB2BF", - Punctuation: "#ABB2BF", - Punctuation.Marker: "#ABB2BF", - Keyword: "#C678DD", - Keyword.Constant: "#fdd06c", - Keyword.Declaration: "#C678DD", - Keyword.Namespace: "#C678DD", - Keyword.Reserved: "#C678DD", - Keyword.Type: "#fdd06c", - Name: "#ff8c58", - Name.Attribute: "#ff8c58", - Name.Builtin: "#fdd06c", - Name.Class: "#fdd06c", - Name.Function: "bold #61AFEF", - Name.Function.Magic: "bold #56B6C2", - Name.Other: "#ff8c58", - Name.Tag: "#ff8c58", - Name.Decorator: "#61AFEF", - Name.Variable.Class: "", - String: "#bde3a1", - Number: "#D19A66", - Operator: "#56B6C2", - Comment: "#7F848E", - } diff --git a/docs/_static/custom.js b/docs/_static/custom.js deleted file mode 100644 index 7fa8c944..00000000 --- a/docs/_static/custom.js +++ /dev/null @@ -1,6 +0,0 @@ -requirejs.config({ - paths: { - base: '/static/base', - plotly: 'https://cdn.plot.ly/plotly-2.12.1.min.js?noext', - }, -}); \ No newline at end of file diff --git a/docs/_static/imgs/bair_dark.png b/docs/_static/imgs/bair_dark.png deleted file mode 100644 index 23407523..00000000 Binary files a/docs/_static/imgs/bair_dark.png and /dev/null differ diff --git a/docs/_static/imgs/bair_light.png b/docs/_static/imgs/bair_light.png deleted file mode 100644 index a09794a3..00000000 Binary files a/docs/_static/imgs/bair_light.png and /dev/null differ diff --git a/docs/_static/imgs/luma_dark.png b/docs/_static/imgs/luma_dark.png deleted file mode 100644 index a7833328..00000000 Binary files a/docs/_static/imgs/luma_dark.png and /dev/null differ diff --git a/docs/_static/imgs/luma_light.png b/docs/_static/imgs/luma_light.png deleted file mode 100644 index a4bdc7cc..00000000 Binary files a/docs/_static/imgs/luma_light.png and /dev/null differ diff --git a/docs/_static/imgs/neurad_logo_with_text.png b/docs/_static/imgs/neurad_logo_with_text.png new file mode 100644 index 00000000..62b5dbfb Binary files /dev/null and b/docs/_static/imgs/neurad_logo_with_text.png differ diff --git a/docs/_static/imgs/neurad_logo_with_text_dark.png b/docs/_static/imgs/neurad_logo_with_text_dark.png new file mode 100644 index 00000000..f84fa45e Binary files /dev/null and b/docs/_static/imgs/neurad_logo_with_text_dark.png differ diff --git a/docs/_static/imgs/readme_colab.png b/docs/_static/imgs/readme_colab.png deleted file mode 100644 index 2ba11d76..00000000 Binary files a/docs/_static/imgs/readme_colab.png and /dev/null differ diff --git a/docs/_static/imgs/readme_documentation.png b/docs/_static/imgs/readme_documentation.png deleted file mode 100644 index a6be9c6e..00000000 Binary files a/docs/_static/imgs/readme_documentation.png and /dev/null differ diff --git a/docs/_static/imgs/readme_training_progress_example.png b/docs/_static/imgs/readme_training_progress_example.png new file mode 100644 index 00000000..ab3af98e Binary files /dev/null and b/docs/_static/imgs/readme_training_progress_example.png differ diff --git a/docs/_static/imgs/readme_viewer.png b/docs/_static/imgs/readme_viewer.png deleted file mode 100644 index 033958a2..00000000 Binary files a/docs/_static/imgs/readme_viewer.png and /dev/null differ diff --git a/docs/_static/imgs/readme_viewer_neurad.png b/docs/_static/imgs/readme_viewer_neurad.png new file mode 100644 index 00000000..242a24ec Binary files /dev/null and b/docs/_static/imgs/readme_viewer_neurad.png differ diff --git a/docs/_static/require.min.js b/docs/_static/require.min.js deleted file mode 100644 index 75d9b0eb..00000000 --- a/docs/_static/require.min.js +++ /dev/null @@ -1 +0,0 @@ -var requirejs,require,define;!function(global,setTimeout){var req,s,head,baseElement,dataMain,src,interactiveScript,currentlyAddingScript,mainScript,subPath,version="2.3.6",commentRegExp=/\/\*[\s\S]*?\*\/|([^:"'=]|^)\/\/.*$/gm,cjsRequireRegExp=/[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,jsSuffixRegExp=/\.js$/,currDirRegExp=/^\.\//,op=Object.prototype,ostring=op.toString,hasOwn=op.hasOwnProperty,isBrowser=!("undefined"==typeof window||"undefined"==typeof navigator||!window.document),isWebWorker=!isBrowser&&"undefined"!=typeof importScripts,readyRegExp=isBrowser&&"PLAYSTATION 3"===navigator.platform?/^complete$/:/^(complete|loaded)$/,defContextName="_",isOpera="undefined"!=typeof opera&&"[object Opera]"===opera.toString(),contexts={},cfg={},globalDefQueue=[],useInteractive=!1;function commentReplace(e,t){return t||""}function isFunction(e){return"[object Function]"===ostring.call(e)}function isArray(e){return"[object Array]"===ostring.call(e)}function each(e,t){var i;if(e)for(i=0;i - - - - {%- block site_meta -%} - - - - - {%- if metatags %}{{ metatags }}{% endif -%} - - {%- block linktags %} - {%- if hasdoc('about') -%} - - {%- endif -%} - {%- if hasdoc('genindex') -%} - - {%- endif -%} - {%- if hasdoc('search') -%} - - {%- endif -%} - {%- if hasdoc('copyright') -%} - - {%- endif -%} - {%- if next -%} - - {%- endif -%} - {%- if prev -%} - - {%- endif -%} - {#- rel="canonical" (set by html_baseurl) -#} - {%- if pageurl %} - - {%- endif %} - {%- endblock linktags %} - - {# Favicon #} - {%- if favicon_url -%} - - {%- endif -%} - - {#- Generator banner -#} - - - {%- endblock site_meta -%} - - {#- Site title -#} - {%- block htmltitle -%} - {% if not docstitle %} - {{ title|striptags|e }} - {% elif pagename == master_doc %} - {{ docstitle|striptags|e }} - {% else %} - {{ title|striptags|e }} - {{ docstitle|striptags|e }} - {% endif %} - {%- endblock -%} - - {%- block styles -%} - - {# Custom stylesheets #} - {%- block regular_styles -%} - {%- for css in css_files -%} - {% if css|attr("filename") -%} - {{ css_tag(css) }} - {%- else -%} - - {%- endif %} - {% endfor -%} - {%- endblock regular_styles -%} - - {#- Theme-related stylesheets -#} - {%- block theme_styles %} - {% include "partials/_head_css_variables.html" with context %} - {%- endblock -%} - - {%- block extra_styles %} - {%- endblock -%} - - {%- endblock styles -%} - - {#- Custom front matter #} - {%- block extrahead -%}{%- endblock -%} - - {# Custom JS #} - {%- block regular_scripts -%} - {% for path in script_files -%} - {{ js_tag(path) }} - {% endfor -%} - {%- endblock regular_scripts -%} - - {# Theme-related JavaScript code #} - {%- block theme_scripts -%} - {%- endblock -%} - - - - - {% block body %} - - {% endblock %} - - {%- block scripts -%} - {%- endblock scripts -%} - - - \ No newline at end of file diff --git a/docs/_templates/sidebar/brand.html b/docs/_templates/sidebar/brand.html deleted file mode 100644 index 7a0140a9..00000000 --- a/docs/_templates/sidebar/brand.html +++ /dev/null @@ -1,102 +0,0 @@ -{#- Hi there! You might be interested in -https://pradyunsg.me/furo/customisation/sidebar/ Although if you're reading -this, chances are that you're either familiar enough with Sphinx that you know -what you're doing, or landed here from that documentation page. Hope your day's -going well. :) -#} - - -
- - - - Github - -
- - - - - - - - - - -  colab -
diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index bfd1ed83..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,127 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys - -sys.path.insert(0, os.path.abspath("..")) -sys.path.append(os.path.abspath("./_pygments")) - -# -- Project information ----------------------------------------------------- - -project = "nerfstudio" -copyright = "2022, nerfstudio Team" -author = "nerfstudio Team" - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinxarg.ext", - "sphinx.ext.duration", - "sphinx.ext.doctest", - "sphinx.ext.autodoc", - "sphinx.ext.intersphinx", - "sphinxemoji.sphinxemoji", - "myst_nb", - "sphinx.ext.napoleon", - "sphinx_copybutton", - "sphinx_design", - "sphinx.ext.mathjax", - "sphinxext.opengraph", - "sphinx.ext.viewcode", -] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] -suppress_warnings = ["myst.header"] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -# Needed for interactive plotly in notebooks -html_js_files = [ - "require.min.js", - "custom.js", -] - -# -- MYST configs ----------------------------------------------------------- - -# To enable admonitions: -myst_enable_extensions = ["amsmath", "colon_fence", "deflist", "dollarmath", "html_image", "substitution"] - - -# -- Options for open graph ------------------------------------------------- - -ogp_site_url = "http://docs.nerf.studio/" -ogp_image = "https://assets.nerf.studio/opg.png" - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "furo" -html_title = "nerfstudio" - -autosectionlabel_prefix_document = True - -html_theme_options = { - "light_css_variables": { - "color-brand-primary": "#d34600", - "color-brand-content": "#ff6f00", - }, - "dark_css_variables": { - "color-brand-primary": "#fdd06c", - "color-brand-content": "##fea96a", - }, - "light_logo": "imgs/logo.png", - "dark_logo": "imgs/logo-dark.png", -} - -# -- Code block theme -------------------------------------------------------- - -pygments_style = "style.NerfstudioStyleLight" -pygments_dark_style = "style.NerfstudioStyleDark" - -# -- Napoleon settings ------------------------------------------------------- - -# Settings for parsing non-sphinx style docstrings. We use Google style in this -# project. -napoleon_google_docstring = True -napoleon_numpy_docstring = False -napoleon_include_init_with_doc = False -napoleon_include_private_with_doc = False -napoleon_include_special_with_doc = True -napoleon_use_admonition_for_examples = False -napoleon_use_admonition_for_notes = False -napoleon_use_admonition_for_references = False -napoleon_use_ivar = False -napoleon_use_param = True -napoleon_use_rtype = True -napoleon_preprocess_types = False -napoleon_type_aliases = None -napoleon_attr_annotations = True - -# -- MYSTNB ----------------------------------------------------------------- - -suppress_warnings = ["mystnb.unknown_mime_type", "myst.header"] -nb_execution_mode = "off" diff --git a/docs/developer_guides/config.md b/docs/developer_guides/config.md deleted file mode 100644 index acf6914d..00000000 --- a/docs/developer_guides/config.md +++ /dev/null @@ -1,120 +0,0 @@ -# Customizable configs - -Our dataclass configs allow you to easily plug in different permutations of models, dataloaders, modules, etc. -and modify all parameters from a typed CLI supported by [tyro](https://pypi.org/project/tyro/). - -### Base components - -All basic, reusable config components can be found in `nerfstudio/configs/base_config.py`. The `Config` class at the bottom of the file is the upper-most config level and stores all of the sub-configs needed to get started with training. - -You can browse this file and read the attribute annotations to see what configs are available and what each specifies. - -### Creating new configs - -If you are interested in creating a brand new model or data format, you will need to create a corresponding config with associated parameters you want to expose as configurable. - -Let's say you want to create a new model called Nerfacto. You can create a new `Model` class that extends the base class as described [here](pipelines/models.ipynb). Before the model definition, you define the actual `NerfactoModelConfig` which points to the `NerfactoModel` class (make sure to wrap the `_target` classes in a `field` as shown below). - -:::{admonition} Tip -:class: info - -You can then enable type/auto complete on the config passed into the `NerfactoModel` by specifying the config type below the class definition. -::: - -```python -"""nerfstudio/models/nerfacto.py""" - -@dataclass -class NerfactoModelConfig(ModelConfig): - """Nerfacto Model Config""" - - _target: Type = field(default_factory=lambda: NerfactoModel) - ... - -class NerfactoModel(Model): - """Nerfacto model - - Args: - config: Nerfacto configuration to instantiate model - """ - - config: NerfactoModelConfig - ... -``` - -The same logic applies to all other custom configs you want to create. For more examples, you can see `nerfstudio/data/dataparsers/nerfstudio_dataparsers.py`, `nerfstudio/data/datamanagers.py`. - -:::{admonition} See Also -:class: seealso - -For how to create the actual data and model classes that follow the configs, please refer to [pipeline overview](pipelines/index.rst). -::: - -### Updating method configs - -If you are interested in creating a new model config, you will have to modify the `nerfstudio/configs/method_configs.py` file. This is where all of the configs for implemented models are housed. You can browse this file to see how we construct various existing models by modifying the `Config` class and specifying new or modified default components. - -For instance, say we created a brand new model called Nerfacto that has an associated `NerfactoModelConfig`, we can specify the following new Config by overriding the pipeline and optimizers attributes appropriately. - -```python -"""nerfstudio/configs/method_configs.py""" - -method_configs["nerfacto"] = Config( - method_name="nerfacto", - pipeline=VanillaPipelineConfig( - model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 14), - ), - optimizers={ - "proposal_networks": { - "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), - "scheduler": None, - }, - "fields": { - "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15), - "scheduler": None, - }, - }, -) -``` - -After placing your new `Config` class into the `method_configs` dictionary, you can provide a description for the model by updating the `descriptions` dictionary at the top of the file. - -### Modifying from CLI - -Often times, you just want to play with the parameters of an existing model without having to specify a new one. You can easily do so via CLI. Below, we showcase some useful CLI commands. - -- List out all existing models - - ```bash - ns-train --help - ``` - -- List out all existing configurable parameters for `{METHOD_NAME}` - - ```bash - ns-train {METHOD_NAME} --help - ``` - -- Change the train/eval dataset - - ```bash - ns-train {METHOD_NAME} --data DATA_PATH - ``` - -- Enable the viewer - - ```bash - ns-train {METHOD_NAME} --vis viewer - ``` - -- See what options are available for the specified dataparser (e.g. blender-data) - - ```bash - ns-train {METHOD_NAME} {DATA_PARSER} --help - ``` - -- Run with changed dataparser attributes and viewer on - ```bash - # NOTE: the dataparser and associated configurations go at the end of the command - ns-train {METHOD_NAME} --vis viewer {DATA_PARSER} --scale-factor 0.5 - ``` diff --git a/docs/developer_guides/debugging_tools/benchmarking.md b/docs/developer_guides/debugging_tools/benchmarking.md deleted file mode 100644 index bf97db4b..00000000 --- a/docs/developer_guides/debugging_tools/benchmarking.md +++ /dev/null @@ -1,93 +0,0 @@ -# Benchmarking workflow - -We make it easy to benchmark your new NeRF against the standard Blender dataset. - -## Launching training on Blender dataset - -To start, you will need to train your NeRF on each of the blender objects. -To launch training jobs automatically on each of these items, you can call: - -```bash - -./nerfstudio/scripts/benchmarking/launch_train_blender.sh -m {METHOD_NAME} [-s] [-v {VIS}] [{GPU_LIST}] -``` - -Simply replace the arguments in brackets with the correct arguments. - -- `-m {METHOD_NAME}`: Name of the method you want to benchmark (e.g. `nerfacto`, `mipnerf`). -- `-s`: Launch a single job per GPU. -- `-v {VIS}`: Use another visualization than wandb, which is the default. Other options are comet & tensorboard. -- `{GPU_LIST}`: (optional) Specify the list of gpus you want to use on your machine space separated. for instance, if you want to use GPU's 0-3, you will need to pass in `0 1 2 3`. If left empty, the script will automatically find available GPU's and distribute training jobs on the available GPUs. - -:::{admonition} Tip -:class: info - -To view all the arguments and annotations, you can run `./nerfstudio/scripts/benchmarking/launch_train_blender.sh --help` -::: - -A full example would be: - -- Specifying gpus - - ```bash - ./nerfstudio/scripts/benchmarking/launch_train_blender.sh -m nerfacto 0 1 2 3 - ``` - -- Automatically find available gpus - ```bash - ./nerfstudio/scripts/benchmarking/launch_train_blender.sh -m nerfacto - ``` - -The script will automatically launch training on all of the items and save the checkpoints in an output directory with the experiment name and current timestamp. - -## Evaluating trained Blender models - -Once you have launched training, and training converges, you can test your method with `nerfstudio/scripts/benchmarking/launch_eval_blender.sh`. - -Say we ran a benchmark on 08-10-2022 for `instant-ngp`. By default, the train script will save the benchmarks in the following format: - -``` -outputs -└───blender_chair_2022-08-10 -| └───instant-ngp -| └───2022-08-10_172517 -| └───config.yml -| ... -└───blender_drums_2022-08-10 -| └───instant-ngp -| └───2022-08-10_172517 -| └───config.yml -| ... -... -``` - -If we wanted to run the benchmark on all the blender data for the above example, we would run: - -```bash - -./nerfstudio/scripts/benchmarking/launch_eval_blender.sh -m instant-ngp -o outputs/ -t 2022-08-10_172517 [{GPU_LIST}] -``` - -The flags used in the benchmarking script are defined as follows: - -- `-m`: config name (e.g. `instant-ngp`). This should be the same as what was passed in for -c in the train script. -- `-o`: base output directory for where all of the benchmarks are stored (e.g. `outputs/`). Corresponds to the `--output-dir` in the base `Config` for training. -- `-t`: timestamp of benchmark; also the identifier (e.g. `2022-08-10_172517`). -- `-s`: Launch a single job per GPU. -- `{GPU_LIST}`: (optional) Specify the list of gpus you want to use on your machine space separated. For instance, if you want to use GPU's 0-3, you will need to pass in `0 1 2 3`. If left empty, the script will automatically find available GPU's and distribute evaluation jobs on the available GPUs. - -The script will simultaneously run the benchmarking across all the objects in the blender dataset and calculates the PSNR/FPS/other stats. The results are saved as .json files in the `-o` directory with the following format: - -``` -outputs -└───instant-ngp -| └───blender_chair_2022-08-10_172517.json -| | blender_ficus_2022-08-10_172517.json -| | ... -``` - -:::{admonition} Warning -:class: warning - -Since we are running multiple backgrounded processes concurrently with this script, please note the terminal logs may be messy. -::: diff --git a/docs/developer_guides/debugging_tools/index.rst b/docs/developer_guides/debugging_tools/index.rst deleted file mode 100644 index 1a6aab8e..00000000 --- a/docs/developer_guides/debugging_tools/index.rst +++ /dev/null @@ -1,12 +0,0 @@ -Debugging tools -==================== - -We document a few of the supported tooling systems and pipelines we support for debugging our models (e.g. profiling to debug speed). -As we grow, we hope to provide more updated and extensive tooling support. - -.. toctree:: - :maxdepth: 1 - - local_logger - profiling - benchmarking \ No newline at end of file diff --git a/docs/developer_guides/debugging_tools/local_logger.md b/docs/developer_guides/debugging_tools/local_logger.md deleted file mode 100644 index 92461154..00000000 --- a/docs/developer_guides/debugging_tools/local_logger.md +++ /dev/null @@ -1,48 +0,0 @@ -# Local writer - -The `LocalWriter` simply outputs numerical stats to the terminal. -You can specify additional parameters to customize your logging experience. -A skeleton of the local writer config is defined below. - -```python -"""nerfstudio/configs/base_config.py"""" - -@dataclass -class LocalWriterConfig(InstantiateConfig): - """Local Writer config""" - - _target: Type = writer.LocalWriter - enable: bool = False - stats_to_track: Tuple[writer.EventName, ...] = ( - writer.EventName.ITER_TRAIN_TIME, - ... - ) - max_log_size: int = 10 - -``` - -You can customize the local writer by editing the attributes: -- `enable`: enable/disable the logger. -- `stats_to_track`: all the stats that you want to print to the terminal (see list under `EventName` in `utils/writer.py`). You can add or remove any of the defined enums. -- `max_log_size`: how much content to print onto the screen (By default, only print 10 lines onto the screen at a time). If 0, will print everything without deleting any previous lines. - -:::{admonition} Tip -:class: info - -If you want to create a new stat to track, simply add the stat name to the `EventName` enum. -- Remember to call some put event (e.g. `put_scalar` from `utils/writer.py` to place the value in the `EVENT_STORAGE`. -- Remember to add the new enum to the `stats_to_track` list - ::: - -The local writer is easily configurable via CLI. -A few common commands to use: - -- Disable local writer - ```bash - ns-train {METHOD_NAME} --logging.local-writer.no-enable - ``` - -- Disable line wrapping - ```bash - ns-train {METHOD_NAME} --logging.local-writer.max-log-size=0 - ``` \ No newline at end of file diff --git a/docs/developer_guides/debugging_tools/profiling.md b/docs/developer_guides/debugging_tools/profiling.md deleted file mode 100644 index aabada5f..00000000 --- a/docs/developer_guides/debugging_tools/profiling.md +++ /dev/null @@ -1,86 +0,0 @@ -# Code profiling support - -We provide built-in performance profiling capabilities to make it easier for you to debug and assess the performance of your code. - -#### In-house profiler - -You can use our built-in profiler. By default, it is enabled and will print at the termination of the program. You can disable it via CLI using the flag `--logging.no-enable-profiler`. - - -The profiler computes the average total time of execution for any function with the `@profiler.time_function` decorator. -For instance, if you wanted to profile the total time it takes to generate rays given pixel and camera indices via the `RayGenerator` class, you might want to time its `forward()` function. In that case, you would need to add the decorator to the function. - -```python -"""nerfstudio/model_components/ray_generators.py"""" - -class RayGenerator(nn.Module): - - ... - - @profiler.time_function # <-- add the profiler decorator before the function - def forward(self, ray_indices: TensorType["num_rays", 3]) -> RayBundle: - # implementation here - ... -``` - -Alternatively, you can also time parts of the code: -```python -... - -def forward(self, ray_indices: TensorType["num_rays", 3]) -> RayBundle: - # implementation here - with profiler.time_function("code1"): - # some code here - ... - - with profiler.time_function("code2"): - # some code here - ... - ... -``` - - -At termination of training or end of the training run, the profiler will print out the average execution time for all of the functions or code blocks that have the profiler tag. - -:::{admonition} Tip -:class: info - -Use this profiler if there are *specific/individual functions* you want to measure the times for. - ::: - - -#### Profiling with PyTorch profiler - -If you want to profile the training or evaluation code and track the memory and CUDA kernel launches, consider using [PyTorch profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html). -It will run the profiler for some selected step numbers, once with `CUDA_LAUNCH_BLOCKING=1`, once with `CUDA_LAUNCH_BLOCKING=0`. -The PyTorch profiler can be enabled with `--logging.profiler=pytorch` flag. -The outputs of the profiler are trace files stored in `{PATH_TO_MODEL_OUTPUT}/profiler_traces`, and can be loaded in Google Chrome by typing `chrome://tracing`. - - -#### Profiling with PySpy - -If you want to profile the entire codebase, consider using [PySpy](https://github.com/benfred/py-spy). - -Install PySpy - -```bash -pip install py-spy -``` - -To perform the profiling, you can either specify that you want to generate a flame graph or generate a live-view of the profiler. - -- flame graph: with wandb logging and our inhouse logging disabled - ```bash - program="ns-train nerfacto -- --vis=wandb --logging.no-enable-profiler blender-data" - py-spy record -o {PATH_TO_OUTPUT_SVG} $program - ``` -- top-down stats: running same program configuration as above - ```bash - py-spy top $program - ``` - -:::{admonition} Attention -:class: attention - -In defining `program`, you will need to add an extra `--` before you specify your program's arguments. - ::: \ No newline at end of file diff --git a/docs/developer_guides/new_methods.md b/docs/developer_guides/new_methods.md deleted file mode 100644 index dd1ef31e..00000000 --- a/docs/developer_guides/new_methods.md +++ /dev/null @@ -1,154 +0,0 @@ -# Adding a New Method - -Nerfstudio aims to offer researchers a codebase that they can utilize to extend and develop novel methods. Our vision is for users to establish a distinct repository that imports nerfstudio and overrides pipeline components to cater to specific functionality requirements of the new approach. If any of the new features require modifications to the core nerfstudio repository and can be generally useful, we encourage you to submit a PR to enable others to benefit from it. - -You can use the [nerfstudio-method-template](https://github.com/nerfstudio-project/nerfstudio-method-template) repository as a minimal guide to register your new methods. Examples are often the best way to learn, take a look at the [LERF](https://github.com/kerrj/lerf) repository for a good example of how to extend and use nerfstudio in your projects. - -## File Structure - -We recommend the following file structure: - -``` -β”œβ”€β”€ my_method -β”‚ β”œβ”€β”€ __init__.py -β”‚ β”œβ”€β”€ my_config.py -β”‚ β”œβ”€β”€ custom_pipeline.py [optional] -β”‚ β”œβ”€β”€ custom_model.py [optional] -β”‚ β”œβ”€β”€ custom_field.py [optional] -β”‚ β”œβ”€β”€ custom_datamanger.py [optional] -β”‚ β”œβ”€β”€ custom_dataparser.py [optional] -β”‚ β”œβ”€β”€ ... -β”œβ”€β”€ pyproject.toml -``` - -## Registering custom model with nerfstudio - -In order to extend the Nerfstudio and register your own methods, you can package your code as a python package -and register it with Nerfstudio as a `nerfstudio.method_configs` entrypoint in the `pyproject.toml` file. -Nerfstudio will automatically look for all registered methods and will register them to be used -by methods such as `ns-train`. - -First create a config file: - -```python -"""my_method/my_config.py""" - -from nerfstudio.engine.trainer import TrainerConfig -from nerfstudio.plugins.types import MethodSpecification - -MyMethod = MethodSpecification( - config=TrainerConfig( - method_name="my-method", - pipeline=... - ... - ), - description="Custom description" -) -``` - -Then create a `pyproject.toml` file. This is where the entrypoint to your method is set and also where you can specify additional dependencies required by your codebase. - -```python -"""pyproject.toml""" - -[project] -name = "my_method" - -dependencies = [ - "nerfstudio" # you may want to consider pinning the version, ie "nerfstudio==0.1.19" -] - -[tool.setuptools.packages.find] -include = ["my_method*"] - -[project.entry-points.'nerfstudio.method_configs'] -my-method = 'my_method.my_config:MyMethod' -``` - -finally run the following to register the method, - -``` -pip install -e . -``` - -When developing a new method you don't always want to install your code as a package. -Instead, you may use the `NERFSTUDIO_METHOD_CONFIGS` environment variable to temporarily register your custom method. - -``` -export NERFSTUDIO_METHOD_CONFIGS="my-method=my_method.my_config:MyMethod" -``` - -The `NERFSTUDIO_METHOD_CONFIGS` environment variable additionally accepts a function or derived class to temporarily register your custom method. - -```python -"""my_method/my_config.py""" - -from dataclasses import dataclass -from nerfstudio.engine.trainer import TrainerConfig -from nerfstudio.plugins.types import MethodSpecification - -def MyMethodFunc(): - return MethodSpecification( - config=TrainerConfig(...) - description="Custom description" - ) - -@dataclass -class MyMethodClass(MethodSpecification): - config: TrainerConfig = TrainerConfig(...) - description: str = "Custom description" -``` - -## Registering custom dataparser with nerfstudio - -We also support adding new dataparsers in a similar way. In order to extend the NeRFstudio and register a customized dataparser, you can register it with Nerfstudio as a `nerfstudio.dataparser_configs` entrypoint in the `pyproject.toml` file. Nerfstudio will automatically look for all registered dataparsers and will register them to be used by methods such as `ns-train`. - -You can declare the dataparser in the same config file: - -```python -"""my_method/my_config.py""" - -from nerfstudio.plugins.registry_dataparser import DataParserSpecification -from my_method.custom_dataparser import CustomDataparserConfig - -MyDataparser = DataParserSpecification(config=CustomDataparserConfig()) -``` - -Then add the following lines in the `pyproject.toml` file, where the entrypoint to the new dataparser is set. - -```python -"""pyproject.toml""" - -[project] -name = "my_method" - -[project.entry-points.'nerfstudio.dataparser_configs'] -custom-dataparser = 'my_method.my_config:MyDataparser' -``` - -finally run the following to register the dataparser. - -``` -pip install -e . -``` - -Similarly to the method development, you can also use environment variables to register dataparsers. -Use the `NERFSTUDIO_DATAPARSER_CONFIGS` environment variable: - -``` -export NERFSTUDIO_DATAPARSER_CONFIGS="my-dataparser=my_package.my_config:MyDataParser" -``` - -Same as with custom methods, `NERFSTUDIO_DATAPARSER_CONFIGS` environment variable additionally accepts a function or derived class to temporarily register your custom method. - -## Running custom method - -After registering your method you should be able to run the method with, - -``` -ns-train my-method --data DATA_DIR -``` - -## Adding to the _nerf.studio_ documentation - -We invite researchers to contribute their own methods to our online documentation. You can find more information on how to do this {ref}`here`. diff --git a/docs/developer_guides/pipelines/datamanagers.md b/docs/developer_guides/pipelines/datamanagers.md deleted file mode 100644 index 84722ba7..00000000 --- a/docs/developer_guides/pipelines/datamanagers.md +++ /dev/null @@ -1,96 +0,0 @@ -# DataManagers - -```{image} imgs/pipeline_datamanager-light.png -:align: center -:class: only-light -:width: 600 -``` - -```{image} imgs/pipeline_datamanager-dark.png -:align: center -:class: only-dark -:width: 600 -``` - -## What is a DataManager? - -The DataManager returns RayBundle and RayGT objects. Let's first take a look at the most important abstract methods required by the DataManager. - -```python -class DataManager(nn.Module): - """Generic data manager's abstract class - """ - - @abstractmethod - def next_train(self, step: int) -> Tuple[RayBundle, Dict]: - """Returns the next batch of data for train.""" - - @abstractmethod - def next_eval(self, step: int) -> Tuple[RayBundle, Dict]: - """Returns the next batch of data for eval.""" - - @abstractmethod - def next_eval_image(self, step: int) -> Tuple[int, RayBundle, Dict]: - """Returns the next eval image. - - Returns: - The image index from the eval dataset, the CameraRayBundle, and the RayGT dictionary. - """ -``` - -## Example - -We've implemented a VanillaDataManager that implements the standard logic of most NeRF papers. It will randomly sample training rays with corresponding ground truth information, in RayBundle and RayGT objects respectively. The config for the VanillaDataManager is the following. - -```python -@dataclass -class VanillaDataManagerConfig(InstantiateConfig): - """Configuration for data manager instantiation; DataManager is in charge of keeping the train/eval dataparsers; - After instantiation, data manager holds both train/eval datasets and is in charge of returning unpacked - train/eval data at each iteration - """ - - _target: Type = field(default_factory=lambda: VanillaDataManager) - """target class to instantiate""" - dataparser: AnnotatedDataParserUnion = BlenderDataParserConfig() - """specifies the dataparser used to unpack the data""" - train_num_rays_per_batch: int = 1024 - """number of rays per batch to use per training iteration""" - train_num_images_to_sample_from: int = -1 - """number of images to sample during training iteration""" - eval_num_rays_per_batch: int = 1024 - """number of rays per batch to use per eval iteration""" - eval_num_images_to_sample_from: int = -1 - """number of images to sample during eval iteration""" - camera_optimizer: CameraOptimizerConfig = CameraOptimizerConfig() - """specifies the camera pose optimizer used during training""" -``` - -Let's take a quick look at how the `next_train` method is implemented. Here we sample images, then pixels, and then return the RayBundle and RayGT information. - -```python -def next_train(self, step: int) -> Tuple[RayBundle, Dict]: - """Returns the next batch of data from the train dataloader.""" - self.train_count += 1 - # sample a batch of images - image_batch = next(self.iter_train_image_dataloader) - # sample pixels from this batch of images - batch = self.train_pixel_sampler.sample(image_batch) - ray_indices = batch["indices"] - # generate rays from this image and pixel indices - ray_bundle = self.train_ray_generator(ray_indices) - # return RayBundle and RayGT information - return ray_bundle, batch -``` - -You can see our code for more details. - -```{button-link} https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/data/datamanagers/base_datamanager.py -:color: primary -:outline: -See the code! -``` - -## Creating Your Own - -We currently don't have other implementations because most papers follow the VanillaDataManager implementation. However, it should be straightforward to add a VanillaDataManager with logic that progressively adds cameras, for instance, by relying on the step and modifying RayBundle and RayGT generation logic. diff --git a/docs/developer_guides/pipelines/dataparsers.md b/docs/developer_guides/pipelines/dataparsers.md deleted file mode 100644 index cb1693aa..00000000 --- a/docs/developer_guides/pipelines/dataparsers.md +++ /dev/null @@ -1,134 +0,0 @@ -# DataParsers - -```{image} imgs/pipeline_parser-light.png -:align: center -:class: only-light -:width: 600 -``` - -```{image} imgs/pipeline_parser-dark.png -:align: center -:class: only-dark -:width: 600 -``` - -## What is a DataParser? - -The dataparser returns `DataparserOutputs`, which puts all the various datasets into a common format. The DataparserOutputs should be lightweight, containing filenames or other meta information which can later be processed by actual PyTorch Datasets and Dataloaders. The common format makes it easy to add another DataParser. All you have to do is implement the private method `_generate_dataparser_outputs` shown below. - -```python -@dataclass -class DataparserOutputs: - """Dataparser outputs for the which will be used by the DataManager - for creating RayBundle and RayGT objects.""" - - image_filenames: List[Path] - """Filenames for the images.""" - cameras: Cameras - """Camera object storing collection of camera information in dataset.""" - alpha_color: Optional[TensorType[3]] = None - """Color of dataset background.""" - scene_box: SceneBox = SceneBox() - """Scene box of dataset. Used to bound the scene or provide the scene scale depending on model.""" - mask_filenames: Optional[List[Path]] = None - """Filenames for any masks that are required""" - metadata: Dict[str, Any] = to_immutable_dict({}) - """Dictionary of any metadata that be required for the given experiment. - Will be processed by the InputDataset to create any additional tensors that may be required. - """ - dataparser_transform: TensorType[3, 4] = torch.eye(4)[:3, :] - """Transform applied by the dataparser.""" - dataparser_scale: float = 1.0 - """Scale applied by the dataparser.""" - -@dataclass -class DataParser: - - @abstractmethod - def _generate_dataparser_outputs(self, split: str = "train") -> DataparserOutputs: - """Abstract method that returns the dataparser outputs for the given split. - - Args: - split: Which dataset split to generate (train/test). - - Returns: - DataparserOutputs containing data for the specified dataset and split - """ -``` - -## Example - -Here is an example where we implement a DataParser for our Nerfstudio data format. - -```python -@dataclass -class NerfstudioDataParserConfig(DataParserConfig): - """Nerfstudio dataset config""" - - _target: Type = field(default_factory=lambda: Nerfstudio) - """target class to instantiate""" - data: Path = Path("data/nerfstudio/poster") - """Directory specifying location of data.""" - scale_factor: float = 1.0 - """How much to scale the camera origins by.""" - downscale_factor: Optional[int] = None - """How much to downscale images. If not set, images are chosen such that the max dimension is <1600px.""" - scene_scale: float = 1.0 - """How much to scale the region of interest by.""" - orientation_method: Literal["pca", "up", "vertical", "none"] = "up" - """The method to use for orientation.""" - center_method: Literal["poses", "focus", "none"] = "poses" - """The method to use to center the poses.""" - auto_scale_poses: bool = True - """Whether to automatically scale the poses to fit in +/- 1 bounding box.""" - train_split_fraction: float = 0.9 - """The fraction of images to use for training. The remaining images are for eval.""" - depth_unit_scale_factor: float = 1e-3 - """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion.""" - -@dataclass -class Nerfstudio(DataParser): - """Nerfstudio DatasetParser""" - - config: NerfstudioDataParserConfig - - def _generate_dataparser_outputs(self, split="train"): - meta = load_from_json(self.config.data / "transforms.json") - image_filenames = [] - poses = [] - ... - dataparser_outputs = DataparserOutputs( - image_filenames=image_filenames, - cameras=cameras, - scene_box=scene_box, - ) - return dataparser_outputs -``` - -## Train and Eval Logic - -The DataParser will generate a train and eval DataparserOutputs depending on the `split` argument. For example, here is how you'd initialize some `InputDataset` classes that live in the DataManager. Because our DataparserOutputs maintain a common form, our Datasets should be plug-and-play. These datasets will load images needed to supervise the model with `RayGT` objects. - -```python -config = NerfstudioDataParserConfig() -dataparser = config.setup() -# train dataparser -dataparser_outputs = dataparser.get_dataparser_outputs(split="train") -input_dataset = InputDataset(dataparser_outputs) -``` - -You can also pull out information from the DataParserOutputs for other DataManager components, such as the RayGenerator. The RayGenerator generates RayBundle objects from camera and pixel indices. - -```python -ray_generator = RayGenerator(dataparser_outputs.cameras) -``` - -## Included DataParsers - -```{toctree} ---- -maxdepth: 2 ---- - -../../reference/api/data/dataparsers -``` diff --git a/docs/developer_guides/pipelines/fields.md b/docs/developer_guides/pipelines/fields.md deleted file mode 100644 index 5533db49..00000000 --- a/docs/developer_guides/pipelines/fields.md +++ /dev/null @@ -1,118 +0,0 @@ -# Fields - -```{image} imgs/pipeline_field-light.png -:align: center -:class: only-light -:width: 600 -``` - -```{image} imgs/pipeline_field-dark.png -:align: center -:class: only-dark -:width: 600 -``` - -## What is a Field? - -A Field is a model component that associates a region of space with some sort of quantity. In the most typical case, the input to a field is a 3D location and viewing direction, and the output is density and color. Let's take a look at the code. - -```python -class Field(nn.Module): - """Base class for fields.""" - - @abstractmethod - def get_density(self, ray_samples: RaySamples) -> Tuple[TensorType[..., 1], TensorType[..., "num_features"]]: - """Computes and returns the densities. Returns a tensor of densities and a tensor of features. - - Args: - ray_samples: Samples locations to compute density. - """ - - @abstractmethod - def get_outputs( - self, ray_samples: RaySamples, density_embedding: Optional[TensorType] = None - ) -> Dict[FieldHeadNames, TensorType]: - """Computes and returns the colors. Returns output field values. - - Args: - ray_samples: Samples locations to compute outputs. - density_embedding: Density embeddings to condition on. - """ - - def forward(self, ray_samples: RaySamples): - """Evaluates the field at points along the ray. - - Args: - ray_samples: Samples to evaluate field on. - """ - density, density_embedding = self.get_density(ray_samples) - field_outputs = self.get_outputs(ray_samples, density_embedding=density_embedding) - - field_outputs[FieldHeadNames.DENSITY] = density # type: ignore - return field_outputs -``` - -## Separate density and outputs - -The forward function is the main function you'll use, which takes in RaySamples returns quantities for each sample. You'll notice that the get_density function is called for every field, followed by the get_outputs function. - -The get_outputs function is what you need to implement to return custom data. For example, check out of SemanticNerfField where we rely on different FieldHeads to produce correct dimensional outputs for typical quantities. Our implemented FieldHeads have the following FieldHeadNames names. - -```python -class FieldHeadNames(Enum): - """Possible field outputs""" - - RGB = "rgb" - SH = "sh" - DENSITY = "density" - UNCERTAINTY = "uncertainty" - TRANSIENT_RGB = "transient_rgb" - TRANSIENT_DENSITY = "transient_density" - SEMANTICS = "semantics" -``` - -```{button-link} https://github.com/nerfstudio-project/nerfstudio/blob/master/nerfstudio/field_components/field_heads.py -:color: primary -:outline: -See the code! -``` - -Sometimes all you need is the density from a Field, so we have a helper method called density_fn which takes positions and returns densities. - -## Using Frustums instead of positions - -Let's say you want to query a region of space, rather than a point. Our RaySamples data structure contains Frustums which can be used for exactly this purpose. This enables methods like Mip-NeRF to be implemented in our framework. - -```python -@dataclass -class RaySamples(TensorDataclass): - """Samples along a ray""" - - frustums: Frustums - """Frustums along ray.""" - ... - -@dataclass -class Frustums(TensorDataclass): - """Describes region of space as a frustum.""" - - origins: TensorType["bs":..., 3] - """xyz coordinate for ray origin.""" - directions: TensorType["bs":..., 3] - """Direction of ray.""" - starts: TensorType["bs":..., 1] - """Where the frustum starts along a ray.""" - ends: TensorType["bs":..., 1] - """Where the frustum ends along a ray.""" - pixel_area: TensorType["bs":..., 1] - """Projected area of pixel a distance 1 away from origin.""" - ... -``` - -Take a look at our RaySamples class for more information on the input to our Field classes. - -```{button-link} https://github.com/nerfstudio-project/nerfstudio/blob/master/nerfstudio/fields/base_field.py -:color: primary -:outline: -See the code! -``` diff --git a/docs/developer_guides/pipelines/imgs/pipeline_datamanager-dark.png b/docs/developer_guides/pipelines/imgs/pipeline_datamanager-dark.png deleted file mode 100644 index 18e0210b..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_datamanager-dark.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_datamanager-light.png b/docs/developer_guides/pipelines/imgs/pipeline_datamanager-light.png deleted file mode 100644 index 0d21b70d..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_datamanager-light.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_field-dark.png b/docs/developer_guides/pipelines/imgs/pipeline_field-dark.png deleted file mode 100644 index 2b72c4bc..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_field-dark.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_field-light.png b/docs/developer_guides/pipelines/imgs/pipeline_field-light.png deleted file mode 100644 index 59b440a1..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_field-light.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_model-dark.png b/docs/developer_guides/pipelines/imgs/pipeline_model-dark.png deleted file mode 100644 index 499e1750..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_model-dark.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_model-light.png b/docs/developer_guides/pipelines/imgs/pipeline_model-light.png deleted file mode 100644 index e4534136..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_model-light.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_overview-dark.png b/docs/developer_guides/pipelines/imgs/pipeline_overview-dark.png deleted file mode 100644 index f9e9b172..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_overview-dark.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_overview-light.png b/docs/developer_guides/pipelines/imgs/pipeline_overview-light.png deleted file mode 100644 index 7f1c6b8b..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_overview-light.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_parser-dark.png b/docs/developer_guides/pipelines/imgs/pipeline_parser-dark.png deleted file mode 100644 index 57141655..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_parser-dark.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_parser-light.png b/docs/developer_guides/pipelines/imgs/pipeline_parser-light.png deleted file mode 100644 index b8043580..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_parser-light.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_pipeline-dark.png b/docs/developer_guides/pipelines/imgs/pipeline_pipeline-dark.png deleted file mode 100644 index 836015e0..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_pipeline-dark.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/imgs/pipeline_pipeline-light.png b/docs/developer_guides/pipelines/imgs/pipeline_pipeline-light.png deleted file mode 100644 index e249ced1..00000000 Binary files a/docs/developer_guides/pipelines/imgs/pipeline_pipeline-light.png and /dev/null differ diff --git a/docs/developer_guides/pipelines/index.rst b/docs/developer_guides/pipelines/index.rst deleted file mode 100644 index 7255af29..00000000 --- a/docs/developer_guides/pipelines/index.rst +++ /dev/null @@ -1,49 +0,0 @@ -Pipelines overview -------------------------- - -Here we describe what a Pipeline is and how it works. You can see an overview figure with the major Pipeline components below. - -.. image:: imgs/pipeline_overview-light.png - :width: 600 - :align: center - :alt: pipeline figure - :class: only-light - -.. image:: imgs/pipeline_overview-dark.png - :width: 600 - :align: center - :alt: pipeline figure - :class: only-dark - - -.. admonition:: Note - - RayGT and RayOutputs are currently dictionaries. In the future, they will be typed objects. - - -Why Pipelines? -========================== - -Our goal is for any NeRF paper to be implemented as a Pipeline. - -The Pipeline is composed of two major components, namely the DataManager and the Model. The DataManager is responsible for loading data and generating RayBundle and RayGT objects. RayBundles are the input to the forward pass of the Model. These are needed for both training and inference time. RayGT objects, however, are needed only during training to calculate the losses in the Loss Dict. - -RayBundle objects describe origins and viewing directions. The model will take these rays and render them into quantities as RayOutputs. RayGT contains the necessary ground truth (GT) information needed to compute losses. For example, the GT pixel values can be used to supervise the rendered rays with an L2 loss. - -In the following sections, we describe the Pipeline components and look at their code. - -.. toctree:: - :maxdepth: 1 - - dataparsers - datamanagers - models - fields - pipelines - -Implementing NeRF Papers -========================== - -Let's say you want to create a custom Pipeline that has a custom DataManager and a custom Model. Perhaps you care about dynamically adding cameras to the DataManager during training or you want to importance sample and generate rays from pixels where the loss is high. This can be accomplished by mixing and matching components into a Pipeline. The following guide will take you through an example of this. - -This guide is coming soon! diff --git a/docs/developer_guides/pipelines/models.md b/docs/developer_guides/pipelines/models.md deleted file mode 100644 index 4fb1cb3f..00000000 --- a/docs/developer_guides/pipelines/models.md +++ /dev/null @@ -1,152 +0,0 @@ -# Models - -```{image} imgs/pipeline_model-light.png -:align: center -:class: only-light -:width: 600 -``` - -```{image} imgs/pipeline_model-dark.png -:align: center -:class: only-dark -:width: 600 -``` - -## What is a Model? - -A Model is probably what you think of when you think of a NeRF paper. Often the phrases "Model" and "Method" are used interchangeably and for this reason, our implemented [Methods](/nerfology/methods/index) typically only change the model code. - -A model, at a high level, takes in regions of space described by RayBundle objects, samples points along these rays, and returns rendered values for each ray. So, let's take a look at what it takes to create your own model! - -## Functions to Implement - -[The code](https://github.com/nerfstudio-project/nerfstudio/blob/master/nerfstudio/models/base_model.py) is quite verbose, so here we distill the most important functions with succint descriptions. - -```python -class Model: - - config: ModelConfig - """Set the model config so that Python gives you typed autocomplete!""" - - def populate_modules(self): - """Set the fields and modules.""" - - # Fields - - # Ray Samplers - - # Colliders - - # Renderers - - # Losses - - # Metrics - - def get_param_groups(self) -> Dict[str, List[Parameter]]: - """Returns the parameter groups needed to optimizer your model components.""" - - def get_training_callbacks( - self, training_callback_attributes: TrainingCallbackAttributes - ) -> List[TrainingCallback]: - """Returns the training callbacks, such as updating a density grid for Instant NGP.""" - - def get_outputs(self, ray_bundle: RayBundle): - """Process a RayBundle object and return RayOutputs describing quanties for each ray.""" - - def get_metrics_dict(self, outputs, batch): - """Returns metrics dictionary which will be plotted with comet, wandb or tensorboard.""" - - def get_loss_dict(self, outputs, batch, metrics_dict=None): - """Returns a dictionary of losses to be summed which will be your loss.""" - - def get_image_metrics_and_images( - self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor] - ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]: - """Returns a dictionary of images and metrics to plot. Here you can apply your colormaps.""" -``` - -## Pythonic Configs with Models - -Our config system is most useful when it comes to models. Let's take a look at our Nerfacto model config. - -```python -@dataclass -class NerfactoModelConfig(ModelConfig): - """Nerfacto Model Config""" - - _target: Type = field(default_factory=lambda: NerfactoModel) - near_plane: float = 0.05 - """How far along the ray to start sampling.""" - far_plane: float = 1000.0 - """How far along the ray to stop sampling.""" - background_color: Literal["background", "last_sample"] = "last_sample" - """Whether to randomize the background color.""" - num_proposal_samples_per_ray: Tuple[int] = (64,) - """Number of samples per ray for the proposal network.""" - num_nerf_samples_per_ray: int = 64 - """Number of samples per ray for the nerf network.""" - num_proposal_network_iterations: int = 1 - """Number of proposal network iterations.""" - use_same_proposal_network: bool = False - """Use the same proposal network. Otherwise use different ones.""" - interlevel_loss_mult: float = 1.0 - """Proposal loss multiplier.""" - distortion_loss_mult: float = 0.002 - """Distortion loss multiplier.""" - use_proposal_weight_anneal: bool = True - """Whether to use proposal weight annealing.""" - use_average_appearance_embedding: bool = True - """Whether to use average appearance embedding or zeros for inference.""" - proposal_weights_anneal_slope: float = 10.0 - """Slope of the annealing function for the proposal weights.""" - proposal_weights_anneal_max_num_iters: int = 1000 - """Max num iterations for the annealing function.""" - use_single_jitter: bool = True - """Whether use single jitter or not for the proposal networks.""" -``` - -There are a lot of options! Thankfully, our config system makes this easy to handle. If you want to add another argument, you simply add a value to this config and when you type in `ns-train nerfacto --help`, it will show in the terminal as a value you can modify. - -Furthermore, you have Python autocomplete and static checking working in your favor. At the top of every Model, we specify the config and then can easily pull of values throughout the implementation. Let's take a look at the beginning of the NerfactoModel implementation. - -```python -class NerfactoModel(Model): - """Nerfacto model - - Args: - config: Nerfacto configuration to instantiate model - """ - - config: NerfactoModelConfig - - def populate_modules(self): - """Set the fields and modules.""" - ... - # Fields - self.field = TCNNNerfactoField( - self.scene_box.aabb, - spatial_distortion=scene_contraction, - num_images=self.num_train_data, - use_average_appearance_embedding=self.config.use_average_appearance_embedding, # notice self.config - ) - ... - # Renderers - self.renderer_rgb = RGBRenderer(background_color=self.config.background_color) # notice self.config - self.renderer_accumulation = AccumulationRenderer() - self.renderer_depth = DepthRenderer() -``` - -We invite you to take a look at the Nerfacto model and others to see how our models are formatted. - -```{button-link} https://github.com/nerfstudio-project/nerfstudio/blob/master/nerfstudio/models/nerfacto.py -:color: primary -:outline: -See the code! -``` - -## Implementing a Model - -Now that you understand how the model is structured, you can create a model by populating these functions. We provide a library of model components to pull from when creating your model. Check out those tutorials here! - -One of these components is a Field, which you can learn more about in the next section. Fields associate a quantity of space with a value (e.g., density and color) and are used in every model. diff --git a/docs/developer_guides/pipelines/pipelines.md b/docs/developer_guides/pipelines/pipelines.md deleted file mode 100644 index 36a05274..00000000 --- a/docs/developer_guides/pipelines/pipelines.md +++ /dev/null @@ -1,73 +0,0 @@ -# Pipelines - -```{image} imgs/pipeline_pipeline-light.png -:align: center -:class: only-light -:width: 600 -``` - -```{image} imgs/pipeline_pipeline-dark.png -:align: center -:class: only-dark -:width: 600 -``` - -## What is a Pipeline? - -The Pipeline contains all the code you need to implement a NeRF method. There are two main functions that you need to implement for the Pipeline. - -```python -class Pipeline(nn.Module): - - datamanager: DataManager - model: Model - - @profiler.time_function - def get_train_loss_dict(self, step: int): - """This function gets your training loss dict. This will be responsible for - getting the next batch of data from the DataManager and interfacing with the - Model class, feeding the data to the model's forward function. - - Args: - step: current iteration step to update sampler if using DDP (distributed) - """ - - @profiler.time_function - def get_eval_loss_dict(self, step: int): - """This function gets your evaluation loss dict. It needs to get the data - from the DataManager and feed it to the model's forward function - - Args: - step: current iteration step - """ -``` - -## Vanilla Implementation - -Here you can see a simple implementation of the get_train_loss_dict from the VanillaPipeline. Essentially, all the pipeline has to do is route data from the DataManager to the Model. - -```python -@profiler.time_function -def get_train_loss_dict(self, step: int): - ray_bundle, batch = self.datamanager.next_train(step) - model_outputs = self.model(ray_bundle) - metrics_dict = self.model.get_metrics_dict(model_outputs, batch) - loss_dict = self.model.get_loss_dict(model_outputs, batch, metrics_dict) - return model_outputs, loss_dict, metrics_dict -``` - -## Creating Custom Methods - -:::{admonition} Note -:class: info - -The VanillaPipeline works for most of our methods. -::: - -We also have a DynamicBatchPipeline that is used with InstantNGP to dynamically choose the number of rays to use per training and evaluation iteration. - -```{button-link} https://github.com/nerfstudio-project/nerfstudio/blob/master/nerfstudio/pipelines/dynamic_batch.py -:color: primary -:outline: -See the code! -``` \ No newline at end of file diff --git a/docs/developer_guides/viewer/custom_gui.md b/docs/developer_guides/viewer/custom_gui.md deleted file mode 100644 index 0fd60073..00000000 --- a/docs/developer_guides/viewer/custom_gui.md +++ /dev/null @@ -1,105 +0,0 @@ -# Custom GUI - -We provide support for custom viewer GUI elements that can be defined in any `nn.Module`. Although we don't have any specific use cases in mind, here are some examples of what can be achieved with this feature: - -- Using text input to modify the rendering -- Logging numerical values to the viewer -- Using checkboxes to turn off and on losses -- Using a dropdown to switch between appearances - -## Adding an Element - -To define a custom element, create an instance of one of the provided classes in `nerfstudio.viewer.viewer_elements`, and assign it as a class variable in your `nn.Module`. - -```python -from nerfstudio.viewer.viewer_elements import ViewerNumber - -class MyClass(nn.Module):#must inherit from nn.Module - def __init__(self): - # Must be a class variable - self.custom_value = ViewerNumber(name="My Value", default_value=1.0) -``` -**Element Hierarchy** -The viewer recursively searches all `nn.Module` children of the base `Pipeline` object, and arranges parameters into folders based on their variable names. -For example, a `ViewerElement` defined in `pipeline.model.field` will be in the "Custom/model/field" folder in the GUI. - -**Reading the value** -To read the value of a custom element, simply access its `value` attribute. In this case it will be `1.0` unless modified by the user in the viewer. - -```python -current_value = self.custom_value.value -``` - -**Callbacks** -You can register a callback that will be called whenever a new value for your GUI element is available. For example, one can use a callback to update config parameters when elements are changed: -```python -def on_change_callback(handle: ViewerCheckbox) -> None: - self.config.example_parameter = handle.value - -self.custom_checkbox = ViewerCheckbox( - name="Checkbox", - default_value=False, - cb_hook=on_change_callback, -) -``` - -**Thread safety** -Note that `ViewerElement` values can change asynchronously to model execution. So, it's best practice to store the value of a viewer element once at the beginning -of a forward pass and refer to the static variable afterwards. -```python -class MyModel(Model): - def __init__(self): - self.slider = ViewerSlider(name="Slider", default_value=0.5, min_value=0.0, max_value=1.0) - - def get_outputs(self,ray) - slider_val = self.slider.value - #self.slider.value could change after this, unsafe to use - -``` - - -**Writing to the element** -You can write to a viewer element in Python, which provides a convenient way to track values in your code without the need for comet/wandb/tensorboard or relying on `print` statements. - -```python -self.custom_value.value = x -``` - -:::{admonition} Warning -:class: warning - -Updating module state while training can have unexpected side effects. It is up to the user to ensure that GUI actions are safe. Conditioning on `self.training` can help determine whether effects are applied during forward passes for training or rendering. -::: - -## Example Elements - -```{image} imgs/custom_controls.png -:align: center -:width: 400 -``` - -This was created with the following elements: - -```python -from nerfstudio.viewer.viewer_elements import * - -class MyModel(Model): - def __init__(self): - self.a = ViewerButton(name="My Button", cb_hook=self.handle_btn) - self.b = ViewerNumber(name="Number", default_value=1.0) - self.c = ViewerCheckbox(name="Checkbox", default_value=False) - self.d = ViewerDropdown(name="Dropdown", default_value="A", options=["A", "B"]) - self.e = ViewerSlider(name="Slider", default_value=0.5, min_value=0.0, max_value=1.0) - self.f = ViewerText(name="Text", default_value="Hello World") - self.g = ViewerVec3(name="3D Vector", default_value=(0.1, 0.7, 0.1)) - - self.rgb_renderer = RGBRenderer() -... -class RGBRenderer(nn.Module): - def __init__(self): - #lives in "Custom/model/rgb_renderer" GUI folder - self.a = ViewerRGB(name="F", default_value=(0.1, 0.7, 0.1)) -... -``` - -For more information on the available classes and their arguments, refer to the [API documentation](../../reference/api/viewer.rst) diff --git a/docs/developer_guides/viewer/imgs/custom_controls.png b/docs/developer_guides/viewer/imgs/custom_controls.png deleted file mode 100644 index 557f76b5..00000000 Binary files a/docs/developer_guides/viewer/imgs/custom_controls.png and /dev/null differ diff --git a/docs/developer_guides/viewer/index.md b/docs/developer_guides/viewer/index.md deleted file mode 100644 index 9df5c60d..00000000 --- a/docs/developer_guides/viewer/index.md +++ /dev/null @@ -1,23 +0,0 @@ -# Viewer - -> We have a real-time web viewer that requires no installation. It's available at [https://viewer.nerf.studio/](https://viewer.nerf.studio/), where you can connect to your training job. - -The viewer is built on [Viser](https://github.com/brentyi/viser/tree/main/viser) using [ThreeJS](https://threejs.org/) and packaged into a [ReactJS](https://reactjs.org/) application. This client viewer application will connect via a websocket to a server running on your machine. - -```{toctree} -:titlesonly: - -custom_gui -viewer_control -local_viewer -``` - -## Acknowledgements and references - -We thank the authors and contributors to the following repos, which we've started, used, and modified for our use-cases. - -- [Viser](https://github.com/brentyi/viser/) - made by [Brent Yi](https://github.com/brentyi) -- [meshcat-python](https://github.com/rdeits/meshcat-python) - made by [Robin Deits](https://github.com/rdeits) -- [meshcat](https://github.com/rdeits/meshcat) - made by [Robin Deits](https://github.com/rdeits) -- [ThreeJS](https://threejs.org/) -- [ReactJS](https://reactjs.org/) diff --git a/docs/developer_guides/viewer/local_viewer.md b/docs/developer_guides/viewer/local_viewer.md deleted file mode 100644 index ef8b1ac1..00000000 --- a/docs/developer_guides/viewer/local_viewer.md +++ /dev/null @@ -1,66 +0,0 @@ -# (Legacy Viewer) Local Server - -**Note:** this doc only applies to the legacy version of the viewer, which was the default in in Nerfstudio versions `<=0.3.4`. It was deprecated starting Nerfstudio version `1.0.0`, where it needs to be opted into via the `--vis viewer_legacy` argument. - ---- - -If you are unable to connect to `https://viewer.nerf.studio`, want to use Safari, or want to develop the viewer codebase, you can launch your own local viewer. - -## Installing Dependencies - -```shell -cd nerfstudio/viewer/app -``` - -Install npm (to install yarn) and yarn - -```shell -sudo apt-get install npm -npm install --global yarn -``` - -Install nvm and set the node version -Install nvm with [instructions](https://heynode.com/tutorial/install-nodejs-locally-nvm/). - -```shell -nvm install 17.8.0 -``` - -Now running `node --version` in the shell should print "v17.8.0". -Install package.json dependencies and start the client viewer app: - -```shell -yarn install -``` - -## Launch the web client - -From the `nerfstudio/viewer/app` folder, run: - -```shell -yarn start -``` - -The local webserver runs on port 4000 by default, -so when `ns-train` is running, you can connect to the viewer locally at -[http://localhost:4000/?websocket_url=ws://localhost:7007](http://localhost:4000/?websocket_url=ws://localhost:7007) - -## FAQ - -### Engine node incompatible - -While running `yarn install`, you run into: `The engine "node" is incompatible with this module.` - -**Solution**: - -Install nvm with instructions at [instructions](https://heynode.com/tutorial/install-nodejs-locally-nvm/). - -```shell -nvm install 17.8.0 -``` - -If you cannot install nvm, try ignoring the engines - -```shell -yarn install --ignore-engines -``` diff --git a/docs/developer_guides/viewer/viewer_control.md b/docs/developer_guides/viewer/viewer_control.md deleted file mode 100644 index 26803052..00000000 --- a/docs/developer_guides/viewer/viewer_control.md +++ /dev/null @@ -1,128 +0,0 @@ -# Python Viewer Control - -Similar to [`ViewerElements`](./custom_gui.md), Nerfstudio includes supports a Python interface to the viewer through which you can: - -* Set viewer camera pose and FOV -* Set viewer scene crop -* Retrieve the current viewer camera matrix -* Install listeners for click events inside the viewer window - -## Usage - -First, instantiate a `ViewerControl` object as a class variable inside a model file. -Just like `ViewerElements`, you can create an instance inside any class which inherits from `nn.Module` -and is contained within the `Pipeline` object (for example the `Model`) - -```python -from nerfstudio.viewer.viewer_elements import ViewerControl - -class MyModel(nn.Module): # Must inherit from nn.Module - def __init__(self): - # Must be a class variable - self.viewer_control = ViewerControl() # no arguments -``` -## Get Camera Matrix -To get the current camera intrinsics and extrinsics, use the `get_camera` function. This returns a `nerfstudio.cameras.cameras.Cameras` object. This object can be used to generate `RayBundles`, retrieve intrinsics and extrinsics, and more. - -```python -from nerfstudio.viewer.viewer_elements import ViewerControl, ViewerButton - -class MyModel(nn.Module): # Must inherit from nn.Module - def __init__(self): - ... - def button_cb(button): - # example of using the get_camera function, pass img width and height - # returns a Cameras object with 1 camera - camera = self.viewer_control.get_camera(100,100) - if camera is None: - # returns None when the viewer is not connected yet - return - # get the camera pose - camera_extrinsics_matrix = camera.camera_to_worlds[0,...] # 3x4 matrix - # generate image RayBundle - bundle = camera.generate_rays(camera_indices=0) - # Compute depth, move camera, or whatever you want - ... - self.viewer_button = ViewerButton(name="Dummy Button",cb_hook=button_cb) -``` - -## Set Camera Properties -You can set the viewer camera position and FOV from python. -To set position, you must define a new camera position as well as a 3D "look at" point which the camera aims towards. -```python -from nerfstudio.viewer.viewer_elements import ViewerControl,ViewerButton - -class MyModel(nn.Module): # Must inherit from nn.Module - def __init__(self): - ... - def aim_at_origin(button): - # instant=False means the camera smoothly animates - # instant=True means the camera jumps instantly to the pose - self.viewer_control.set_pose(position=(1,1,1),look_at=(0,0,0),instant=False) - self.viewer_button = ViewerButton(name="Dummy Button",cb_hook=button_cb) -``` - -## Scene Pointer Callbacks -We forward user interactions with the viewer to the `ViewerControl` object, which you can use to interact with the scene. - -We currently support: - - `ViewerClick`: *single* clicks inside the viewer. The click is defined to be a ray that starts at the camera origin and passes through the click point on the screen, in world coordinates. - - `ViewerRectSelect`: drag to select a rectangle in the viewer screen. The rectangle is defined by two points (top-left and bottom-right corners) in normalized OpenCV screen coordinates. - -To do this, register a callback using `register_pointer_cb()`. - -You can also use `unregister_pointer_cb()` to remove callbacks that are no longer needed. A good example is a "Click on Scene" button, that when pressed, would register a callback that would wait for the next click, and then unregister itself. - -Note that the viewer can only listen to *one* scene pointer callback at a time. If you register a new callback, the old one will be unregistered! Be warned that if the callback includes GUI state changes (e.g., re-enabling a disabled button), they may be lost. You can ensure that the GUI state is restored by providing a `removed_cb` function that will be called after the callback is removed. - -```python -from nerfstudio.viewer.viewer_elements import ViewerControl,ViewerClick - -class MyModel(nn.Module): # must inherit from nn.Module - def __init__(self): - # Must be a class variable - self.viewer_control = ViewerControl() # no arguments - - # Listen to clicks in the viewer... - def pointer_click_cb(click: ViewerClick): - print(f"Click at {click.origin} in direction {click.direction}, screen position {click.screen_pos}.") - self.viewer_control.register_pointer_cb("click", pointer_click_cb) - - # Listen to rectangle selections in the viewer... - def pointer_rect_cb(rect: ViewerRectSelect): - print(f"Rectangular selection from {rect.min_bounds} to {rect.max_bounds}.") - self.viewer_control.register_pointer_cb("click", pointer_rect_cb) - - ... - # Or make a button that, once pressed, listens to clicks in the viewer. - def button_cb(button: ViewerButton): - def pointer_click_cb(click: ViewerClick): - ... - self.viewer_control.unregister_pointer_cb() - self.viewer_control.register_pointer_cb("click", pointer_click_cb) - self.viewer_button = ViewerButton(name="Click on Scene", cb_hook=button_cb) - - # Or make a button that, once pressed, listens to clicks in the viewer. - # Here, the button is disabled while it is listening to clicks. - # The button will become enabled again if either: - # - the callback is removed in `pointer_click_cb`, with the `unregister...`, or - # - the callback is overridden by the viewer (to listen to another callback). - def button_cb(button: ViewerButton): - def pointer_click_cb(click: ViewerClick): - ... - self.viewer_control.unregister_pointer_cb() - - def pointer_click_removed_cb(): - self.viewer_button.set_disabled(False) - - self.viewer_button.set_disabled(True) - self.viewer_control.register_pointer_cb( - "click", - cb=pointer_click_cb, - removed_cb=pointer_click_removed_cb - ) - self.viewer_button = ViewerButton(name="Click on Scene", cb_hook=button_cb) -``` - -### Thread safety -Just like `ViewerElement` callbacks, click callbacks are asynchronous to training and can potentially interrupt a call to `get_outputs()`. diff --git a/docs/extensions/blender_addon.md b/docs/extensions/blender_addon.md deleted file mode 100644 index 5b24d723..00000000 --- a/docs/extensions/blender_addon.md +++ /dev/null @@ -1,229 +0,0 @@ -# Blender VFX add-on - -

- -

- -## Overview - -This Blender add-on allows for compositing with a Nerfstudio render as a background layer by generating a camera path JSON file from the Blender camera path, as well as a way to import Nerfstudio JSON files as a Blender camera baked with the Nerfstudio camera path. This add-on also allows compositing multiple NeRF objects into a NeRF scene. This is achieved by importing a mesh or point-cloud representation of the NeRF scene from Nerfstudio into Blender and getting the camera coordinates relative to the transformations of the NeRF representation. Dynamic FOV from the Blender camera is supported and will match the Nerfstudio render. Perspective, equirectangular, VR180, and omnidirectional stereo (VR 360) cameras are supported. This add-on also supports Gaussian Splatting scenes as well, however equirectangular and VR video rendering is not currently supported for splats. - -
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- -## Add-on Setup Instructions - -1. The add-on requires Blender 3.0 or newer, install Blender [here](https://www.blender.org/). - -2. Download Blender Add-on Script - -3. Install and enable the add-on in Blender in `Edit β†’ Preferences β†’ Add-Ons`. The add-on will be visible in the Render Properties tab on the right panel. -
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- -4. The add-on should now be installed in the `Render Properties` panel -
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- -## Scene Setup - -1. Export the mesh or point cloud representation of the NeRF from Nerfstudio, which will be used as reference for the actual NeRF in the Blender scene. Mesh export at a good quality is preferred, however, if the export is not clear or the NeRF is large, a detailed point cloud export will also work. Keep the `save_world_frame` flag as False or in the viewer, de-select the "Save in world frame" checkbox to keep the correct coordinate system for the add-on. - -2. Import the mesh or point cloud representation of the NeRF into the scene. You may need to crop the mesh further. Since it is used as a reference and won't be visible in the final render, only the parts that the blender animation will interact with may be necessary to import. - -3. Select the NeRF mesh or point cloud in the add-on. - -4. Resize, position, or rotate the NeRF representation to fit your scene. - -## Generate Nerfstudio JSON Camera Path from Blender Camera - -1. There are a few ways to hide the reference mesh for the Blender render - - - In object properties, select "Shadow Catcher". This makes the representation invisible in the render, but all shadows cast on it will render. You may have to switch to the cycles renderer to see the shadow catcher option. -
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- - - Note: This may not give ideal results if the mesh is not very clear or occludes other objects in the scene. If this is the case, you can hide the mesh from the render instead by clicking the camera button in the Outliner next to its name. -
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- -2. Verify that the animation plays and the NeRF representation does not occlude the camera. -3. Go to the Nerfstudio Add-on in Render Properties and expand the "Nerfstudio Path Generator" tab in the panel. Use the object selector to select the NeRF representation. Then, select the file path for the output JSON camera path file. -4. Click "Generate JSON file". The output JSON file is named `camera_path_blender.json`. -
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- -5. Render the NeRF with the generated camera path using Nerfstudio in the command prompt or terminal. - -6. Before rendering the Blender animation, go to the Render Properties and in the Film settings select "Transparent" so that the render will be rendered with a clear background to allow it to be composited over the NeRF render. -
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- -7. Now the scene can be rendered and composited over the camera aligned Nerfstudio render. -
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- -### Examples - -

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- -### Additional details - -- You can also apply an environment texture to the Blender scene by using Nerfstudio to render an equirectangular image (360 image) of the NeRF from a place in the scene. -- The settings for the Blender equirectangular camera are: "Panoramic" camera type and panorama type of "Equirectangular". -
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- -- The generated JSON camera path follows the user specified frame start, end, and step fields in the Output Properties in Blender. The JSON file specifies the user specified x and y render resolutions at the given % in the Output Properties. -- The add-on computes the camera coordinates based on the active camera in the scene. -- FOV animated changes of the camera will be matched with the NeRF render. -- Perspective, equirectangular, VR180, and omnidirectional stereo cameras are supported and can be configured within Blender. -- The generated JSON file can be imported into Nerfstudio. Each keyframe of the camera transform in the frame sequence in Blender will be a keyframe in Nerfstudio. The exported JSON camera path is baked, where each frame in the sequence is a keyframe. This is to ensure that frame interpolation across Nerfstudio and Blender do not differ. -
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-- It is recommended to run the camera path in the Nerfstudio web interface with the NeRF to ensure that the NeRF is visible throughout the camera path. -- The fps and keyframe timestamps are based on the "Frame Rate" setting in the Output Properties in Blender. -- The NeRF representation can also be transformed (position, rotation, and scale) as well as animated. -- The pivot point of the NeRF representation should not be changed -- It is recommended to export a high fidelity mesh as the NeRF representation from Nerfstudio. However if that is not possible or the scene is too large, a point cloud representation also works. -- For compositing, it is recommended to convert the video render into image frames to ensure the Blender and NeRF renders are in sync. -- Currently, dynamic camera focus is not supported. -- Compositing with Blender Objects and VR180 or ODS Renders - - Configure the Blender camera to be panoramic equirectangular and enable stereoscopy in the Output Properties. For the VR180 Blender camera, set the panoramic longitude min and max to -90 and 90. - - Under the Stereoscopy panel the Blender camera settings, change the mode to "Parallel", set the Interocular Distance to 0.064 m, and checkmark "Spherical Stereo". - -
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-- Fisheye and orthographic cameras are not supported. -- Renders with Gaussian Splats are supported, but the point cloud or mesh representation would need to be generated from training a NeRF on the same dataset. -- A walkthrough of this section is included in the tutorial video. - -## Create Blender Camera from Nerfstudio JSON Camera Path - -

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- -1. Expand the "Nerfstudio Camera Generator" tab in the panel. After inputting the NeRF representation, select the JSON Nerfstudio file and click "Create Camera from JSON" - -2. A new camera named "NerfstudioCamera" should appear in the viewport with the camera path and FOV of the input file. This camera's type will match the Nerfstudio input file, except fisheye cameras. - -### Additional details - -- Since only the camera path, camera type, and FOV are applied on the created Blender camera, the timing and duration of the animation may need to be adjusted. -- Fisheye cameras imported from Nerfstudio are not supported and will default to perspective cameras. -- Animated NeRF representations will not be reflected in the imported camera path animation. -- The newly created camera is not active by default, so you may need to right click and select "Set Active Camera". -- The newly created Blender camera animation is baked, where each frame in the sequence is a keyframe. This is to ensure that frame interpolation across Nerfstudio and Blender do not differ. -- The resolution settings from the input JSON file do not affect the Blender render settings -- Scale of the camera is not keyframed from the Nerfstudio camera path. -- This newly created camera has a sensor fit of "Vertical" - -## Compositing NeRF Objects in NeRF Environments - -You can composite NeRF objects into a scene with a NeRF background by rendering the cropped NeRF object along with an accumulation render as an alpha mask and compositing that over the background NeRF render. - -

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- -1. Import the background NeRF scene as a point cloud (or mesh, but point cloud is preferred for large scenes). - -2. Export a cropped NeRF mesh of the NeRF object(s) - - - Open the NeRF object from the Nerfstudio viewer and select "Crop Viewport" and accordingly adjust the Scale and Center values of the bounding box to crop the NeRF scene to around the bounds of the object of interest. - -
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- - - Copy over the scale and center values into the Export panel and export the NeRF object as a mesh (point cloud will also work but shadows can be rendered if the object is a mesh) - - - Keep note of the scale and center values for the crop. This can be done by creating a new JSON camera path in the editor which will add a crop section towards the end of the file. - -
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- -3. Import the NeRF object representation. Rescale and position the scene and NeRF object and background environment. You can also animate either of them. - -4. (Optional) To add shadows of the NeRF object(s) - - - Add a plane representing the ground of the environment. In object properties, select "Shadow Catcher" under Visibility. You may have to switch to the cycles renderer to see the shadow catcher option. - - - In the object properties of the NeRF object, go to the Ray Visibility section and deselect the "Camera" option. This will hide the mesh in the Blender render, but keep its shadow. - -
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- - - Render the Blender animation with only the shadow of the object on the shadow catcher visible. Render with a transparent background by selecting "Transparent" under the Film Settings in the Render Properties. - -5. Go to the Nerfstudio Add-on in Render Properties and expand the "Nerfstudio Path Generator" tab in the panel. Use the object selector to select the NeRF environment representation. Then, select the file path for the output JSON camera path file. Click "Generate JSON file". The output JSON file is named `camera_path_blender.json`. It is recommended to rename this JSON file to keep track of the multiple camera paths that will be used to construct the full render. - -6. Generate a Nerfstudio camera path now for the NeRF object. Select the object selector to select the NeRF object. Before generating the new JSON file, you may need to rename the previously generated camera path for the environment or move it to a different directory otherwise the new JSON file will overwrite it. Click "Generate JSON file". The new output JSON file is named `camera_path_blender.json`. You will need to repeat this process for each NeRF object you want to composite in your scene. - -7. Render the NeRF of the background environment using the generated camera path for the environment using the Nerfstudio in the command line or terminal. - -8. Render the NeRF object - - - Open the recently generated blender Nerfstudio JSON camera path and add the crop parameters to the camera path. This section will be placed towards the end of the Blender Nerfstudio camera path after the `is_cycle` field. This can be done by copying over the crop section from the JSON camera path with the scene crop which was exported earlier. Alternatively, you can enter the crop values manually in this format. -
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- - - Render the NeRF object using Nerfstudio and the edited camera path in the command line or terminal. This will be the RGB render. - - - Next, render the accumulation render as an alpha mask of the NeRF object by adding the command line argument `--rendered-output-names accumulation` to the render command. - -
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- -9. Convert each of the Nerfstudio render videos into an image sequence of frames as PNG or JPG files. This will ensure that the frames will be aligned when compositing. You can convert the video mp4 to an image sequence by creating a Blender Video Editing file and rendering the mp4 as JPGs or PNGs. - -10. Composite the NeRF renders in a video editing software such as Adobe Premiere Pro. - - - Place the render of the Nerfstudio background environment at the lowest layer, then place the shadow render of the NeRF object if created. - - - Place the RGB NeRF render of the NeRF object over the environment (and shadow if present) layers and then place the accumulation NeRF object render over the RGB NeRF object render. - - - Apply a filter to use the accumulation render as an alpha mask. In Premiere Pro, apply the effect "Track Matte Key" to the RGB render and select the "Matte" as the video track of the accumulation render and under "Composite Using" select "Matte Luma". - -### Additional Details - -- The RGB and accumulation renders will need to be rendered for each NeRF cropped object in the scene, but not for the NeRF environment if it is not cropped. -- If you will composite a shadow layer, the quality of the exported mesh of the NeRF object should have enough fidelity to cast a shadow, but the texture doesn't need to be clear. -- If motion tracking or compositing over real camera footage, you can add planes or cubes to represent walls or doorways as shadow catcher or holdout objects. This will composite the shadow layer over the NeRF environment and help create alpha masks. -- The pivot point of the NeRF representations should not be changed. -- A walkthrough of this section is included in the tutorial video. - -### Examples - -

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- -## Implementation Details - -For generating the JSON camera path, we iterate over the scene frame sequence (from the start to the end with step intervals) and get the camera 4x4 world matrix at each frame. The world transformation matrix gives the position, rotation, and scale of the camera. We then obtain the world matrix of the NeRF representation at each frame and transform the camera coordinates with this to get the final camera world matrix. This allows us to re-position, rotate, and scale the NeRF representation in Blender and generate the right camera path to render the NeRF accordingly in Nerfstudio. Additionally, we calculate the FOV of the camera at each frame based on the sensor fit (horizontal or vertical), angle of view, and aspect ratio. -Next, we construct the list of keyframes which is very similar to the world matrices of the transformed camera matrix. -Camera properties in the JSON file are based on user specified fields such as resolution (user specified in Output Properties in Blender), camera type (Perspective or Equirectangular). In the JSON file, `aspect` is specified as 1.0, `smoothness_value` is set to 0, and `is_cycle` is set to false. The Nerfstudio render is the fps specified in Blender where the duration is the total number of frames divided by the fps. -Finally, we construct the full JSON object and write it to the file path specified by the user. - -For generating the camera from the JSON file, we create a new Blender camera based on the input file and iterate through the `camera_path` field in the JSON to get the world matrix of the object from the `matrix_to_world` and similarly get the FOV from the `fov` fields. At each iteration, we set the camera to these parameters and insert a keyframe based on the position, rotation, and scale of the camera as well as the focal length of the camera based on the vertical FOV input. diff --git a/docs/extensions/imgs/desolation_unreal.png b/docs/extensions/imgs/desolation_unreal.png deleted file mode 100644 index 3214d14a..00000000 Binary files a/docs/extensions/imgs/desolation_unreal.png and /dev/null differ diff --git a/docs/extensions/imgs/export_nvol.png b/docs/extensions/imgs/export_nvol.png deleted file mode 100644 index d00be381..00000000 Binary files a/docs/extensions/imgs/export_nvol.png and /dev/null differ diff --git a/docs/extensions/imgs/nvol_ready.png b/docs/extensions/imgs/nvol_ready.png deleted file mode 100644 index 4d8d9471..00000000 Binary files a/docs/extensions/imgs/nvol_ready.png and /dev/null differ diff --git a/docs/extensions/imgs/sdfstudio_overview.svg b/docs/extensions/imgs/sdfstudio_overview.svg deleted file mode 100644 index 9183ef45..00000000 --- a/docs/extensions/imgs/sdfstudio_overview.svg +++ /dev/null @@ -1,33888 +0,0 @@ - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/extensions/sdfstudio.md b/docs/extensions/sdfstudio.md deleted file mode 100644 index fa2625d0..00000000 --- a/docs/extensions/sdfstudio.md +++ /dev/null @@ -1,51 +0,0 @@ -# SDFStudio - -[project website](https://autonomousvision.github.io/sdfstudio/) - -```{image} imgs/sdfstudio_overview.svg -:width: 800 -:align: center -:alt: sdfstudio overview figure -``` - -## Overview - -SDFStudio is built on top of nerfstudio. It implements multiple implicit surface reconstruction methods including: - -- UniSurf -- VolSDF -- NeuS -- MonoSDF -- Mono-UniSurf -- Mono-NeuS -- Geo-NeuS -- Geo-UniSurf -- Geo-VolSDF -- NeuS-acc -- NeuS-facto -- NeuralReconW - -You can learn more about these methods [here](https://github.com/autonomousvision/sdfstudio/blob/master/docs/sdfstudio-methods.md#Methods) - -## Surface models in nerfstudio - -We intend to integrate many of the SDFStudio improvements back into the nerfstudio core repository. - -Supported methods: - -- NeuS -- NeuS-facto - -## Citation - -If you use these surface based models in your research, you should consider citing the authors of SDFStudio, - -```none -@misc{Yu2022SDFStudio, - author = {Yu, Zehao and Chen, Anpei and Antic, Bozidar and Peng, Songyou Peng and Bhattacharyya, Apratim - and Niemeyer, Michael and Tang, Siyu and Sattler, Torsten and Geiger, Andreas}, - title = {SDFStudio: A Unified Framework for Surface Reconstruction}, - year = {2022}, - url = {https://github.com/autonomousvision/sdfstudio}, -} -``` diff --git a/docs/extensions/unreal_engine.md b/docs/extensions/unreal_engine.md deleted file mode 100644 index 1fa523a0..00000000 --- a/docs/extensions/unreal_engine.md +++ /dev/null @@ -1,35 +0,0 @@ -# Exporting to Unreal Engine - - ```{image} imgs/desolation_unreal.png - :width: 800 - :align: center - :alt: NeRF in Unreal Engine - ``` - -## Overview - -NeRFStudio models can be used in Unreal Engine if they are converted to an NVOL file. NVOL is a new standard file format to store NeRFs in a fast and efficient way. NVOL files can be obtained from NeRFStudio checkpoints files (.ckpt) using the [Volinga Suite](https://volinga.ai/). - - -## Exporting your model to NVOL -Currently NVOL file only supports Volinga model (which is based on nerfacto). To use Volinga model you will need to install [Volinga extension for NeRFStudio](https://github.com/Volinga/volinga-model). You can train your model using the following command: - -```bash -ns-train volinga --data /path/to/your/data --vis viewer -``` - -Once the training is done, you can find your checkpoint file in the `outputs/path-to-your-data/volinga` folder. Then, you can drag it to Volinga Suite to export it to NVOL. - - ```{image} imgs/export_nvol.png - :width: 400 - :align: center - :alt: Nvol export in Voliga Suite - ``` - -Once the NVOL is ready, you can download it and use it in Unreal Engine. - - ```{image} imgs/nvol_ready.png - :width: 800 - :align: center - :alt: NVOL ready to use - ``` \ No newline at end of file diff --git a/docs/index.md b/docs/index.md deleted file mode 100644 index fcb62c64..00000000 --- a/docs/index.md +++ /dev/null @@ -1,250 +0,0 @@ ---- -myst: - substitutions: - luma: | - ```{image} _static/imgs/luma_light.png - :alt: Luma AI - :width: 300px - :class: only-light - :target: https://lumalabs.ai/ - ``` - - ```{image} _static/imgs/luma_dark.png - :alt: Luma AI - :width: 300px - :class: only-dark - :target: https://lumalabs.ai/ - ``` - bair: | - ```{image} _static/imgs/bair_light.png - :alt: BAIR - :width: 300px - :class: only-light - :target: https://bcommons.berkeley.edu/home - ``` - - ```{image} _static/imgs/bair_dark.png - :alt: BAIR - :width: 300px - :class: only-dark - :target: https://bcommons.berkeley.edu/home - ``` ---- - -```{eval-rst} -:og:description: Nerfstudio Documentation -:og:image: https://assets.nerf.studio/opg.png -``` - -
- -```{image} _static/imgs/logo.png -:width: 400 -:align: center -:alt: nerfstudio -:class: only-light -``` - -```{image} _static/imgs/logo-dark.png -:width: 400 -:align: center -:alt: nerfstudio -:class: only-dark -``` - -
- - - -
- -Nerfstudio provides a simple API that allows for a simplified end-to-end process of creating, training, and testing NeRFs. -The library supports a **more interpretable implementation of NeRFs by modularizing each component.** -With more modular NeRFs, we hope to create a more user-friendly experience in exploring the technology. - -This is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other's contributions. -Nerfstudio initially launched as an opensource project by Berkeley students in [KAIR lab](https://people.eecs.berkeley.edu/~kanazawa/index.html#kair) at [Berkeley AI Research (BAIR)](https://bair.berkeley.edu/) in October 2022 as a part of a research project ([paper](https://arxiv.org/abs/2302.04264)). It is currently developed by Berkeley students and community contributors. - -We are committed to providing learning resources to help you understand the basics of (if you're just getting started), and keep up-to-date with (if you're a seasoned veteran) all things NeRF. As researchers, we know just how hard it is to get onboarded with this next-gen technology. So we're here to help with tutorials, documentation, and more! - -Have feature requests? Want to add your brand-spankin'-new NeRF model? Have a new dataset? **We welcome [contributions](reference/contributing)!** -Please do not hesitate to reach out to the nerfstudio team with any questions via [Discord](https://discord.gg/uMbNqcraFc). - -Have feedback? We'd love for you to fill out our [Nerfstudio Feedback Form](https://forms.gle/sqN5phJN7LfQVwnP9) if you want to let us know who you are, why you are interested in Nerfstudio, or provide any feedback! - -We hope nerfstudio enables you to build faster πŸ”¨ learn together πŸ“š and contribute to our NeRF community πŸ’–. - -## Contents - -```{toctree} -:hidden: -:caption: Getting Started - -quickstart/installation -quickstart/first_nerf -quickstart/existing_dataset -quickstart/custom_dataset -quickstart/viewer_quickstart -quickstart/export_geometry -quickstart/data_conventions -Contributing -``` - -```{toctree} -:hidden: -:caption: Extensions -extensions/blender_addon -extensions/unreal_engine -extensions/sdfstudio -``` - -```{toctree} -:hidden: -:caption: NeRFology - -nerfology/methods/index -nerfology/model_components/index -``` - -```{toctree} -:hidden: -:caption: Developer Guides - -developer_guides/new_methods -developer_guides/pipelines/index -developer_guides/viewer/index -developer_guides/config -developer_guides/debugging_tools/index -``` - -```{toctree} -:hidden: -:caption: Reference - -reference/cli/index -reference/api/index -``` - -This documentation is organized into 3 parts: - -- **πŸƒβ€β™€οΈ Getting Started**: a great place to start if you are new to nerfstudio. Contains a quick tour, installation, and an overview of the core structures that will allow you to get up and running with nerfstudio. -- **πŸ§ͺ Nerfology**: want to learn more about the tech itself? We're here to help with our educational guides. We've provided some interactive notebooks that walk you through what each component is all about. -- **πŸ€“ Developer Guides**: describe all of the components and additional support we provide to help you construct, train, and debug your NeRFs. Learn how to set up a model pipeline, use the viewer, create a custom config, and more. -- **πŸ“š Reference**: describes each class and function. Develop a better understanding of the core of our technology and terminology. This section includes descriptions of each module and component in the codebase. - -## Supported Methods - -### Included Methods - -- [**Nerfacto**](nerfology/methods/nerfacto.md): Recommended method, integrates multiple methods into one. -- [Instant-NGP](nerfology/methods/instant_ngp.md): Instant Neural Graphics Primitives with a Multiresolution Hash Encoding -- [NeRF](nerfology/methods/nerf.md): OG Neural Radiance Fields -- [Mip-NeRF](nerfology/methods/mipnerf.md): A Multiscale Representation for Anti-Aliasing Neural Radiance Fields -- [TensoRF](nerfology/methods/tensorf.md): Tensorial Radiance Fields -- [Splatfacto](nerfology/methods/splat.md): Nerfstudio's Gaussian Splatting implementation - -(third_party_methods)= - -### Third-party Methods - -- [Instruct-NeRF2NeRF](nerfology/methods/in2n.md): Editing 3D Scenes with Instructions -- [K-Planes](nerfology/methods/kplanes.md): Unified 3D and 4D Radiance Fields -- [LERF](nerfology/methods/lerf.md): Language Embedded Radiance Fields -- [Nerfbusters](nerfology/methods/nerfbusters.md): Removing Ghostly Artifacts from Casually Captured NeRFs -- [NeRFPlayer](nerfology/methods/nerfplayer.md): 4D Radiance Fields by Streaming Feature Channels -- [Tetra-NeRF](nerfology/methods/tetranerf.md): Representing Neural Radiance Fields Using Tetrahedra -- [Instruct-GS2GS](nerfology/methods/igs2gs.md): Editing 3DGS Scenes with Instructions -- [PyNeRF](nerfology/methods/pynerf.md): Pyramidal Neural Radiance Fields -- [SeaThru-NeRF](nerfology/methods/seathru_nerf.md): Neural Radiance Field for subsea scenes -- [Zip-NeRF](nerfology/methods/zipnerf.md): Anti-Aliased Grid-Based Neural Radiance Fields - -**Eager to contribute a method?** We'd love to see you use nerfstudio in implementing new (or even existing) methods! Please view our {ref}`guide` for more details about how to add to this list! - -## Quicklinks - -| | | -| ---------------------------------------------------------- | ---------------------- | -| [Github](https://github.com/nerfstudio-project/nerfstudio) | Official Github Repo | -| [Discord](https://discord.gg/RyVk6w5WWP) | Join Discord Community | -| [Feedback Form](https://forms.gle/sqN5phJN7LfQVwnP9) | Provide Nerfstudio Feedback | - -## Sponsors -Sponsors of this work includes [Luma AI](https://lumalabs.ai/) and the [BAIR commons](https://bcommons.berkeley.edu/home). - -| | | -| -------- | -------- | -| {{luma}} | {{bair}} | - -## Built On - -```{image} https://brentyi.github.io/tyro/_static/logo-light.svg -:width: 150 -:alt: tyro -:class: only-light -:target: https://github.com/brentyi/tyro -``` - -```{image} https://brentyi.github.io/tyro/_static/logo-dark.svg -:width: 150 -:alt: tyro -:class: only-dark -:target: https://github.com/brentyi/tyro -``` - -- Easy to use config system -- Developed by [Brent Yi](https://brentyi.com/) - -```{image} https://user-images.githubusercontent.com/3310961/199084143-0d63eb40-3f35-48d2-a9d5-78d1d60b7d66.png -:width: 250 -:alt: tyro -:class: only-light -:target: https://github.com/KAIR-BAIR/nerfacc -``` - -```{image} https://user-images.githubusercontent.com/3310961/199083722-881a2372-62c1-4255-8521-31a95a721851.png -:width: 250 -:alt: tyro -:class: only-dark -:target: https://github.com/KAIR-BAIR/nerfacc -``` - -- Library for accelerating NeRF renders -- Developed by [Ruilong Li](https://www.liruilong.cn/) - -## Citation - -You can find a paper writeup of the framework on [arXiv](https://arxiv.org/abs/2302.04264). - -If you use this library or find the documentation useful for your research, please consider citing: - -```none -@inproceedings{nerfstudio, - title = {Nerfstudio: A Modular Framework for Neural Radiance Field Development}, - author = { - Tancik, Matthew and Weber, Ethan and Ng, Evonne and Li, Ruilong and Yi, Brent - and Kerr, Justin and Wang, Terrance and Kristoffersen, Alexander and Austin, - Jake and Salahi, Kamyar and Ahuja, Abhik and McAllister, David and Kanazawa, - Angjoo - }, - year = 2023, - booktitle = {ACM SIGGRAPH 2023 Conference Proceedings}, - series = {SIGGRAPH '23} -} -``` - -## Contributors - - - - - -## Maintainers - -| | Nerfstudio Discord | Affiliation | -| ----------------------------------------------- | ------------------ | ------------------------------------ | -| [Justin Kerr](https://kerrj.github.io/) | justin.kerr | UC Berkeley | -| [JonΓ‘Ε‘ KulhΓ‘nek](https://jkulhanek.com/) | jkulhanek | Czech Technical University in Prague | -| [Matt Tancik](https://www.matthewtancik.com) | tancik | Luma AI | -| [Matias Turkulainen](https://maturk.github.io/) | maturk | ETH Zurich | -| [Ethan Weber](https://ethanweber.me/) | ethanweber | UC Berkeley | -| [Brent Yi](https://github.com/brentyi) | brent | UC Berkeley | diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 008ca8c4..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,36 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build -set SPHINXOPTS="-W --keep-going" - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/nerfology/methods/generfacto.md b/docs/nerfology/methods/generfacto.md deleted file mode 100644 index 4adba583..00000000 --- a/docs/nerfology/methods/generfacto.md +++ /dev/null @@ -1,62 +0,0 @@ -# Generfacto - -

Generate 3D models from text

- -**Our model that combines generative 3D with our latest NeRF methods** - -## Installation - -First install nerfstudio dependencies. Then run: - -```bash -pip install -e .[gen] -``` - -Two options for text to image diffusion models are provided: Stable Diffusion and DeepFloyd IF. -We use Deepfloyd IF by default because it trains faster and produces better results. Using this model requires users to sign a license agreement for the model card of DeepFloyd IF, which can be found [here](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0). Once the licensed is signed, log into Huggingface locally by running the following command: - -```bash -huggingface-cli login -``` - -If you do not want to sign the license agreement, you can use the Stable Diffusion model (instructions below). - -## Running Generfacto - -Once installed, run: - -```bash -ns-train generfacto --prompt "a high quality photo of a pineapple" -``` - -The first time you run this method, the diffusion model weights will be downloaded and cached -from Hugging Face, which may take a couple minutes. - -Specify which diffusion model to use with the diffusion_model flag: - -```bash -ns-train generfacto --pipeline.model.diffusion_model ["stablediffusion", "deepfloyd"] -``` - -## Example Results - -The following videos are renders of NeRFs generated from Generfacto. Each model was trained 30k steps, which took around 1 hour with DeepFloyd -and around 4 hours with Stable Diffusion. - -"a high quality photo of a ripe pineapple" (Stable Diffusion) - - - -"a high quality zoomed out photo of a palm tree" (DeepFloyd) - - - -"a high quality zoomed out photo of a light grey baby shark" (DeepFloyd) - - diff --git a/docs/nerfology/methods/igs2gs.md b/docs/nerfology/methods/igs2gs.md deleted file mode 100644 index 0b60486f..00000000 --- a/docs/nerfology/methods/igs2gs.md +++ /dev/null @@ -1,103 +0,0 @@ -# Instruct-GS2GS - -

Editing Gaussian Splatting Scenes with Instructions

- -```{button-link} https://instruct-gs2gs.github.io/ -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/cvachha/instruct-gs2gs -:color: primary -:outline: -Code -``` - - - -**Instruct-GS2GS enables instruction-based editing of 3D Gaussian Splatting scenes via a 2D diffusion model** - -## Installation - -First install nerfstudio dependencies. Then run: - -```bash -pip install git+https://github.com/cvachha/instruct-gs2gs -cd instruct-gs2gs -pip install --upgrade pip setuptools -pip install -e . -``` - -## Running Instruct-GS2GS - -Details for running Instruct-GS2GS (built with Nerfstudio!) can be found [here](https://github.com/cvachha/instruct-gs2gs). Once installed, run: - -```bash -ns-train igs2gs --help -``` - -| Method | Description | Memory | -| ------------ | ---------------------------- | ------ | -| `igs2gs` | Full model, used in paper | ~15GB | - -Datasets need to be processed with COLMAP for Gaussian Splatting support. - -Once you have trained your GS scene for 20k iterations, the checkpoints will be saved to the `outputs` directory. Copy the path to the `nerfstudio_models` folder. (Note: We noticed that training for 20k iterations rather than 30k seemed to run more reliably) - -To start training for editing the GS, run the following command: - -```bash -ns-train igs2gs --data {PROCESSED_DATA_DIR} --load-dir {outputs/.../nerfstudio_models} --pipeline.prompt {"prompt"} --pipeline.guidance-scale 12.5 --pipeline.image-guidance-scale 1.5 -``` - -The `{PROCESSED_DATA_DIR}` must be the same path as used in training the original GS. Using the CLI commands, you can choose the prompt and the guidance scales used for InstructPix2Pix. - -## Method - -### Overview - -Instruct-GS2GS is a method for editing 3D Gaussian Splatting (3DGS) scenes with text instructions in a method based on [Instruct-NeRF2NeRF](https://instruct-nerf2nerf.github.io/). Given a 3DGS scene of a scene and the collection of images used to reconstruct it, this method uses an image-conditioned diffusion model ([InstructPix2Pix](https://www.timothybrooks.com/instruct-pix2pix)) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. The paper demonstrates that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish realistic and targeted edits. - - -## Pipeline - - - -This section will walk through each component of the Instruct-GS2GS method. - -### How it Works - -Instruct-GS2GS gradually updates a reconstructed Gaussian Splatting scene by iteratively updating the dataset images while training the 3DGS: - -1. Images are rendered from the scene at all training viewpoints. -2. They get edited by InstructPix2Pix given a global text instruction. -3. The training dataset images are replaced with the edited images. -4. The 3DGS continues training as usual for 2.5k iterations. - -### Editing Images with InstructPix2Pix - -To update a dataset image from a given viewpoint, Instruct-GS2GS takes the original, unedited training image as image conditioning and uses the global text instruction as text conditioning. This process mixes the information of the diffusion model, which attempts to edit the image, the current 3D structure of the 3DGS, and view-consistent information from the unedited, ground-truth images. By combining this set of information, the edit is respected while maintaining 3D consistency. - -The code snippet for how an image is edited in the pipeline can be found [here](https://github.com/cvachha/instruct-gs2gs/blob/main/igs2gs/ip2p.py). - -### Iterative Dataset Update and Implementation - -The method takes in a dataset of camera poses and training images, a trained 3DGS scene, and a user-specified text-prompt instruction, e.g. β€œmake him a marble statue”. Instruct-GS2GS constructs the edited GS scene guided by the text-prompt by applying a 2D text and image conditioned diffusion model, in this case Instruct-Pix2Pix, to all training images over the course of training. It performs these edits using an iterative udpate scheme in which all training dataset images are updated using a diffusion model individually, for sequential iterations spanning the size of the training images, every 2.5k training iterations. This process allows the GS to have a holistic edit and maintain 3D consistency. - -The process is similar to Instruct-NeRF2NeRF where for a given training camera view, it sets the original training image as the conditioning image, the noisy image input as the GS rendered from the camera combined with some randomly selected noise, and receives an edited image respecting the text conditioning. With this method, it is able to propagate the edited changes to the GS scene. The method is able to maintain grounded edits by conditioning Instruct-Pix2Pix on the original unedited training image. - -This method uses Nerfstudio’s gsplat library for our underlying gaussian splatting model. We adapt similar parameters for the diffusion model from Instruct-NeRF2NeRF. Among these are the values that define the amount of noise (and therefore the amount signal retained from the original images). We vary the classifier-free guidance scales per edit and scene, using a range of values. We edit the entire dataset and then train the scene for 2.5k iterations. For GS training, we use L1 and LPIPS losses. We train our method for a maximum of 27.5k iterations (starting with a GS scene trained for 20k iterations). However, in practice we stop training once the edit has converged. In many cases, the optimal training length is a subjective decision β€” a user may prefer more subtle or more extreme edits that are best found at different stages of training. - - -## Results - -For results, view the [project page](https://instruct-gs2gs.github.io/)! - - \ No newline at end of file diff --git a/docs/nerfology/methods/imgs/ingp/hash_figure.png b/docs/nerfology/methods/imgs/ingp/hash_figure.png deleted file mode 100644 index 602d5fdb..00000000 Binary files a/docs/nerfology/methods/imgs/ingp/hash_figure.png and /dev/null differ diff --git a/docs/nerfology/methods/imgs/mipnerf/models_mipnerf_field-dark.png b/docs/nerfology/methods/imgs/mipnerf/models_mipnerf_field-dark.png deleted file mode 100644 index 2c6ade39..00000000 Binary files a/docs/nerfology/methods/imgs/mipnerf/models_mipnerf_field-dark.png and /dev/null differ diff --git a/docs/nerfology/methods/imgs/mipnerf/models_mipnerf_field-light.png b/docs/nerfology/methods/imgs/mipnerf/models_mipnerf_field-light.png 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Editing 3D Scenes with Instructions

- -```{button-link} https://instruct-nerf2nerf.github.io/ -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/ayaanzhaque/instruct-nerf2nerf -:color: primary -:outline: -Code -``` - - - -**Instruct-NeRF2NeRF enables instruction-based editing of NeRFs via a 2D diffusion model** - -## Installation - -First install nerfstudio dependencies. Then run: - -```bash -pip install git+https://github.com/ayaanzhaque/instruct-nerf2nerf -``` - -## Running Instruct-NeRF2NeRF - -Details for running Instruct-NeRF2NeRF (built with Nerfstudio!) can be found [here](https://github.com/ayaanzhaque/instruct-nerf2nerf). Once installed, run: - -```bash -ns-train in2n --help -``` - -Three variants of Instruct-NeRF2NeRF are provided: - -| Method | Description | Memory | Quality | -| ------------ | ---------------------------- | ------ | ------- | -| `in2n` | Full model, used in paper | ~15GB | Best | -| `in2n-small` | Half precision model | ~12GB | Good | -| `in2n-tiny` | Half prevision with no LPIPS | ~10GB | Ok | - -## Method - -### Overview - -Instruct-NeRF2NeRF is a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, the method uses an image-conditioned diffusion model ([InstructPix2Pix](https://www.timothybrooks.com/instruct-pix2pix)) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. The paper demonstrates that their method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work. - -## Pipeline - - - -This section will walk through each component of the Instruct-NeRF2NeRF method. - -### How it Works - -Instruct-NeRF2NeRF gradually updates a reconstructed NeRF scene by iteratively updating the dataset images while training the NeRF: - -1. An image is rendered from the scene at a training viewpoint. -2. It is edited by InstructPix2Pix given a global text instruction. -3. The training dataset image is replaced with the edited image. -4. The NeRF continues training as usual. - -### Editing Images with InstructPix2Pix - -InstructPix2Pix is an image-editing diffusion model which can be prompted using text instructions. More details on InstructPix2Pix can be found [here](https://www.timothybrooks.com/instruct-pix2pix). - -Traditionally, at inference time, InstructPix2Pix takes as input random noise and is conditioned on an image (the image to edit) and a text instruction. The strength of the edit can be controlled using the image and text classifier-free guidance scales. - -To update a dataset image a given viewpoint, Instruct-NeRF2NeRF first takes the original, unedited training image as image conditioning and uses the global text instruction as text conditioning. The main input to the diffusion model is a noised version of the current render from the given viewpoint. The noise is sampled from a normal distribution and scaled based on a randomly chosen timestep. Then InstructPix2Pix slowly denoises the rendered image by predicting the noised version of the image at previous timesteps until the image is fully denoised. This will produce an edited version of the input image. - -This process mixes the information of the diffusion model, which attempts to edit the image, the current 3D structure of the NeRF, and view-consistent information from the unedited, ground-truth images. By combining this set of information, the edit is respected while maintaining 3D consistency. - -The code snippet for how an image is edited in the pipeline can be found [here](https://github.com/ayaanzhaque/instruct-nerf2nerf/blob/main/in2n/ip2p.py). - -### Iterative Dataset Update - -When NeRF training starts, the dataset consists of the original, unedited images used to train the original scene. These images are saved separately to use as conditioning for InstructPix2Pix. At each optimization step, some number of NeRF optimization steps are performed, and then some number of images (often just one) are updated. The images are randomly ordered prior to training and then at each step, the images are chosen in order to edit. Once an image has been edited, it is replaced in the dataset. Importantly, at each NeRF step, rays are sampled across the entire dataset, meaning there is a mixed source of supervision between edited images and unedited images. This allows for a gradual optimization that balances maintaining the 3D structure and consistency of the NeRF as well as performing the edit. - -At early iterations of this process, the edited images may be inconsistent with one another, as InstructPix2Pix often doesn't perform consistent edits across viewpoints. However, over time, since images are edited using the current render of the NeRF, the edits begin to converge towards a globally consistent depiction of the underlying scene. Here is an example of how the underlying dataset evolves and becomes more consistent. - - - -The traditional method for supervising NeRFs using diffusion models is to use a Score Distillation Sampling (SDS) loss, as proposed in [DreamFusion](https://dreamfusion3d.github.io/). The Iterative Dataset Update method can be viewed as a variant of SDS, as instead of updating a discrete set of images at each step, the loss is a mix of rays from various viewpoints which are edited to varying degrees. The results show that this leads to higher quality performance and more stable optimization. - -## Results - -For results, view the [project page](https://instruct-nerf2nerf.github.io/)! diff --git a/docs/nerfology/methods/index.md b/docs/nerfology/methods/index.md deleted file mode 100644 index 5284b688..00000000 --- a/docs/nerfology/methods/index.md +++ /dev/null @@ -1,74 +0,0 @@ -# Methods - -We provide a set of pre implemented nerfstudio methods. - -**The goal of nerfstudio is to modularize the various NeRF techniques as much as possible.** - -As a result, many of the techniques from these pre-implemented methods can be mixed 🎨. - -## Running a method - -It's easy! - -```bash -ns-train {METHOD_NAME} -``` - -To list the available methods run: - -```bash -ns-train --help -``` - -## Implemented Methods - -The following methods are supported in nerfstudio: - -```{toctree} - :maxdepth: 1 - Instant-NGP - Splatfacto - Instruct-NeRF2NeRF - K-Planes - LERF - Mip-NeRF - NeRF - Nerfacto - Nerfbusters - NeRFPlayer - Tetra-NeRF - TensoRF - Generfacto - Instruct-GS2GS - PyNeRF - SeaThru-NeRF - Zip-NeRF -``` - -(own_method_docs)= - -## Adding Your Own Method - -If you're a researcher looking to develop new NeRF-related methods, we hope that you find nerfstudio to be a useful tool. We've provided documentation about integrating with the nerfstudio codebase, which you can find [here](../../developer_guides/new_methods.md). - -We also welcome additions to the list of methods above. To do this, simply create a pull request with the following changes, - -1. Add a markdown file describing the model to the `docs/nerfology/methods` folder -2. Update the above list of implement methods in this file. -3. Add the method to {ref}`this` list in `docs/index.md`. -4. Add a new `ExternalMethod` entry to the `nerfstudio/configs/external_methods.py` file. - -For the method description, please refer to the [Instruct-NeRF2NeRF](in2n) page as an example of the layout. Please try to include the following information: - -- Installation instructions -- Instructions for running the method -- Requirements (dataset, GPU, ect) -- Method description (the more detailed the better, treat it like a blog post) - -You are welcome to include assets (such as images or video) in the description, but please host them elsewhere. - -:::{admonition} Note -:class: note - -Please ensure that the documentation is clear and easy to understand for other users who may want to try out your method. -::: diff --git a/docs/nerfology/methods/instant_ngp.md b/docs/nerfology/methods/instant_ngp.md deleted file mode 100644 index 5f5f7e07..00000000 --- a/docs/nerfology/methods/instant_ngp.md +++ /dev/null @@ -1,69 +0,0 @@ -# Instant-NGP - -

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

- -```{button-link} https://nvlabs.github.io/instant-ngp/ -:color: primary -:outline: -Paper Website -``` - - - -### Running the Model - -Instant-NGP is built locally into Nerfstudio. To use the method, run - -```bash -ns-train instant-ngp --help -``` - -Many of the main contributions of Instant-NGP are built into our Nerfacto method, so for real-world scenes, we recommend using the Nerfacto model. - -## Method - -### Overview - -Instant-NGP breaks NeRF training into 3 pillars and proposes improvements to each to enable real-time training of NeRFs. The 3 pillars are: - -1. An improved training and rendering algorithm via a ray marching scheme which uses an occupancy grid -2. A smaller, fully-fused neural network -3. An effective multi-resolution hash encoding, the main contribution of this paper. - -The core idea behind the improved sampling technique is that sampling over empty space should be skipped and sampling behind high density areas should also be skipped. This is achieved by maintaining a set of multiscale occupancy grids which coarsely mark empty and non-empty space. Occupancy is stored as a single bit, and a sample on a ray is skipped if its occupancy is too low. These occupancy grids are stored independently of the trainable encoding and are updated throughout training based on the updated density predictions. The authors find they can increase sampling speed by 10-100x compared to naive approaches. - -Nerfstudio uses [NerfAcc](https://www.nerfacc.com/index.html) as the sampling algorithm implementation. The details on NerfAcc's sampling and occupancy grid is discussed [here](https://www.nerfacc.com/en/stable/methodology/sampling.html#occupancy-grid-estimator). - -Another major bottleneck for NeRF's training speed has been querying the neural network. The authors of this work implement the network such that it runs entirely on a single CUDA kernel. The network is also shrunk down to be just 4 layers with 64 neurons in each layer. They show that their fully-fused neural network is 5-10x faster than a Tensorflow implementation. - -Nerfstudio uses the [tinycudann](https://github.com/NVlabs/tiny-cuda-nn) framework to utilize the fully-fused neural networks. - -The speedups at each level are multiplicative. With all their improvements, Instant-NGP reaches speedups of 1000x, which enable training NeRF scenes in a matter of seconds! - -### Multi-Resolution Hash Encoding - -```{image} imgs/ingp/hash_figure.png -:align: center -``` - -One contribution of Instant-NGP is the multi-resolution hash encoding. In the traditional NeRF pipelines, input coordinates are mapped to a higher dimensional space using a positional encoding function, which is described [here](../model_components/visualize_encoders.ipynb). Instant-NGP proposes a trainable hash-based encoding. The idea is to map coordinates to trainable feature vectors which can be optimized in the standard flow of NeRF training. - -The trainable features are F-dimensional vectors and are arranged into L grids which contain up to T vectors, where L represents the number of resolutions for features and T represents the number of feature vectors in each hash grid. The steps for the hash grid encoding, as shown in the figure provided by the authors, are as follows: - -1. Given an input coordinate, find the surrounding voxels at L resolution levels and hash the vertices of these grids. -2. The hashed vertices are used as keys to look up trainable F-dimensional feature vectors. -3. Based on where the coordinate lies in space, the feature vectors are linearly interpolated to match the input coordinate. -4. The feature vectors from each grid are concatenated, along with any other parameters such as viewing direction, -5. The final vector is inputted into the neural network to predict the RGB and density output. - -Steps 1-3 are done independently at each resolution level. Thus, since these feature vectors are trainable, when backpropagating the loss gradient, the gradients will flow through the neural network and interpolation function all the way back to the feature vectors. The feature vectors are interpolated relative to the coordinate such that the network can learn a smooth function. - -An important note is that hash collisions are not explicitly handled. At each hash index, there may be multiple vertices which index to that feature vector, but because these vectors are trainable, the vertices that are most important to the specific output will have the highest gradient, and therefore automatically dominate the optimization of that feature. - -This encoding structure creates a tradeoff between quality, memory, and performance. The main parameters which can be adjusted are the size of the hash table (T), the size of the feature vectors (F), and the number of resolutions (L). - -Instant-NGP encodes the viewing direction using spherical harmonic encodings. - -Our [`nerfacto`](./nerfacto.md) model uses both the fully-fused MLP and the hash encoder, which were inspired by Instant-NGP. Lastly, our implementation covers the major ideas from Instant-NGP, but it doesn't strictly follow every detail. Some known differences include learning rate schedulers, hyper-parameters for sampling, and how camera gradients are calculated if enabled. diff --git a/docs/nerfology/methods/kplanes.md b/docs/nerfology/methods/kplanes.md deleted file mode 100644 index 65c6c104..00000000 --- a/docs/nerfology/methods/kplanes.md +++ /dev/null @@ -1,87 +0,0 @@ -# K-Planes - -

Explicit Radiance Fields in Space, Time, and Appearance

- - -```{button-link} https://sarafridov.github.io/K-Planes/ -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/sarafridov/K-Plane -:color: primary -:outline: -Official Code -``` - -```{button-link} https://github.com/Giodiro/kplanes_nerfstudio -:color: primary -:outline: -Nerfstudio add-on code -``` - - - -**A unified model for static, dynamic and variable appearance NeRFs.** - - -## Installation - -First, install nerfstudio and its dependencies. Then install the K-Planes add-on -``` -pip install kplanes-nerfstudio -``` - -## Running K-Planes - -There are two default configurations provided which use the blender and DNeRF dataloaders. However, you can easily extend them to create a new configuration for different datasets. - -The default configurations provided are -| Method | Description | Scene type | Memory | -| ----------------- | -------------------------| ------------------------------ | ------ | -| `kplanes` | Tuned for blender scenes | static, synthetic | ~4GB | -| `kplanes-dynamic` | Tuned for DNeRF dataset | dynamic (monocular), synthetic | ~5GB | - - -for training with these two configurations you should run -```bash -ns-train kplanes --data -``` -or -```bash -ns-train kplanes-dynamic --data -``` - -:::{admonition} Note -:class: warning - -`kplanes` is set up to use blender data, (download it running `ns-download-data blender`), -`kplanes-dynamic` is set up to use DNeRF data, (download it running `ns-download-data dnerf`). -::: - - -## Method - -![method overview](https://sarafridov.github.io/K-Planes/assets/intro_figure.jpg)
-K-planes represents a scene in k dimensions -- where k can be 3 for static 3-dimensional scenes or 4 for scenes which change in time -- -using k-choose-2 planes (or grids). After ray-sampling, the querying the field at a certain position consists in querying each plane (with interpolation), and combining the resulting features through multiplication. -This factorization of space and time keeps memory usage low, and is very flexible in the kinds of priors and regularizers that can be added.
-
- -We support hybrid models with a small MLP (left) and fully explicit models (right), through the `linear_decoder` [configuration key](https://github.com/Giodiro/kplanes_nerfstudio/blob/db4130605159dadaf180228be5d0335d2c4d21f9/kplanes/kplanes.py#L87) -
- - - -The model also supports decomposing a scene into its space and time components. For more information on how to do this see the [official code repo](https://github.com/sarafridov/K-Plane) -
- diff --git a/docs/nerfology/methods/lerf.md b/docs/nerfology/methods/lerf.md deleted file mode 100644 index 509c88fa..00000000 --- a/docs/nerfology/methods/lerf.md +++ /dev/null @@ -1,91 +0,0 @@ -# LERF - -

πŸ“Ž Language Embedded Radiance Fields 🚜

- -```{button-link} https://www.lerf.io/ -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/kerrj/lerf -:color: primary -:outline: -Code -``` - - - -**Grounding CLIP vectors volumetrically inside NeRF allows flexible natural language queries in 3D** - -## Installation - -First install nerfstudio dependencies. Then run: - -```bash -pip install git+https://github.com/kerrj/lerf -``` - -## Running LERF - -Details for running LERF (built with Nerfstudio!) can be found [here](https://github.com/kerrj/lerf). Once installed, run: - -```bash -ns-train lerf --help -``` - -Three variants of LERF are provided: - -| Method | Description | Memory | Quality | -| ----------- | ----------------------------------------------- | ------ | ------- | -| `lerf-big` | LERF with OpenCLIP ViT-L/14 | ~22 GB | Best | -| `lerf` | Model with OpenCLIP ViT-B/16, used in paper | ~15 GB | Good | -| `lerf-lite` | LERF with smaller network and less LERF samples | ~8 GB | Ok | - -`lerf-lite` should work on a single NVIDIA 2080. -`lerf-big` is experimental, and needs further tuning. - -## Method - -LERF enables pixel-aligned queries of the distilled 3D CLIP embeddings without relying on region proposals, masks, or fine-tuning, supporting long-tail open-vocabulary queries hierarchically across the volume. - -### Multi-scale supervision - -To supervise language embeddings, we pre-compute an image pyramid of CLIP features for each training view. Then, each sampled ray during optimization is supervised by interpolating the CLIP embedding within this pyramid. - - - -### LERF Optimization - -LERF optimizes a dense, multi-scale language 3D field by volume rendering CLIP embeddings along training rays, supervising these embeddings with multi-scale CLIP features across multi-view training images. - -Inspired by Distilled Feature Fields (DFF), we use DINO features to regularize CLIP features. This leads to qualitative improvements in object boundaries, as CLIP embeddings in 3D can be sensitive to floaters and regions with few views. - -After optimization, LERF can extract 3D relevancy maps for language queries interactively in real-time. - - - -### Visualizing relevancy for text queries - -Set the "Output Render" type to `relevancy_0`, and enter the text query in the "LERF Positives" textbox (see image). The output render will show the 3D relevancy map for the query. View the [project page](https://lerf.io) for more examples and details about the relevancy map normalization. - -
- -
- -## Results - -For results, view the [project page](https://lerf.io)! - -Datasets used in the original paper can be found [here](https://drive.google.com/drive/folders/1LUzwEvBCE19PNYcwfmrG-9FLpZLbi4on?usp=sharing). - -```none -@article{lerf2023, - author = {Kerr, Justin and Kim, Chung Min and Goldberg, Ken and Kanazawa, Angjoo and Tancik, Matthew}, - title = {LERF: Language Embedded Radiance Fields}, - journal = {arXiv preprint arXiv:2303.09553}, - year = {2023}, -} -``` diff --git a/docs/nerfology/methods/mipnerf.md b/docs/nerfology/methods/mipnerf.md deleted file mode 100644 index 58ba4a2a..00000000 --- a/docs/nerfology/methods/mipnerf.md +++ /dev/null @@ -1,43 +0,0 @@ -# Mip-NeRF - -

A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

- -```{button-link} https://jonbarron.info/mipnerf/ -:color: primary -:outline: -Paper Website -``` - -### Running Model - -```bash -ns-train mipnerf -``` - -## Overview - -```{image} imgs/mipnerf/models_mipnerf_pipeline-light.png -:align: center -:class: only-light -``` - -```{image} imgs/mipnerf/models_mipnerf_pipeline-dark.png -:align: center -:class: only-dark -``` - -The primary modification in MipNeRF is in the encoding for the field representation. With the modification the same _mip-NeRF_ field can be use for the coarse and fine steps of the rendering hierarchy. - -```{image} imgs/mipnerf/models_mipnerf_field-light.png -:align: center -:class: only-light -:width: 400 -``` - -```{image} imgs/mipnerf/models_mipnerf_field-dark.png -:align: center -:class: only-dark -:width: 400 -``` - -In the field, the Positional Encoding (PE) is replaced with an Integrated Positional Encoding (IPE) that takes into account the size of the sample. diff --git a/docs/nerfology/methods/nerf.md b/docs/nerfology/methods/nerf.md deleted file mode 100644 index bf79592e..00000000 --- a/docs/nerfology/methods/nerf.md +++ /dev/null @@ -1,178 +0,0 @@ -# NeRF - -

Neural Radiance Fields

- -```{button-link} https://www.matthewtancik.com/nerf -:color: primary -:outline: -Paper Website -``` - -### Running the model - -```bash -ns-train vanilla-nerf -``` - -## Method overview - -If you have arrived to this site, it is likely that you have at least heard of NeRFs. This page will discuss the original NeRF paper, _"NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"_ by Mildenhall, Srinivasan, Tancik et al. (2020). - -For most tasks, using the original NeRF model is likely not a good choice and hence we provide implementations of various other NeRF related models. It is however useful to understand how NeRF's work as most follow ups follow a similar structure and it doesn't require CUDA to execute (useful for stepping through the code with a debugger if you don't have a GPU at hand). - -The goal is to optimize a volumetric representation of a scene that can be rendered from novel viewpoints. This representation is optimized from a set of images and associated camera poses. - -```{admonition} Assumptions -If any of the following assumptions are broken, the reconstructions may fail completely or contain artifacts such as excess geometry. -* Camera poses are known -* Scene is static, objects do not move -* The scene appearance is constant (ie. exposure doesn't change) -* Dense input capture (Each point in the scene should be visible in multiple images) -``` - -## Pipeline - -```{image} imgs/models_nerf-pipeline-light.png -:align: center -:class: only-light -``` - -```{image} imgs/models_nerf-pipeline-dark.png -:align: center -:class: only-dark -``` - -Here is an overview pipeline for NeRF, we will walk through each component in this guide. - -### Field representation - -```{image} imgs/models_nerf-pipeline-field-light.png -:align: center -:class: only-light -``` - -```{image} imgs/models_nerf-pipeline-field-dark.png -:align: center -:class: only-dark -``` - -NeRFs are a volumetric representation encoded into a neural network. They are not 3D meshes and they are not voxels. **For each point in space the NeRF represents a view dependent radiance.** More concretely each point has a density which describes how transparent or opaque a point in space is. They also have a view dependent color that changes depending on the angle the point is viewed. - -```{image} imgs/models_nerf-field-light.png -:align: center -:class: only-light -:width: 400 -``` - -```{image} imgs/models_nerf-field-dark.png -:align: center -:class: only-dark -:width: 400 -``` - -The associated NeRF fields can be instantiated with the following nerfstudio code (encoding described in next section): - -```python -from nerfstudio.fields.vanilla_nerf_field import NeRFField - -field_coarse = NeRFField(position_encoding=pos_enc, direction_encoding=dir_enc) -field_fine = NeRFField(position_encoding=pos_enc, direction_encoding=dir_enc) -``` - -#### Positional encoding - -An extra trick is necessary to make the neural network expressive enough to represent fine details in the scene. The input coordinates $(x,y,z,\theta,\phi)$ need to be encoded to a higher dimensional space prior to being input into the network. You can learn more about encodings [here](../model_components/visualize_encoders.ipynb). - -```python -from nerfstudio.field_components.encodings import NeRFEncoding - -pos_enc = NeRFEncoding( - in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True -) -dir_enc = NeRFEncoding( - in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True -) -``` - -### Rendering - -```{image} imgs/models_nerf-pipeline-renderer-light.png -:align: center -:class: only-light -``` - -```{image} imgs/models_nerf-pipeline-renderer-dark.png -:align: center -:class: only-dark -``` - -Now that we have a representation of space, we need some way to render new images of it. To accomplish this, we are going to _project_ a ray from the target pixel and evaluate points along that ray. We then rely on classic volumetric rendering techniques [[Kajiya, 1984]](https://dl.acm.org/doi/abs/10.1145/964965.808594) to composite the points into a predicted color. - -This compositing is similar to what happens in tools like Photoshop when you layer multiple objects of varying opacity on top of each other. The only difference is that NeRF takes into account the differences in spacing between points. - -Rending RGB images is not the only type of output render supported. It is possible to render other output types such as depth and semantics. Additional renderers can be found [Here](../../reference/api/model_components/renderers.rst). - -Associated nerfstudio code: - -```python -from nerfstudio.model_components.renderers import RGBRenderer - -renderer_rgb = RGBRenderer(background_color=colors.WHITE) -# Ray samples discussed in the next section -field_outputs = field_coarse.forward(ray_samples) -weights = ray_samples.get_weights(field_outputs[FieldHeadNames.DENSITY]) -rgb = renderer_rgb( - rgb=field_outputs[FieldHeadNames.RGB], - weights=weights, -) -``` - -#### Sampling - -```{image} imgs/models_nerf-pipeline-sampler-light.png -:align: center -:class: only-light -``` - -```{image} imgs/models_nerf-pipeline-sampler-dark.png -:align: center -:class: only-dark -``` - -How we sample points along rays in space is an important design decision. Various sampling strategies can be used which are discussed in detail in the [Ray Samplers](../model_components/visualize_samplers.ipynb) guide. In NeRF we take advantage of a hierarchical sampling scheme that first uses a _uniform sampler_ and is followed by a _PDF sampler_. - -The uniform sampler distributes samples evenly between a predefined distance range from the camera. These are then used to compute an initial render of the scene. The renderer optionally produces _weights_ for each sample that correlate with how important each sample was to the final renderer. - -The PDF sampler uses these _weights_ to generate a new set of samples that are biased to regions of higher weight. In practice, these regions are near the surface of the object. - -Associated code: - -```python -from nerfstudio.model_components.ray_samplers import PDFSampler, UniformSampler - -sampler_uniform = UniformSampler(num_samples=num_coarse_samples) -ray_samples_uniform = sampler_uniform(ray_bundle) - -sampler_pdf = PDFSampler(num_samples=num_importance_samples) -field_outputs_coarse = field_coarse.forward(ray_samples_uniform) -weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse[FieldHeadNames.DENSITY]) -ray_samples_pdf = sampler_pdf(ray_bundle, ray_samples_uniform, weights_coarse) -``` - -```{warning} -Described above is specific to scenes that have known bounds (ie. the Blender Synthetic dataset). For unbounded scenes, the original NeRF paper uses Normalized Device Coordinates (NDC) to warp space, along with a _linear in disparity_ sampler. We do not support NDC, for unbounded scenes consider using [Spatial Distortions](../model_components/visualize_spatial_distortions.ipynb). -``` - -```{tip} -For all sampling, we use _Stratified_ samples during optimization and unmodified samples during inference. Further details can be found in the [Ray Samplers](../model_components/visualize_samplers.ipynb) guide. -``` - -## Benchmarks - -##### Blender synthetic - -| Implementation | Mic | Ficus | Chair | Hotdog | Materials | Drums | Ship | Lego | Average | -| --------------------------------------------------------------------------------- | :-------: | --------- | :-------: | --------- | --------- | --------- | --------- | --------- | --------- | -| nerfstudio | 33.76 | **31.98** | **34.35** | 36.57 | **31.00** | **25.11** | 29.87 | **34.46** | **32.14** | -| [TF NeRF](https://github.com/bmild/nerf) | 32.91 | 30.13 | 33.00 | 36.18 | 29.62 | 25.01 | 28.65 | 32.54 | 31.04 | -| [JaxNeRF](https://github.com/google-research/google-research/tree/master/jaxnerf) | **34.53** | 30.43 | 34.08 | **36.92** | 29.91 | 25.03 | **29.36** | 33.28 | 31.69 | diff --git a/docs/nerfology/methods/nerfacto.md b/docs/nerfology/methods/nerfacto.md deleted file mode 100644 index e835176d..00000000 --- a/docs/nerfology/methods/nerfacto.md +++ /dev/null @@ -1,87 +0,0 @@ -# Nerfacto - -

Our *defacto* method.

- -### Running the Method - -```bash -ns-train nerfacto --help -``` - -We provide a few additional variants: - -| Method | Description | Memory | Speed | -| ---------------- | ------------------------------ | ------ | ------- | -| `nerfacto` | Default Model | ~6GB | Fast | -| `nerfacto-big` | Larger higher quality | ~12GB | Slower | -| `nerfacto-huge` | Even larger and higher quality | ~24GB | Slowest | -| `depth-nerfacto` | Supervise on depth | ~6GB | Fast | - -## Method - -### Overview - -We created the nerfacto model to act as our default for real data captures of static scenes. The model is not existing published work, but rather a combination of many published methods that we have found work well for real data. This guide discusses the details of the model, understanding the [NeRF model](./nerf.md) is a prerequisite. - -```{admonition} TLDR -We combine the following techniques in this model: -* Camera pose refinement -* Per image appearance conditioning -* Proposal sampling -* Scene contraction -* Hash encoding -``` - -```{warning} -πŸ—οΈ This guide is under construction πŸ—οΈ -``` - -## Pipeline - -```{image} imgs/nerfacto/models_nerfacto_pipeline-light.png -:align: center -:class: only-light -``` - -```{image} imgs/nerfacto/models_nerfacto_pipeline-dark.png -:align: center -:class: only-dark -``` - -Here is an overview pipeline for nerfacto, we will walk through each component in this guide. - -### Pose Refinement - -It is not uncommon to have errors in the predicted camera poses. This is even more of a factor when using poses acquired from devices such as phones (ie. if you use the Record3D IOS app to capture data). Misaligned poses result in both cloudy artifacts in the scene and a reduction of sharpness and details. The NeRF framework allows us to backpropagate loss gradients to the input pose calculations. We can use this information to optimize and refine the poses. - -### Piecewise Sampler - -We use a Piecewise sampler to produce the initial set of samples of the scene. This sampler allocates half of the samples uniformly up to a distance of 1 from the camera. The remaining samples are distributed such that the step size increases with each sample. The step size is chosen such that the [frustums](../model_components/visualize_samples.ipynb) are scaled versions of themselves. By increasing the step sizes, we are able to sample distant objects while still having a dense set of samples for nearby objects. - -### Proposal Sampler - -The proposal sampler consolidates the sample locations to the regions of the scene that contribute most to the final render (typically the first surface intersection). This greatly improves reconstruction quality. The proposal network sampler requires a density function for the scene. The density function can be implemented in a variety of ways, we find that using a small fused-mlp with a hash encoding has sufficient accuracy and is fast. The proposal network sampler can be chained together with multiple density functions to further consolidate the sampling. We have found that two density functions are better than one. Larger than 2 leads to diminishing returns. - -#### Density Field - -The density field only needs to represent a coarse density representation of the scene to guide sampling. Combining a hash encoding with a small fused MLP (from [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn)) provides a fast way to query the scene. We can make it more efficient by decreasing the encoding dictionary size and number of feature levels. These simplifications have little impact on the reconstruction quality because the density function does not need to learn high frequency details during the initial passes. - -### Nerfacto Field - -```{image} imgs/nerfacto/models_nerfacto_field-light.png -:align: center -:class: only-light -:width: 600 -``` - -```{image} imgs/nerfacto/models_nerfacto_field-dark.png -:align: center -:class: only-dark -:width: 600 -``` - -```{button-link} https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/models/nerfacto.py -:color: primary -:outline: -See the code! -``` diff --git a/docs/nerfology/methods/nerfbusters.md b/docs/nerfology/methods/nerfbusters.md deleted file mode 100644 index c75baab9..00000000 --- a/docs/nerfology/methods/nerfbusters.md +++ /dev/null @@ -1,66 +0,0 @@ -# Nerfbusters - -

Removing Ghostly Artifacts from Casually Captured NeRFs πŸ‘»

- -```{button-link} https://ethanweber.me/nerfbusters -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/ethanweber/nerfbusters -:color: primary -:outline: -Code and Data -``` - -**TLDR: We present a method that uses a 3D diffusion prior to clean NeRFs and an evaluation procedure for in-the-wild NeRFs** - - - -## Installation - -First install nerfstudio dependencies. Then run: - -```bash -pip install git+https://github.com/ethanweber/nerfbusters -nerfbusters-setup -ns-train nerfbusters --help -``` - -For more details, see the [installation instructions](https://github.com/ethanweber/nerfbusters). - -### Running the Method - -Please checkout the readme for the [Nerfbusters repository](https://github.com/ethanweber/nerfbusters) - -## Abstract - -Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the path of the training views. However, common practice is still to evaluate on every 8th frame, which does not measure rendering quality away from training views, hindering progress in volume rendering. We propose a new dataset and evaluation procedure, where two camera trajectories are recorded of the scene, one used for training, and the other for evaluation. We find that existing hand-crafted regularizers do not remove floaters nor improve scene geometry in this more challenging in-the-wild setting. To this end, we propose a learned, local 3D diffusion prior and a novel density score distillation sampling loss. We show that this learned prior removes floaters and improves scene geometry for casual captures. - -## NeRF evaluation for casually captured videos - -We train on a single camera trajectory and evaluate on a second camera trajectory. Both methods succeed on the training trajectory, but only our method succeeds on the evaluation trajectory. - - - -### Model Overview - -We learn a local 3D prior with a diffusion model that regularizes the 3D geometry of NeRFs. We use importance sampling to query a cube with NeRF densities. We binarize these densities and perform one single denoising step using a pre-trained 3D diffusion model. With these denoised densities, we compute a density score distillation sampling (DSDS) that penalizes NeRF densities where the diffusion model predicts empty voxels and pushes the NeRF densities above the target w where the diffusion model predicts occupied voxels. - -![Overview](https://ethanweber.me/nerfbusters/media/method.png) - - -### Visibiltiy Loss - -Our visibility loss enables stepping behind or outside the training camera frustums. We accomplish this by supervising densities to be low when not seen by at least one training view. Other solutions would be to store an occupancy grid or compute ray-frustum intersection tests during rendering. Our solution is easy to implement and applicable to any NeRF. - -![Visibility](https://ethanweber.me/nerfbusters/media/visibility_loss.png) - -### Results and dataset preview - -For results and a dataset preview, view the [project page](https://ethanweber.me/nerfbusters)! diff --git a/docs/nerfology/methods/nerfplayer.md b/docs/nerfology/methods/nerfplayer.md deleted file mode 100644 index dafe11c5..00000000 --- a/docs/nerfology/methods/nerfplayer.md +++ /dev/null @@ -1,50 +0,0 @@ -# NeRFPlayer - -

A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields

- - -```{button-link} https://lsongx.github.io/projects/nerfplayer.html -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/lsongx/nerfplayer-nerfstudio -:color: primary -:outline: -Nerfstudio add-on code -``` - -[![NeRFPlayer Video](https://img.youtube.com/vi/flVqSLZWBMI/0.jpg)](https://www.youtube.com/watch?v=flVqSLZWBMI) - - -## Installation - -First install nerfstudio dependencies. Then run: - -```bash -pip install git+https://github.com/lsongx/nerfplayer-nerfstudio.git -``` - -## Running NeRFPlayer - -Details for running NeRFPlayer can be found [here](https://github.com/lsongx/nerfplayer-nerfstudio). Once installed, run: - -```bash -ns-train nerfplayer-ngp --help -``` - -Two variants of NeRFPlayer are provided: - -| Method | Description | -| --------------------- | ----------------------------------------------- | -| `nerfplayer-nerfacto` | NeRFPlayer with nerfacto backbone | -| `nerfplayer-ngp` | NeRFPlayer with instant-ngp-bounded backbone | - - -## Method Overview - -![method overview](https://lsongx.github.io/projects/images/nerfplayer-framework.png)
-First, we propose to decompose the 4D spatiotemporal space according to temporal characteristics. Points in the 4D space are associated with probabilities of belonging to three categories: static, deforming, and new areas. Each area is represented and regularized by a separate neural field. Second, we propose a hybrid representations based feature streaming scheme for efficiently modeling the neural fields. - -Please see [TODO lists](https://github.com/lsongx/nerfplayer-nerfstudio#known-todos) to see the unimplemented components in the nerfstudio based version. \ No newline at end of file diff --git a/docs/nerfology/methods/pynerf.md b/docs/nerfology/methods/pynerf.md deleted file mode 100644 index 31704b6d..00000000 --- a/docs/nerfology/methods/pynerf.md +++ /dev/null @@ -1,92 +0,0 @@ -# PyNeRF - -

Pyramidal Neural Radiance Fields

- - -```{button-link} https://haithemturki.com/pynerf/ -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/hturki/pynerf -:color: primary -:outline: -Code -``` - - - -**A fast NeRF anti-aliasing strategy.** - - -## Installation - -First, install Nerfstudio and its dependencies. Then install the PyNeRF extension and [torch-scatter](https://github.com/rusty1s/pytorch_scatter): -``` -pip install git+https://github.com/hturki/pynerf -pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH_VERSION}+${CUDA}.html -``` - -## Running PyNeRF - -There are three default configurations provided which use the MipNeRF 360 and Multicam dataparsers by default. You can easily use other dataparsers via the ``ns-train`` command (ie: ``ns-train pynerf nerfstudio-data --data `` to use the Nerfstudio data parser) - -The default configurations provided are: - -| Method | Description | Scene type | Memory | -| ----------------------- |---------------------------------------------------| ------------------------------ |--------| -| `pynerf ` | Tuned for outdoor scenes, uses proposal network | outdoors | ~5GB | -| `pynerf-synthetic` | Tuned for synthetic scenes, uses proposal network | synthetic | ~5GB | -| `pynerf-occupancy-grid` | Tuned for Multiscale blender, uses occupancy grid | synthetic | ~5GB | - - -The main differences between them is whether they are suited for synthetic/indoor or real-world unbounded scenes (in case case appearance embeddings and scene contraction are enabled), and whether sampling is done with a proposal network (usually better for real-world scenes) or an occupancy grid (usally better for single-object synthetic scenes like Blender). - -## Method - -Most NeRF methods assume that training and test-time cameras capture scene content from a roughly constant distance: - - - - - - - - - -
-
- -
- 		- -
- -They degrade and render blurry views in less constrained settings: - - - - - - - - - -
-
- -
- 		- -
- -This is due to NeRF being scale-unaware, as it reasons about point samples instead of volumes. We address this by training a pyramid of NeRFs that divide the scene at different resolutions. We use "coarse" NeRFs for far-away samples, and finer NeRF for close-up samples: - - \ No newline at end of file diff --git a/docs/nerfology/methods/seathru_nerf.md b/docs/nerfology/methods/seathru_nerf.md deleted file mode 100644 index a5b3b590..00000000 --- a/docs/nerfology/methods/seathru_nerf.md +++ /dev/null @@ -1,105 +0,0 @@ -# SeaThru-NeRF -```{button-link} https://sea-thru-nerf.github.io -:color: primary -:outline: -Official Paper Website -``` - -```{button-link} https://github.com/AkerBP/seathru_nerf -:color: primary -:outline: -Code (nerfstudio implementation) -``` - -

- Example Render -

- -**A Neural Radiance Field for subsea scenes.** - -## Requirements - -We provide the following two model configurations: - -| Method | Description | Memory | Quality | -| ------------------- | ------------- | ------ | ------- | -| `seathru-nerf` | Larger model | ~23 GB | Best | -| `seathru-nerf-lite` | Smaller model | ~7 GB | Good | - -`seathru-nerf-lite` should run on a single desktop/laptop GPU with 8GB VRAM. - -## Installation - -After installing nerfstudio and its dependencies, run: - -```bash -pip install git+https://github.com/AkerBP/seathru_nerf -``` - -## Running SeaThru-NeRF - -To check your installation and to see the hyperparameters of the method, run: - -```bash -ns-train seathru-nerf-lite --help -``` - -If you see the help message with all the training options, you are good to go and ready to train your first susbea NeRF! πŸš€πŸš€πŸš€ - -For a detailed tutorial of a training process, please see the docs provided [here](https://akerbp.github.io/seathru_nerf/). - - -## Method -This method is an unofficial extension that adapts the official [SeaThru-NeRF](https://sea-thru-nerf.github.io) publication. Since it is built ontop of nerfstudio, it allows for easy modification and experimentation. - -Compared to a classical NeRF approach, we differentiate between solid objects and the medium within a scene. Therefore both, the object colours and the medium colours of samples along a ray contribute towards the final pixel colour as follows: - -$$\boldsymbol{\hat{C}}(\mathbf{r})=\sum_{i=1}^N \boldsymbol{\hat{C}}^{\rm obj}_i(\mathbf{r})+\sum_{i=1}^N \boldsymbol{\hat{C}}^{\rm med}_i(\mathbf{r}) \,.$$ - -Those two contributions can be calculated as follows: - -$$\boldsymbol{\hat{C}}^{\rm obj}_i(\mathbf{r}) = - T^{\rm obj}_i \cdot \exp (-\boldsymbol{\sigma}^{\rm attn} t_i) - \cdot \big(1-\exp({-\sigma^{\rm obj}_i\delta_i})\big) \cdot \mathbf{c}^{\rm obj}_i \,,$$ -$$\boldsymbol{\hat{C}}^{\rm med}_i(\mathbf{r}) = - T^{\rm obj}_i \cdot \exp ( -\boldsymbol{\sigma}^{\rm bs} t_i ) - \cdot \big( 1 - \exp ( -\boldsymbol{\sigma}^{\rm bs} \delta_i ) \big) \cdot \mathbf{c}^{\rm med}\,,$$ -$$\textrm{where } \ T^{\rm obj}_i = \exp\bigg(-\sum_{j=0}^{i-1}\sigma^{\rm obj}_j\delta_j\bigg) \,. $$ - -The above equations contain five parameters that are used to describe the underlying scene: object density $\sigma^{\rm obj}_i \in \mathbb{R}^{1}$, object colour $\mathbf{c}^{obj}_i \in \mathbb{R}^{3}$, backscatter density $\boldsymbol{\sigma}^{\rm bs} \in \mathbb{R}^{3}$, attenuation density $\boldsymbol{\sigma}^{\rm attn} \in \mathbb{R}^{3}$ and medium colour $\mathbf{c}^{\rm med} \in \mathbb{R}^{3}$. - -To get a better idea of the different densities, the following figure shows an example ray with the different densities visualised: - -

-SeaThru-NeRF ray -

- -*The image above was taken from [Levy et al. (2023)](https://arxiv.org/abs/2304.07743).* - -To predict the object and medium parameters, we use two separate networks. This subsea specific approach can be visualised as follows: (note that the third network is the proposal network, which is used to sample points in regions that contribute most to the final image) - -

-SeaThru-NeRF Architecture -

- -If you are interested in an in depth description of the method, make sure to check out the documentation [here](https://akerbp.github.io/seathru_nerf/intro.html). - - -## Example results -Due to the underlying image formation model that allows us to seperate between the objects and the water within a scene, you can choose between different rendering options. The following options exist: - -- rgb: To render normal RGB of the scene. -- J: To render the clear scene (water effect removed). -- direct: To render the attenuated clear scene. -- bs: To render backscatter of the water within the scene. -- depth: To render depthmaps of the scene. -- accumulation: To render object weight accumulation of the scene. - -Below, you can see an original render of a scene and one with the water effects removed: - -

-RBG rendering -J rendering -

- -*Please note that those gifs are compressed and do not do the approach justice. Please render your own videos to see the level of detail and clarity of the renders.* \ No newline at end of file diff --git a/docs/nerfology/methods/splat.md b/docs/nerfology/methods/splat.md deleted file mode 100644 index 69303f35..00000000 --- a/docs/nerfology/methods/splat.md +++ /dev/null @@ -1,69 +0,0 @@ -# Splatfacto -

Nerfstudio's Gaussian Splatting Implementation

- - -```{button-link} https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ -:color: primary -:outline: -Paper Website -``` - -[3D Gaussian Splatting](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) was proposed in SIGGRAPH 2023 from INRIA, and is a completely different method of representing radiance fields by explicitly storing a collection of 3D volumetric gaussians. These can be "splatted", or projected, onto a 2D image provided a camera pose, and rasterized to obtain per-pixel colors. Because rasterization is very fast on GPUs, this method can render much faster than neural representations of radiance fields. - -To avoid confusion with the original paper, we refer to nerfstudio's implementation as "Splatfacto", which will drift away from the original as more features are added. Just as Nerfacto is a blend of various different methods, Splatfacto will be a blend of different gaussian splatting methodologies. - -### Installation - -```{button-link} https://docs.gsplat.studio/ -:color: primary -:outline: -GSplat -``` - -Nerfstudio uses [gsplat](https://github.com/nerfstudio-project/gsplat) as its gaussian rasterization backend, an in-house re-implementation which is designed to be more developer friendly. This can be installed with `pip install gsplat`. The associated CUDA code will be compiled the first time gsplat is executed. Some users with PyTorch 2.0 have experienced issues with this, which can be resolved by either installing gsplat from source, or upgrading torch to 2.1. - -### Data -Gaussian splatting works much better if you initialize it from pre-existing geometry, such as SfM points from COLMAP. COLMAP datasets or datasets from `ns-process-data` will automatically save these points and initialize gaussians on them. Other datasets currently do not support initialization, and will initialize gaussians randomly. Initializing from other data inputs (i.e. depth from phone app scanners) may be supported in the future. - -Because the method trains on *full images* instead of bundles of rays, there is a new datamanager in `full_images_datamanager.py` which undistorts input images, caches them, and provides single images at each train step. - - -### Running the Method -To run splatfacto, run `ns-train splatfacto --data `. Just like NeRF methods, the splat can be interactively viewed in the web-viewer, loaded from a checkpoint, rendered, and exported. - -We provide a few additional variants: - -| Method | Description | Memory | Speed | -| ---------------- | ------------------------------ | ------ | ------- | -| `splatfacto` | Default Model | ~6GB | Fast | -| `splatfacto-big` | More Gaussians, Higher Quality | ~12GB | Slower | - - -A full evalaution of Nerfstudio's implementation of Gaussian Splatting against the original Inria method can be found [here](https://docs.gsplat.studio/tests/eval.html). - -#### Quality and Regularization -The default settings provided maintain a balance between speed, quality, and splat file size, but if you care more about quality than training speed or size, you can decrease the alpha cull threshold -(threshold to delete translucent gaussians) and disable culling after 15k steps like so: `ns-train splatfacto --pipeline.model.cull_alpha_thresh=0.005 --pipeline.model.continue_cull_post_densification=False --data ` - -A common artifact in splatting is long, spikey gaussians. [PhysGaussian](https://xpandora.github.io/PhysGaussian/) proposes a scale regularizer that encourages gaussians to be more evenly shaped. To enable this, set the `use_scale_regularization` flag to `True`. - -### Details -For more details on the method, see the [original paper](https://arxiv.org/abs/2308.04079). Additionally, for a detailed derivation of the gradients used in the gsplat library, see [here](https://arxiv.org/abs/2312.02121). - -### Exporting splats -Gaussian splats can be exported as a `.ply` file which are ingestable by a variety of online web viewers. You can do this via the viewer, or `ns-export gaussian-splat --load-config --output-dir exports/splat`. Currently splats can only be exported from trained splats, not from nerfacto. - -Nerfstudio's splat export currently supports multiple third-party splat viewers: -- [Polycam Viewer](https://poly.cam/tools/gaussian-splatting) -- [Playcanvas SuperSplat](https://playcanvas.com/super-splat) -- [WebGL Viewer by antimatter15](https://antimatter15.com/splat/) -- [Spline](https://spline.design/) -- [Three.js Viewer by mkkellogg](https://github.com/mkkellogg/GaussianSplats3D) - -### FAQ -- Can I export a mesh or pointcloud? - -Currently these export options are not supported, but may be in the future. Contributions are always welcome! -- Can I render fisheye, equirectangular, orthographic images? - -Currently, no. Gaussian rasterization assumes a perspective camera for its rasterization pipeline. Implementing other camera models is of interest but not currently planned. diff --git a/docs/nerfology/methods/tensorf.md b/docs/nerfology/methods/tensorf.md deleted file mode 100644 index e418f524..00000000 --- a/docs/nerfology/methods/tensorf.md +++ /dev/null @@ -1,33 +0,0 @@ -# TensoRF - -

Tensorial Radiance Fields

- -```{button-link} https://apchenstu.github.io/TensoRF/ -:color: primary -:outline: -Paper Website -``` - - - -### Running Model - -```bash -ns-train tensorf -``` - -## Overview - -```{image} imgs/tensorf/models_tensorf_pipeline.png -:align: center -``` - -TensoRF models the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features. TensoRF factorizes the 4D scene tensor into multiple compact low-rank tensor components using CP or VM modes. CP decomposition factorizes tensors into rank-one components with compact vectors. Vector-Matrix (VM) decomposition factorizes tensors into compact vector and matrix factors. - -```{image} imgs/tensorf/models_tensorf_factorization.png -:align: center -``` - -TensoRF with CP(left) and VM(right) decompositions results in a significantly reduced memory footprint compared to previous and concurrent works that directly optimize per-voxel features, such as [Plenoxels](https://alexyu.net/plenoxels/) and [PlenOctrees](https://alexyu.net/plenoctrees/). In experiments, TensoRF with CP decomposition achieves fast reconstruction with improved rendering quality and a smaller model size compared to NeRF. Furthermore, TensoRF with VM decomposition enhances rendering quality even further, while reducing reconstruction time and maintaining a compact model size. diff --git a/docs/nerfology/methods/tetranerf.md b/docs/nerfology/methods/tetranerf.md deleted file mode 100644 index e1816308..00000000 --- a/docs/nerfology/methods/tetranerf.md +++ /dev/null @@ -1,77 +0,0 @@ -# Tetra-NeRF - -

Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra

- -```{button-link} https://jkulhanek.com/tetra-nerf -:color: primary -:outline: -Paper Website -``` - -```{button-link} https://github.com/jkulhanek/tetra-nerf -:color: primary -:outline: -Code -``` - - - -**SfM input pointcloud is triangulated and resulting tetrahedra is used as the radiance field representation** - -## Installation - -First, make sure to install the following: -``` -CUDA (>=11.3) -PyTorch (>=1.12.1) -Nerfstudio (>=0.2.0) -OptiX (>=7.2, preferably 7.6) -CGAL -CMake (>=3.22.1) -``` -Follow the [installation section](https://github.com/jkulhanek/tetra-nerf/blob/master/README.md#installation) in the tetra-nerf repository - -Finally, you can install **Tetra-NeRF** by running: -```bash -pip install git+https://github.com/jkulhanek/tetra-nerf -``` - -## Running Tetra-NeRF on custom data -Details for running Tetra-NeRF can be found [here](https://github.com/jkulhanek/tetra-nerf). - -```bash -python -m tetranerf.scripts.process_images --path -python -m tetranerf.scripts.triangulate --pointcloud /sparse.ply --output /sparse.th -ns-train tetra-nerf --pipeline.model.tetrahedra-path /sparse.th minimal-parser --data -``` - -Three following variants of Tetra-NeRF are provided: - -| Method | Description | Memory | Quality | -| --------------------- | -------------------------------------- | ------- | ------- | -| `tetra-nerf-original` | Official implementation from the paper | ~18GB* | Good | -| `tetra-nerf` | Different sampler - faster and better | ~16GB* | Best | - -*Depends on the size of the input pointcloud, estimate is given for a larger scene (1M points) - -## Method -![method overview](https://jkulhanek.com/tetra-nerf/resources/overview-white.svg)
-The input to Tetra-NeRF is a point cloud which is triangulated to get a set of tetrahedra used to represent the radiance field. Rays are sampled, and the field is queried. The barycentric interpolation is used to interpolate tetrahedra vertices, and the resulting features are passed to a shallow MLP to get the density and colours for volumetric rendering.
- -[![demo blender lego (sparse)](https://jkulhanek.com/tetra-nerf/resources/images/blender-lego-sparse-100k-animated-cover.gif)](https://jkulhanek.com/tetra-nerf/demo.html?scene=blender-lego-sparse) -[![demo mipnerf360 garden (sparse)](https://jkulhanek.com/tetra-nerf/resources/images/360-garden-sparse-100k-animated-cover.gif)](https://jkulhanek.com/tetra-nerf/demo.html?scene=360-garden-sparse) -[![demo mipnerf360 garden (sparse)](https://jkulhanek.com/tetra-nerf/resources/images/360-bonsai-sparse-100k-animated-cover.gif)](https://jkulhanek.com/tetra-nerf/demo.html?scene=360-bonsai-sparse) -[![demo mipnerf360 kitchen (dense)](https://jkulhanek.com/tetra-nerf/resources/images/360-kitchen-dense-300k-animated-cover.gif)](https://jkulhanek.com/tetra-nerf/demo.html?scene=360-kitchen-dense) - - -## Existing checkpoints and predictions -For an easier comparisons with Tetra-NeRF, published checkpoints and predictions can be used: - -| Dataset | Checkpoints | Predictions | Input tetrahedra | -| -------- | ----------- | ----------- | ---------------- | -| Mip-NeRF 360 (public scenes) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/mipnerf360-public-checkpoints.tar.gz) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/mipnerf360-public-predictions.tar.gz) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/mipnerf360-public-tetrahedra.tar.gz) | -| Blender | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/blender-checkpoints.tar.gz) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/blender-predictions.tar.gz) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/blender-tetrahedra.tar.gz) | -| Tanks and Temples | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/nsvf-tanks-and-temples-checkpoints.tar.gz) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/nsvf-tanks-and-temples-predictions.tar.gz) | [download](https://data.ciirc.cvut.cz/public/projects/2023TetraNeRF/assets/nsvf-tanks-and-temples-tetrahedra.tar.gz) | - diff --git a/docs/nerfology/methods/zipnerf.md b/docs/nerfology/methods/zipnerf.md deleted file mode 100644 index f435eabb..00000000 --- a/docs/nerfology/methods/zipnerf.md +++ /dev/null @@ -1,33 +0,0 @@ -# Zip-NeRF - -

A pytorch implementation of "Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields"

- -```{button-link} https://jonbarron.info/zipnerf/ -:color: primary -:outline: -Paper Website -``` -```{button-link} https://github.com/SuLvXiangXin/zipnerf-pytorch -:color: primary -:outline: -Code -``` -### Installation -First, install nerfstudio and its dependencies. Then run: -``` -pip install git+https://github.com/SuLvXiangXin/zipnerf-pytorch#subdirectory=extensions/cuda -pip install git+https://github.com/SuLvXiangXin/zipnerf-pytorch -``` -Finally, install torch_scatter corresponding to your cuda version(https://pytorch-geometric.com/whl/torch-2.0.1%2Bcu118.html). - - -### Running Model - -```bash -ns-train zipnerf --data {DATA_DIR/SCENE} -``` - -## Overview -Zipnerf combines mip-NeRF 360’s overall framework with iNGP’s featurization approach. -Following mip-NeRF, zipnerf assume each pixel corresponds to a cone. Given an interval along the ray, it construct a set of multisamples that approximate the shape of that conical frustum. -Also, it present an alternative loss that, unlike mip-NeRF 360’s interlevel loss, is continuous and smooth with respect to distance along the ray to prevent z-aliasing. \ No newline at end of file diff --git a/docs/nerfology/model_components/imgs/frustums-dark.png b/docs/nerfology/model_components/imgs/frustums-dark.png deleted file mode 100644 index e1539380..00000000 Binary files a/docs/nerfology/model_components/imgs/frustums-dark.png and /dev/null differ diff --git a/docs/nerfology/model_components/imgs/frustums.png b/docs/nerfology/model_components/imgs/frustums.png deleted file mode 100644 index 173952c3..00000000 Binary files a/docs/nerfology/model_components/imgs/frustums.png and /dev/null differ diff --git a/docs/nerfology/model_components/imgs/samplers_stratified-dark.png b/docs/nerfology/model_components/imgs/samplers_stratified-dark.png deleted file mode 100644 index 59d7b9a5..00000000 Binary files a/docs/nerfology/model_components/imgs/samplers_stratified-dark.png and /dev/null differ diff --git a/docs/nerfology/model_components/imgs/samplers_stratified-light.png b/docs/nerfology/model_components/imgs/samplers_stratified-light.png deleted file mode 100644 index 0e4f9e84..00000000 Binary files a/docs/nerfology/model_components/imgs/samplers_stratified-light.png and /dev/null differ diff --git a/docs/nerfology/model_components/imgs/samplers_type-dark.png b/docs/nerfology/model_components/imgs/samplers_type-dark.png deleted file mode 100644 index 62fd9039..00000000 Binary files a/docs/nerfology/model_components/imgs/samplers_type-dark.png and /dev/null differ diff --git a/docs/nerfology/model_components/imgs/samplers_type-light.png b/docs/nerfology/model_components/imgs/samplers_type-light.png deleted file mode 100644 index fd3283f0..00000000 Binary files a/docs/nerfology/model_components/imgs/samplers_type-light.png and /dev/null differ diff --git a/docs/nerfology/model_components/index.md b/docs/nerfology/model_components/index.md deleted file mode 100644 index 513826ed..00000000 --- a/docs/nerfology/model_components/index.md +++ /dev/null @@ -1,12 +0,0 @@ -# Model components - -It can be difficult getting started with NeRFs. The research field is still quite new and most of the key nuggets are buried in academic papers. For this reason, we have consolidated many of the key concepts into a series of guides. - -```{toctree} - :maxdepth: 1 - Cameras models - Sample representation - Ray samplers - Spatial distortions - Encoders -``` diff --git a/docs/nerfology/model_components/visualize_cameras.ipynb b/docs/nerfology/model_components/visualize_cameras.ipynb deleted file mode 100644 index 99bb095a..00000000 --- a/docs/nerfology/model_components/visualize_cameras.ipynb +++ /dev/null @@ -1,91449 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Camera Models\n", - "\n", - "Everything starts with the cameras. Given a set of images, we need a way to project their pixels into 3D space. This is accomplished by computing the camera ray for each pixel given information about the type of camera and the location of the camera. This guide describes the camera models implemented in nerfstudio.\n", - "\n", - "Each image should have an associated pose which consists of two properties, intrinsic and extrinsic parameters.\n", - "\n", - "Intrinsics\n", - ": All of the parameters internal to the camera such as lense or sensor properties.\n", - "\n", - "Extrinsics\n", - ": All of the parameters external to the camera such as the location and rotation relative to the world frame.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# HIDDEN\n", - "import torch\n", - "from nerfstudio.cameras import cameras\n", - "import plotly.graph_objects as go\n", - "import mediapy as media" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Perspective Camera Model\n", - "\n", - "The simplest and most used camera model. 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "from nerfstudio.cameras.cameras import Cameras, CameraType\n", - "from nerfstudio.utils import plotly_utils as vis\n", - "\n", - "cx = 20.0\n", - "cy = 10.0\n", - "fx = 20.0\n", - "fy = 20.0\n", - "\n", - "c2w = torch.eye(4)[None, :3, :]\n", - "\n", - "camera = Cameras(fx=fx, fy=fy, cx=cx, cy=cy, camera_to_worlds=c2w, camera_type=CameraType.PERSPECTIVE)\n", - "fig = vis.vis_camera_rays(camera)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Fisheye Camera Model\n", - "\n", - "The spacing of rays on a fisheye camera is more even on the edges of the image (most noticeable in wide angle images). This makes them desirable for NeRF reconstructions.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "lightblue", - "width": 1 - }, - "marker": { - "color": [ - [ - 0.02500000037252903, - 0.02500000037252903, - 1 - ], - [ - 0.02500000037252903, - 0.02500000037252903, - 1 - ], - [ - 0.02500000037252903, - 0.07500000298023224, - 1 - ], - [ - 0.02500000037252903, - 0.07500000298023224, - 1 - ], - [ - 0.02500000037252903, - 0.125, - 1 - ], - [ - 0.02500000037252903, - 0.125, - 1 - ], - [ - 0.02500000037252903, - 0.17499999701976776, - 1 - ], - [ - 0.02500000037252903, - 0.17499999701976776, - 1 - ], - [ - 0.02500000037252903, - 0.22499999403953552, - 1 - ], - [ - 0.02500000037252903, - 0.22499999403953552, - 1 - ], - [ - 0.02500000037252903, - 0.2750000059604645, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "from nerfstudio.cameras.cameras import Cameras, CameraType\n", - "from nerfstudio.utils import plotly_utils as vis\n", - "\n", - "cx = 10.0\n", - "cy = 10.0\n", - "fx = 10.0\n", - "fy = 10.0\n", - "\n", - "c2w = torch.eye(4)[None, :3, :]\n", - "\n", - "camera = Cameras(fx=fx, fy=fy, cx=cx, cy=cy, camera_to_worlds=c2w, camera_type=CameraType.FISHEYE)\n", - "fig = vis.vis_camera_rays(camera)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Equirectangular/Spherical Camera Model\n", - "\n", - "Rays are produced at all angles around the camera." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "lightblue", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "from nerfstudio.cameras.cameras import Cameras, CameraType\n", - "from nerfstudio.utils import plotly_utils as vis\n", - "\n", - "cx = 20.0 # = width/2\n", - "cy = 10.0 # = height/2\n", - "fx = 20.0 # = height = width/2\n", - "fy = 20.0 # = height = width/2\n", - "\n", - "c2w = torch.eye(4)[None, :3, :]\n", - "\n", - "camera = Cameras(fx=fx, fy=fy, cx=cx, cy=cy, camera_to_worlds=c2w, camera_type=CameraType.EQUIRECTANGULAR)\n", - "fig = vis.vis_camera_rays(camera)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Distortion Parameters\n", - "\n", - "Unless you are using a pinhole camera (you probably aren't), modeling the distortion caused by the lenses may be beneficial. We represent the distortion with 6 parameters, $[k_1, k_2, k_3, k_4]$ for radial distortion and $[p_1, p_2]$ for tangential distortion.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "marker": { - "size": 10 - }, - "mode": "markers", - "type": "scatter", - "visible": false, - "x": [ - -0.9811120629310608, - -0.9834977388381958, - -0.9855434894561768, - -0.9872362017631531, - -0.988564670085907, - -0.9895201921463013, - -0.9900960922241211, - -0.990288496017456, - -0.9900960922241211, - -0.9895201921463013, - -0.988564670085907, - -0.9872362017631531, - -0.9855434894561768, - -0.9834977388381958, - -0.9811120629310608, - -0.8429980278015137, - -0.8450727462768555, - -0.8468520641326904, - -0.8483244776725769, - -0.849480390548706, - -0.8503117561340332, - -0.8508129715919495, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# OUTPUT_ONLY\n", - "height = 15\n", - "width = 15\n", - "\n", - "from plotly.subplots import make_subplots\n", - "\n", - "distortion_names = (\"k1\", \"k2\", \"p1\", \"k3\", \"k4\", \"p2\")\n", - "distortion_min = (0.01, 0.001, 0.01, 0.001, -0.05, -0.05)\n", - "distortion_max = (0.05, 0.05, 0.05, 0.05, 0.05, 0.05)\n", - "fig = make_subplots(rows=2, cols=3, vertical_spacing=0.1, subplot_titles=distortion_names)\n", - "\n", - "num_steps = 19\n", - "all_steps = []\n", - "for i in range(len(distortion_names)):\n", - " for step in torch.linspace(distortion_min[i], distortion_max[i], num_steps):\n", - " distortion_params = torch.zeros(6)\n", - " distortion_params[i] = step\n", - "\n", - " from nerfstudio.cameras import camera_utils\n", - "\n", - " coords = torch.meshgrid(torch.linspace(-1, 1, height), torch.linspace(-1, 1, width), indexing=\"ij\")\n", - " coords = torch.stack(coords, dim=-1)\n", - "\n", - " coords = camera_utils.radial_and_tangential_undistort(coords, distortion_params)\n", - "\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=coords[..., 0].flatten(),\n", - " y=coords[..., 1].flatten(),\n", - " mode=\"markers\",\n", - " marker=dict(\n", - " size=10,\n", - " ),\n", - " visible=False,\n", - " ),\n", - " row=i % 2 + 1,\n", - " col=i // 2 + 1,\n", - " )\n", - "\n", - " fig.data[num_steps * i + num_steps // 2].visible = True\n", - "\n", - "# Create and add slider\n", - "steps = []\n", - "for i in range(num_steps):\n", - " step = dict(method=\"update\", args=[{\"visible\": [False] * len(fig.data)}], label=\"\")\n", - " for d in range(len(distortion_names)):\n", - " step[\"args\"][0][\"visible\"][num_steps * d + i] = True\n", - " steps.append(step)\n", - "\n", - "sliders = [\n", - " dict(\n", - " active=num_steps // 2,\n", - " pad={\"t\": 10},\n", - " steps=steps,\n", - " len=0.5,\n", - " x=0.5,\n", - " xanchor=\"center\",\n", - " )\n", - "]\n", - "\n", - "webdocs_layout = go.Layout(\n", - " height=600,\n", - " sliders=sliders,\n", - " margin=dict(r=0, b=0, l=0, t=20),\n", - " hovermode=False,\n", - " showlegend=False,\n", - " paper_bgcolor=\"rgba(0,0,0,0)\",\n", - " plot_bgcolor=\"rgba(0,0,0,0)\",\n", - " font=dict(color=\"#a3a3a3\", size=18),\n", - ")\n", - "fig.for_each_yaxis(lambda x: x.update(range=[-1.3, 1.3], constrain=\"domain\", showgrid=False, visible=False))\n", - "fig.for_each_xaxis(lambda x: x.update(scaleanchor=\"y\", scaleratio=1, showgrid=False, visible=False))\n", - "fig.update_layout(webdocs_layout)\n", - "\n", - "fig.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "nerfactory", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "vscode": { - "interpreter": { - "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/nerfology/model_components/visualize_encoders.ipynb b/docs/nerfology/model_components/visualize_encoders.ipynb deleted file mode 100644 index b4707732..00000000 --- a/docs/nerfology/model_components/visualize_encoders.ipynb +++ /dev/null @@ -1,571 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Field Encoders" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(nerf)=\n", - "## NeRF Positional Encoding\n", - "First introduced in the original NeRF paper. This encoding assumes the inputs are between zero and one and can opperate on any dimensional input." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Integrate Values:\n", - "Covariance Magnitude: 0.01\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 0.1\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 1\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.field_components import encodings as encoding\n", - "\n", - "num_frequencies = 4\n", - "min_freq_exp = 0\n", - "max_freq_exp = 6\n", - "include_input = False\n", - "resolution = 128\n", - "covariance_magnitudes = [0.01, 0.1, 1]\n", - "\n", - "encoder = encoding.NeRFEncoding(\n", - " in_dim=2,\n", - " num_frequencies=num_frequencies,\n", - " min_freq_exp=min_freq_exp,\n", - " max_freq_exp=max_freq_exp,\n", - " include_input=include_input,\n", - ")\n", - "\n", - "x_samples = torch.linspace(0, 1, resolution)\n", - "grid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", - "\n", - "encoded_values = encoder(grid)\n", - "\n", - "print(\"Input Values:\")\n", - "media.show_images(torch.moveaxis(grid, 2, 0), cmap=\"plasma\", border=True)\n", - "print(\"Encoded Values:\")\n", - "media.show_images(torch.moveaxis(encoded_values, 2, 0), vmin=-1, vmax=1, cmap=\"plasma\", border=True)\n", - "\n", - "print(\"Encoded Integrate Values:\")\n", - "for covariance_magnitude in covariance_magnitudes:\n", - " print(f\"Covariance Magnitude: {covariance_magnitude}\")\n", - " covs = torch.eye(2)[None, None, :, :] * covariance_magnitude\n", - " encoded_values = encoder(grid, covs=covs)\n", - " media.show_images(torch.moveaxis(encoded_values, 2, 0), vmin=-1, vmax=1, cmap=\"plasma\", border=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(rff)=\n", - "## Random Fourier Feature (RFF) Encoding\n", - "This encoding assumes the inputs are between zero and one and can opperate on any dimensional input." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Integrate Values:\n", - "Covariance Magnitude: 0.001\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 0.01\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 0.1\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.field_components import encodings as encoding\n", - "\n", - "num_frequencies = 8\n", - "scale = 10\n", - "resolution = 128\n", - "covariance_magnitudes = [0.001, 0.01, 0.1]\n", - "\n", - "encoder = encoding.RFFEncoding(in_dim=2, num_frequencies=num_frequencies, scale=scale)\n", - "\n", - "x_samples = torch.linspace(0, 1, resolution)\n", - "grid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", - "\n", - "encoded_values = encoder(grid)\n", - "\n", - "print(\"Input Values:\")\n", - "media.show_images(torch.moveaxis(grid, 2, 0), cmap=\"plasma\", border=True)\n", - "print(\"Encoded Values:\")\n", - "media.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\"plasma\", vmin=-1, vmax=1, border=True)\n", - "\n", - "print(\"Encoded Integrate Values:\")\n", - "for covariance_magnitude in covariance_magnitudes:\n", - " print(f\"Covariance Magnitude: {covariance_magnitude}\")\n", - " covs = torch.eye(2)[None, None, :, :] * covariance_magnitude\n", - " encoded_values = encoder(grid, covs=covs)\n", - " media.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\"plasma\", vmin=-1, vmax=1, border=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(hash)=\n", - "## Hash Encoding\n", - "The hash incoding was originally introduced in Instant-NGP. The encoding is optimized during training. This is a visualization of the initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.0010, grad_fn=)\n", - "tensor(-0.0010, grad_fn=)\n", - "Input Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.field_components import encodings as encoding\n", - "\n", - "num_levels = 8\n", - "min_res = 2\n", - "max_res = 128\n", - "log2_hashmap_size = 4 # Typically much larger tables are used\n", - "\n", - "resolution = 128\n", - "slice = 0\n", - "\n", - "# Fixing features_per_level to 3 for easy RGB visualization. Typical value is 2 in networks\n", - "features_per_level = 3\n", - "\n", - "encoder = encoding.HashEncoding(\n", - " num_levels=num_levels,\n", - " min_res=min_res,\n", - " max_res=max_res,\n", - " log2_hashmap_size=log2_hashmap_size,\n", - " features_per_level=features_per_level,\n", - " hash_init_scale=0.001,\n", - " implementation=\"torch\",\n", - ")\n", - "\n", - "x_samples = torch.linspace(0, 1, resolution)\n", - "grid = torch.stack(torch.meshgrid([x_samples, x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", - "\n", - "encoded_values = encoder(grid)\n", - "print(torch.max(encoded_values))\n", - "print(torch.min(encoded_values))\n", - "\n", - "grid_slice = grid[slice, ...]\n", - "encoded_values_slice = encoded_values[slice, ...]\n", - "\n", - "print(\"Input Values:\")\n", - "media.show_images(torch.moveaxis(grid_slice, 2, 0), cmap=\"plasma\", border=True)\n", - "\n", - "print(\"Encoded Values:\")\n", - "encoded_images = encoded_values_slice.view(resolution, resolution, num_levels, 3)\n", - "encoded_images = torch.moveaxis(encoded_images, 2, 0)\n", - "encoded_images -= torch.min(encoded_images)\n", - "encoded_images /= torch.max(encoded_images)\n", - "media.show_images(encoded_images.detach().numpy(), cmap=\"plasma\", border=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(spherical)=\n", - "## Spherical Harmonic Encoding\n", - "Encode direction using spherical harmonics. (Mostly used to encode viewing direction)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Level: 1\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.field_components import encodings as encoding\n", - "\n", - "levels = 4\n", - "\n", - "height = 100\n", - "width = 150\n", - "\n", - "encoder = encoding.SHEncoding(levels=levels)\n", - "\n", - "theta = torch.linspace(-torch.pi, torch.pi, width)\n", - "phi = torch.linspace(0, torch.pi, height)\n", - "[theta, phi] = torch.meshgrid([theta, phi], indexing=\"xy\")\n", - "\n", - "directions = torch.stack([torch.cos(theta) * torch.sin(phi), torch.sin(theta) * torch.sin(phi), torch.cos(phi)], dim=-1)\n", - "\n", - "encoded_values = encoder(directions)\n", - "encoded_values = torch.moveaxis(encoded_values, 2, 0)\n", - "\n", - "for level in range(levels):\n", - " print(f\"Level: {level+1}\")\n", - " media.show_images(encoded_values[level**2 : (level + 1) ** 2, ...], cmap=\"plasma\", border=True)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.12 ('nerfactory')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "vscode": { - "interpreter": { - "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/nerfology/model_components/visualize_samplers.ipynb b/docs/nerfology/model_components/visualize_samplers.ipynb deleted file mode 100644 index a91c754e..00000000 --- a/docs/nerfology/model_components/visualize_samplers.ipynb +++ /dev/null @@ -1,7441 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ray Samplers\n", - "\n", - "## Overview\n", - "\n", - "Once we have a set of cameras, we want to cast camera rays associated with each pixel. \n", - "\n", - "Along these ray we will sample the _field_ and aggregate the samples to predict the pixels value (ie. color). The parameterization of the samples are described [here](./visualize_samples.ipynb) however we must decide where to place these samples along a ray. For this task we will use a [`Sampler`](../../reference/api/model_components/ray_sampler.rst).\n", - "\n", - "In the ideal world we would compute many dense samples along a ray. Unfortunately, each additional sample adds a computation cost to the system as it needs to be processed by the _field_ which is often a neural network.\n", - "\n", - "As a result it is common for NeRF methods to use on the order of 100 samples. Therefore, we want to optimize where those samples are placed in the scene.\n", - "\n", - "For example, if the scene can be bounded by a box and the objects are all similar scales, uniform sampling along the ray may be a good option. On the other hand if the scene is unbounded (potential extending as far as the eye can see) uniform sampling does not make sense as the samples would be very sparse for close objects. In this case a different sampling like _Uniform in Disparity_ may perform better.\n", - "\n", - "```{image} imgs/samplers_type-light.png\n", - ":align: center\n", - ":class: only-light\n", - "```\n", - "\n", - "```{image} imgs/samplers_type-dark.png\n", - ":align: center\n", - ":class: only-dark\n", - "```\n", - "\n", - "### Stratified Sampling\n", - "\n", - "Most samplers has the option to _stratify_ the samplers. When stratified, each sample is randomly perturbed. \n", - "\n", - "The magnitude of the pertubation is such that the sample ordering remains consistent and the overall distribution statistics are not changed. Using stratified samples during training generally improves the reconstructions as it help prevent overfitting.\n", - "\n", - "During inference stratified sampling should be disabled (nerfstudio samplers will do this) as it can cause noisy artifacts when the camera moves.\n", - "\n", - "```{image} imgs/samplers_stratified-light.png\n", - ":align: center\n", - ":class: only-light\n", - "```\n", - "\n", - "```{image} imgs/samplers_stratified-dark.png\n", - ":align: center\n", - ":class: only-dark\n", - "```\n", - "\n", - "### Hierarchical Sampling\n", - "\n", - "It is important to sample the scene where it has content otherwise the reconstruction quality will be reduced. \n", - "\n", - "One trick that is often employed in NeRF methods is to do multiple round of sampling. The first round can use a predefined sampler (ie. Uniform) to generate an image. Once the space is sampled, we have an idea which samples contributed to the final color. \n", - "\n", - "We can use this information to sample more around those regions using a `PDFSampler`. The PDF sampler is described more below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spaced Samplers\n", - "\n", - "These are the most basic samplers that spaces samples based on a predefined function. These samplers have all have a starting and ending distance (also known as a near/far plane). 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-    "# COLLAPSED\n",
-    "import torch\n",
-    "from nerfstudio.cameras.rays import RayBundle\n",
-    "from nerfstudio.model_components import ray_sampler\n",
-    "import plotly.graph_objects as go\n",
-    "from plotly.subplots import make_subplots\n",
-    "\n",
-    "\n",
-    "num_samples = 1000\n",
-    "near = 2\n",
-    "far = 5\n",
-    "train_stratified = False\n",
-    "\n",
-    "samplers = [\n",
-    "    ray_sampler.UniformSampler,\n",
-    "    ray_sampler.LinearDisparitySampler,\n",
-    "    ray_sampler.SqrtSampler,\n",
-    "    ray_sampler.LogSampler,\n",
-    "]\n",
-    "\n",
-    "fig = make_subplots(\n",
-    "    rows=2,\n",
-    "    cols=2,\n",
-    "    subplot_titles=(\"Uniform\", \"Linear in Disparity\", \"Square Root\", \"Log Sampler\"),\n",
-    "    shared_xaxes=True,\n",
-    "    shared_yaxes=True,\n",
-    "    vertical_spacing=0.1,\n",
-    ")\n",
-    "\n",
-    "for i, Sampler in enumerate(samplers):\n",
-    "    sampler = Sampler(num_samples=num_samples, train_stratified=train_stratified)\n",
-    "\n",
-    "    ray_bundle = RayBundle(\n",
-    "        origins=torch.ones([1, 3]),\n",
-    "        directions=torch.ones([1, 3]),\n",
-    "        pixel_area=torch.ones([1, 1]),\n",
-    "        nears=torch.ones([1, 1]) * near,\n",
-    "        fars=torch.ones([1, 1]) * far,\n",
-    "    )\n",
-    "\n",
-    "    samples = sampler.generate_ray_samples(ray_bundle)\n",
-    "\n",
-    "    trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=50)\n",
-    "    fig.append_trace(trace, i // 2 + 1, i % 2 + 1)\n",
-    "\n",
-    "fig.update_yaxes(title_text=\"# Samples\", row=1, col=1)\n",
-    "fig.update_yaxes(title_text=\"# Samples\", row=2, col=1)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=2, col=1)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=2, col=2)\n",
-    "\n",
-    "# Overlay both histograms\n",
-    "fig.update_layout(height=700, hovermode=False, showlegend=False, margin=dict(l=20, r=20, t=50, b=20))\n",
-    "fig.update_yaxes(range=[0, 80])\n",
-    "fig.update_traces(opacity=0.7)\n",
-    "fig.show()"
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-    "## PDF Sampler\n",
-    "\n",
-    "The Probability Distribution Function (PDF) Sampler generates samples that match a given distribution. \n",
-    "\n",
-    "In the example below we first create a `UniformSampler` to generate a set of initial samples. We then assign weights to each of these samples to define the PDF (here it is an arbitrary function, but usually you would use the predicted weights from the field). The left plot the target PDF, on the right we plot a histogram of samples generated from the `PDFSampler`."
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-            "gridcolor": "white",
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-           "x": 0.05
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-           "automargin": true,
-           "gridcolor": "white",
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-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
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-            "standoff": 15
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-        },
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
-          0,
-          0.45
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-         "title": {
-          "text": "Distance"
-         }
-        },
-        "xaxis2": {
-         "anchor": "y2",
-         "domain": [
-          0.55,
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-          "text": "Distance"
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-         "anchor": "x",
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-        "yaxis2": {
-         "anchor": "x2",
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-         "title": {
-          "text": "# Samples"
-         }
-        }
-       }
-      },
-      "text/html": [
-       "
                            
                    
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# COLLAPSED\n",
-    "import torch\n",
-    "from nerfstudio.cameras.rays import RayBundle\n",
-    "from nerfstudio.model_components import ray_sampler\n",
-    "import plotly.graph_objects as go\n",
-    "from plotly.subplots import make_subplots\n",
-    "\n",
-    "num_coarse_samples = 20\n",
-    "num_samples = 1000\n",
-    "near = 2\n",
-    "far = 5\n",
-    "train_stratified = False\n",
-    "\n",
-    "fig = make_subplots(\n",
-    "    rows=1,\n",
-    "    cols=2,\n",
-    "    subplot_titles=(\"PDF\", \"Samples\"),\n",
-    ")\n",
-    "\n",
-    "uniform_sampler = ray_sampler.UniformSampler(num_samples=num_coarse_samples, train_stratified=train_stratified)\n",
-    "pdf_sampler = ray_sampler.PDFSampler(num_samples=num_samples, train_stratified=train_stratified, include_original=False)\n",
-    "\n",
-    "ray_bundle = RayBundle(\n",
-    "    origins=torch.ones([1, 3]),\n",
-    "    directions=torch.ones([1, 3]),\n",
-    "    pixel_area=torch.ones([1, 1]),\n",
-    "    nears=torch.ones([1, 1]) * near,\n",
-    "    fars=torch.ones([1, 1]) * far,\n",
-    ")\n",
-    "\n",
-    "coarse_ray_samples = uniform_sampler(ray_bundle)\n",
-    "\n",
-    "# Generate arbitrary PDF\n",
-    "weights = torch.ones(num_coarse_samples)\n",
-    "weights += torch.sin(torch.linspace(0, 3 * torch.pi, num_coarse_samples))\n",
-    "weights += torch.sin(torch.linspace(0, 0.5 * torch.pi, num_coarse_samples))\n",
-    "weights -= torch.min(weights)\n",
-    "weights /= torch.sum(weights)\n",
-    "\n",
-    "samples = pdf_sampler.generate_ray_samples(ray_bundle, coarse_ray_samples, weights[None, :, None], num_samples)\n",
-    "\n",
-    "# Plotting stuff\n",
-    "x = torch.ones((num_coarse_samples * 2))\n",
-    "x[::2] = coarse_ray_samples.frustums.starts[0, :, 0]\n",
-    "x[1::2] = coarse_ray_samples.frustums.ends[0, :, 0]\n",
-    "\n",
-    "y = torch.ones((num_coarse_samples * 2))\n",
-    "y[::2] = weights\n",
-    "y[1::2] = weights\n",
-    "\n",
-    "pdf_trace = go.Scatter(x=x, y=y)\n",
-    "fig.append_trace(pdf_trace, 1, 1)\n",
-    "\n",
-    "samples_trace = go.Histogram(x=samples.frustums.starts[0, :, 0], nbinsx=100)\n",
-    "fig.append_trace(samples_trace, 1, 2)\n",
-    "\n",
-    "fig.update_yaxes(title_text=\"# Samples\", row=1, col=2)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=1, col=1)\n",
-    "fig.update_xaxes(title_text=\"Distance\", row=1, col=2)\n",
-    "\n",
-    "# Overlay both histograms\n",
-    "fig.update_layout(height=400, hovermode=False, showlegend=False, margin=dict(l=20, r=20, t=50, b=20))\n",
-    "fig.update_traces(opacity=0.7)\n",
-    "fig.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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diff --git a/docs/nerfology/model_components/visualize_samples.ipynb b/docs/nerfology/model_components/visualize_samples.ipynb
deleted file mode 100644
index 55f30908..00000000
--- a/docs/nerfology/model_components/visualize_samples.ipynb
+++ /dev/null
@@ -1,68255 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sample Representation\n",
-    "\n",
-    "A core component of volumetric rendering is computing samples along a ray. The representation of these samples is important. We use conical frustums to represent samples. The conical frustums are defined via the following parameters:\n",
-    "\n",
-    "```{image} imgs/frustums.png\n",
-    ":align: center\n",
-    ":class: only-light\n",
-    ":width: 600\n",
-    "```\n",
-    "\n",
-    "```{image} imgs/frustums-dark.png\n",
-    ":align: center\n",
-    ":class: only-dark\n",
-    ":width: 600\n",
-    "```\n",
-    "\n",
-    "| Parameter  | Dimension | Description                                           |\n",
-    "| ---------- | :-------: | ----------------------------------------------------- |\n",
-    "| origin     |     3     | The _x,y,z_ origin of the ray                         |\n",
-    "| direction  |     3     | The direction of the ray represented as a unit vector |\n",
-    "| pixel area |     1     | The pixel's area a distance of 1 from the origin      |\n",
-    "| start      |     1     | The distance at which the frustum starts              |\n",
-    "| end        |     1     | The distance at which the frustums ends               |\n",
-    "\n",
-    ":::{note}\n",
-    "Why conical frustums and not pyramidal frustums? Those interested in this question may be interested in [_A Pixel Is Not A Little Square_](http://alvyray.com/Memos/CG/Microsoft/6_pixel.pdf) by Alvy Ray Smith.\n",
-    ":::\n",
-    "\n",
-    ":::{important}\n",
-    "This representation is only an approximation.\n",
-    "nerfstudio calculates $pixel area$ as $dx * dy$ where $dx$ and $dy$ are the distance between points neighboring rays in the $x$ and $y$ direction a distance of 1 from the origin.\n",
-    ":::\n",
-    "\n",
-    "\n",
-    "## 3D Frustum\n"
-   ]
-  },
-  {
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-   "execution_count": 1,
-   "metadata": {
-    "tags": [
-     "remove-cell"
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-    "# HIDDEN\n",
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-    "webdocs_layout = go.Layout(\n",
-    "    scene=dict(\n",
-    "        aspectmode=\"data\",\n",
-    "        xaxis=dict(showspikes=False),\n",
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-    "        yaxis_visible=False,\n",
-    "        zaxis_visible=False,\n",
-    "    ),\n",
-    "    scene_camera=dict(up=dict(x=0, y=1, z=0)),\n",
-    "    margin=dict(r=0, b=10, l=0, t=10),\n",
-    "    hovermode=False,\n",
-    "    showlegend=False,\n",
-    "    paper_bgcolor=\"rgba(0,0,0,0)\",\n",
-    ")"
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-    "# COLLAPSED\n",
-    "import torch\n",
-    "from nerfstudio.cameras.cameras import Cameras, CameraType\n",
-    "from nerfstudio.utils import plotly_utils as vis\n",
-    "import plotly.graph_objects as go\n",
-    "\n",
-    "cx = 2.0\n",
-    "cy = 2.0\n",
-    "fx = 10.0\n",
-    "fy = 10.0\n",
-    "\n",
-    "num_samples = 3\n",
-    "near_plane = 1\n",
-    "far_plane = 3\n",
-    "\n",
-    "c2w = torch.eye(4)[None, :3, :]\n",
-    "camera = Cameras(fx=fx, fy=fy, cx=cx, cy=cy, camera_to_worlds=c2w, camera_type=CameraType.PERSPECTIVE)\n",
-    "ray_bundle = camera.generate_rays(camera_indices=0)\n",
-    "\n",
-    "bins = torch.linspace(near_plane, far_plane, num_samples + 1)[..., None]\n",
-    "ray_samples = ray_bundle.get_ray_samples(bin_starts=bins[:-1, :], bin_ends=bins[1:, :])\n",
-    "\n",
-    "vis_rays = vis.get_ray_bundle_lines(ray_bundle, color=\"teal\", length=far_plane)\n",
-    "\n",
-    "fig = go.Figure(data=[vis_rays] + vis.get_frustums_mesh_list(ray_samples.frustums), layout=webdocs_layout)\n",
-    "fig.show()"
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-   "metadata": {},
-   "source": [
-    "## Representing Frustums as Points\n",
-    "\n",
-    "These frustums can be converted into point samples using `frustum.get_positions()`.\n"
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-    "fig = go.Figure(data=[vis_rays, vis.get_frustum_points(ray_samples.frustums)], layout=webdocs_layout)\n",
-    "fig.show()"
-   ]
-  },
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-   "metadata": {},
-   "source": [
-    "## Representing Frustums as Gaussians\n",
-    "\n",
-    "We can also appoximate the frustums using Gaussians with `frustum.get_gaussian_blob()`.\n"
-   ]
-  },
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# COLLAPSED\n",
-    "fig = go.Figure(\n",
-    "    data=[vis_rays] + vis.get_gaussian_ellipsoids_list(ray_samples.frustums.flatten().get_gaussian_blob()),\n",
-    "    layout=webdocs_layout,\n",
-    ")\n",
-    "fig.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "nerfactory",
-   "language": "python",
-   "name": "python3"
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-    "name": "ipython",
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diff --git a/docs/nerfology/model_components/visualize_spatial_distortions.ipynb b/docs/nerfology/model_components/visualize_spatial_distortions.ipynb
deleted file mode 100644
index 5a0145fd..00000000
--- a/docs/nerfology/model_components/visualize_spatial_distortions.ipynb
+++ /dev/null
@@ -1,234427 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "# Spatial Distortions\n",
-                "\n",
-                "If you are trying to reconstruct an object floating in an empty void, you can stop reading. However if you are trying to reconstruct a scene or object from images, you may wish to consider adding a spatial distortion.\n",
-                "\n",
-                "When rendering a target view of a scene, the camera will emit a camera ray for each pixel and query the scene at points along this ray. We can choose where to query these points using different [samplers](visualize_samplers.ipynb). These samplers have some notion of _bounds_ that define where the ray should start and terminate. If you know that everything in your scenes exists within some predefined bounds (ie. a cube that a room fits in) then the sampler will properly sample the entire space. If however the scene is unbounded (ie. an outdoor scene) defining where to stop sampling is challenging. One option to increase the far sampling distance to a large value (ie. 1km). Alternatively we can warp the space into a fixed volume. Below are supported distortions.\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "## Scene Contraction\n",
-                "\n",
-                "Contract unbounded space into a ball of radius 2. This contraction was proposed in [MipNeRF-360](https://jonbarron.info/mipnerf360/). Samples within the unit ball are not modified, whereas sample outside the unit ball are contracted to fit within the ball of radius 2.\n",
-                "\n",
-                "We use the following contraction equation:\n",
-                "\n",
-                "$$\n",
-                "    f(x) = \\begin{cases}\n",
-                "        x & ||x|| \\leq 1 \\\\\n",
-                "        (2 - \\frac{1}{||x||})(\\frac{x}{||x||}) & ||x|| > 1\n",
-                "    \\end{cases}\n",
-                "$$\n",
-                "\n",
-                "Below we visualize a ray before and after scene contraction. Visualized are 95% confidence intervals for the multivariate Gaussians for each sample location ([this guide](visualize_samples.ipynb) explains why the samples are represented by Gaussians).\n",
-                "We are also visualizing both a unit sphere and a radius 2 sphere.\n",
-                "\n",
-                "(before)=\n",
-                "### Before Contraction\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {
-                "tags": [
-                    "remove-cell"
-                ]
-            },
-            "outputs": [],
-            "source": [
-                "# HIDDEN\n",
-                "import torch\n",
-                "import plotly.graph_objects as go\n",
-                "\n",
-                "torch.manual_seed(4)\n",
-                "\n",
-                "webdocs_layout = go.Layout(\n",
-                "    scene=dict(\n",
-                "        aspectmode=\"data\",\n",
-                "        xaxis=dict(showspikes=False),\n",
-                "        yaxis=dict(showspikes=False),\n",
-                "        zaxis=dict(showspikes=False),\n",
-                "    ),\n",
-                "    scene_camera=dict(up=dict(x=0, y=1, z=0)),\n",
-                "    margin=dict(r=0, b=10, l=0, t=10),\n",
-                "    hovermode=False,\n",
-                "    showlegend=False,\n",
-                "    paper_bgcolor=\"rgba(0,0,0,0)\",\n",
-                ")"
-            ]
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-                "# COLLAPSED\n",
-                "import nerfstudio\n",
-                "from nerfstudio.utils import plotly as vis\n",
-                "import torch\n",
-                "import plotly.graph_objects as go\n",
-                "\n",
-                "num_rays = 4\n",
-                "num_samples = 15\n",
-                "pixel_area = 0.12\n",
-                "far = 5\n",
-                "\n",
-                "origins = torch.rand((num_rays, 1, 3)) * 1 - 0.5\n",
-                "directions = torch.randn((num_rays, 1, 3))\n",
-                "bins = torch.linspace(0.1, far, num_samples + 1)[None, ..., None]\n",
-                "pixel_area = torch.tensor([pixel_area])[None, None, ...]\n",
-                "\n",
-                "directions = directions / directions.norm(dim=-1, keepdim=True)\n",
-                "\n",
-                "frustums = nerfstudio.cameras.rays.Frustums(\n",
-                "    origins=origins, directions=directions, starts=bins[:, :-1, :], ends=bins[:, 1:, :], pixel_area=pixel_area\n",
-                ")\n",
-                "\n",
-                "data = []\n",
-                "for i, frustum in enumerate(frustums):\n",
-                "    data += vis.get_gaussian_ellipsoids_list(frustum.get_gaussian_blob(), color=vis.get_random_color(idx=i))\n",
-                "data.append(vis.get_sphere(radius=1.0, color=\"#111111\", opacity=0.05))\n",
-                "data.append(vis.get_sphere(radius=2.0, color=\"#111111\", opacity=0.05))\n",
-                "\n",
-                "fig = go.Figure(data=data, layout=webdocs_layout)\n",
-                "fig.show()"
-            ]
-        },
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-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
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-            "source": [
-                "### After Contraction\n",
-                "\n",
-                "Using a $L_2$ norm"
-            ]
-        },
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-            "execution_count": 3,
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"
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-                    "metadata": {},
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-            ],
-            "source": [
-                "# COLLAPSED\n",
-                "from nerfstudio.field_components.spatial_distortions import SceneContraction\n",
-                "\n",
-                "data = []\n",
-                "for i, frustum in enumerate(frustums):\n",
-                "    contracted_gaussian = SceneContraction()(frustum.get_gaussian_blob())\n",
-                "    data += vis.get_gaussian_ellipsoids_list(contracted_gaussian, color=vis.get_random_color(idx=i))\n",
-                "\n",
-                "data.append(vis.get_sphere(radius=1.0, color=\"#111111\", opacity=0.05))\n",
-                "data.append(vis.get_sphere(radius=2.0, color=\"#111111\", opacity=0.05))\n",
-                "\n",
-                "fig = go.Figure(data=data, layout=webdocs_layout)\n",
-                "fig.show()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "Using $L_{\\infty}$ norm will bound the space to a cube of side length 4. This can be useful if downstream encoders opperated on a grid (ie. the hash encoder using in Instant-NGP)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {
-                "tags": [
-                    "hide-input"
-                ]
-            },
-            "outputs": [
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-                                        "scene": {
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-                                                "backgroundcolor": "#E5ECF6",
-                                                "gridcolor": "white",
-                                                "gridwidth": 2,
-                                                "linecolor": "white",
-                                                "showbackground": true,
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-                                            "yaxis": {
-                                                "backgroundcolor": "#E5ECF6",
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-                                            "zaxis": {
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-                                        "shapedefaults": {
-                                            "line": {
-                                                "color": "#2a3f5f"
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-                                        "ternary": {
-                                            "aaxis": {
-                                                "gridcolor": "white",
-                                                "linecolor": "white",
-                                                "ticks": ""
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-                                                "gridcolor": "white",
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-                                            "bgcolor": "#E5ECF6",
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-                                                "gridcolor": "white",
-                                                "linecolor": "white",
-                                                "ticks": ""
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-                                        "title": {
-                                            "x": 0.05
-                                        },
-                                        "xaxis": {
-                                            "automargin": true,
-                                            "gridcolor": "white",
-                                            "linecolor": "white",
-                                            "ticks": "",
-                                            "title": {
-                                                "standoff": 15
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-                                            "zerolinecolor": "white",
-                                            "zerolinewidth": 2
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-                                        "yaxis": {
-                                            "automargin": true,
-                                            "gridcolor": "white",
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-                                            "ticks": "",
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-                            "
                            
                    
"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# COLLAPSED\n",
-                "from nerfstudio.field_components.spatial_distortions import SceneContraction\n",
-                "\n",
-                "data = []\n",
-                "for i, frustum in enumerate(frustums):\n",
-                "    contracted_gaussian = SceneContraction(order=float(\"inf\"))(frustum.get_gaussian_blob())\n",
-                "    data += vis.get_gaussian_ellipsoids_list(contracted_gaussian, color=vis.get_random_color(idx=i))\n",
-                "\n",
-                "data.append(vis.get_cube(side_length=2.0, color=\"#111111\", opacity=0.05))\n",
-                "data.append(vis.get_cube(side_length=4.0, color=\"#111111\", opacity=0.05))\n",
-                "\n",
-                "fig = go.Figure(data=data, layout=webdocs_layout)\n",
-                "fig.show()"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.8.12 ('nerfactory')",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.12"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/docs/quickstart/custom_dataset.md b/docs/quickstart/custom_dataset.md
deleted file mode 100644
index 6444ca44..00000000
--- a/docs/quickstart/custom_dataset.md
+++ /dev/null
@@ -1,527 +0,0 @@
-# Using custom data
-
-Training model on existing datasets is only so fun. If you would like to train on self captured data you will need to process the data into the nerfstudio format. Specifically we need to know the camera poses for each image.
-
-To process your own data run:
-
-```bash
-ns-process-data {video,images,polycam,record3d} --data {DATA_PATH} --output-dir {PROCESSED_DATA_DIR}
-```
-
-A full set of arguments can be found {doc}`here`.
-
-We currently support the following custom data types:
-| Data | Capture Device | Requirements | `ns-process-data` Speed |
-| ----------------------------- | -------------- | ----------------------------------------------- | ----------------------- |
-| πŸ“· [Images](images_and_video) | Any | [COLMAP](https://colmap.github.io/install.html) | 🐒 |
-| πŸ“Ή [Video](images_and_video) | Any | [COLMAP](https://colmap.github.io/install.html) | 🐒 |
-| 🌎 [360 Data](360_data) | Any | [COLMAP](https://colmap.github.io/install.html) | 🐒 |
-| πŸ“± [Polycam](polycam) | IOS with LiDAR | [Polycam App](https://poly.cam/) | πŸ‡ |
-| πŸ“± [KIRI Engine](kiri) | IOS or Android | [KIRI Engine App](https://www.kiriengine.com/) | πŸ‡ |
-| πŸ“± [Record3D](record3d) | IOS with LiDAR | [Record3D app](https://record3d.app/) | πŸ‡ |
-| πŸ“± [Spectacular AI](spectacularai) | IOS, OAK, others| [App](https://apps.apple.com/us/app/spectacular-rec/id6473188128) / [`sai-cli`](https://www.spectacularai.com/mapping) | πŸ‡ |
-| πŸ–₯ [Metashape](metashape) | Any | [Metashape](https://www.agisoft.com/) | πŸ‡ |
-| πŸ–₯ [RealityCapture](realitycapture) | Any | [RealityCapture](https://www.capturingreality.com/realitycapture) | πŸ‡ |
-| πŸ–₯ [ODM](odm) | Any | [ODM](https://github.com/OpenDroneMap/ODM) | πŸ‡ |
-| πŸ‘“ [Aria](aria) | Aria glasses | [Project Aria](https://projectaria.com/) | πŸ‡ |
-
-(images_and_video)=
-
-## Images or Video
-
-To assist running on custom data we have a script that will process a video or folder of images into a format that is compatible with nerfstudio. We use [COLMAP](https://colmap.github.io) and [FFmpeg](https://ffmpeg.org/download.html) in our data processing script, please have these installed. We have provided a quickstart to installing COLMAP below, FFmpeg can be downloaded from [here](https://ffmpeg.org/download.html)
-
-:::{admonition} Tip
-:class: info
-
-- COLMAP can be finicky. Try your best to capture overlapping, non-blurry images.
-  :::
-
-### Processing Data
-
-```bash
-ns-process-data {images, video} --data {DATA_PATH} --output-dir {PROCESSED_DATA_DIR}
-```
-
-### Training on your data
-
-```bash
-ns-train nerfacto --data {PROCESSED_DATA_DIR}
-```
-
-### Training and evaluation on separate data
-
-For `ns-process-data {images, video}`, you can optionally use a separate image directory or video for training and evaluation, as suggested in [Nerfbusters](https://ethanweber.me/nerfbusters/). To do this, run `ns-process-data {images, video} --data {DATA_PATH} --eval-data {EVAL_DATA_PATH} --output-dir {PROCESSED_DATA_DIR}`. Then when running nerfacto, run `ns-train nerfacto --data {PROCESSED_DATA_DIR} nerfstudio-data --eval-mode filename`.
-
-### Installing COLMAP
-
-There are many ways to install COLMAP, unfortunately it can sometimes be a bit finicky. If the following commands do not work, please refer to the [COLMAP installation guide](https://colmap.github.io/install.html) for additional installation methods. COLMAP install issues are common! Feel free to ask for help in on our [Discord](https://discord.gg/uMbNqcraFc).
-
-::::::{tab-set}
-:::::{tab-item} Linux
-
-We recommend trying `conda`:
-
-```
-conda install -c conda-forge colmap
-```
-
-Check that COLMAP 3.8 with CUDA is successfully installed:
-
-```
-colmap -h
-```
-
-If that doesn't work, you can try VKPG:
-::::{tab-set}
-:::{tab-item} CUDA
-
-```bash
-git clone https://github.com/microsoft/vcpkg
-cd vcpkg
-./bootstrap-vcpkg.sh
-./vcpkg install colmap[cuda]:x64-linux
-```
-
-:::
-:::{tab-item} CPU
-
-```bash
-git clone https://github.com/microsoft/vcpkg
-cd vcpkg
-./bootstrap-vcpkg.sh
-./vcpkg install colmap:x64-linux
-```
-
-:::
-::::
-
-If that doesn't work, you will need to build from source. Refer to the [COLMAP installation guide](https://colmap.github.io/install.html) for details.
-
-:::::
-
-:::::{tab-item} OSX
-
-```bash
-git clone https://github.com/microsoft/vcpkg
-cd vcpkg
-./bootstrap-vcpkg.sh
-./vcpkg install colmap
-```
-
-:::::
-
-:::::{tab-item} Windows
-
-::::{tab-set}
-:::{tab-item} CUDA
-
-```bash
-git clone https://github.com/microsoft/vcpkg
-cd vcpkg
-.\bootstrap-vcpkg.bat
-.\vcpkg install colmap[cuda]:x64-windows
-```
-
-:::
-:::{tab-item} CPU
-
-```bash
-git clone https://github.com/microsoft/vcpkg
-cd vcpkg
-.\bootstrap-vcpkg.sh
-.\vcpkg install colmap:x64-windows
-```
-
-:::
-::::
-
-:::::
-::::::
-
-(polycam)=
-
-## Polycam Capture
-
-Nerfstudio can also be trained directly from captures from the [Polycam app](https://poly.cam//). This avoids the need to use COLMAP. Polycam's poses are globally optimized which make them more robust to drift (an issue with ARKit or SLAM methods).
-
-To get the best results, try to reduce motion blur as much as possible and try to view the target from as many viewpoints as possible. Polycam recommends having good lighting and moving the camera slowly if using auto mode. Or, even better, use the manual shutter mode to capture less blurry images.
-
-:::{admonition} Note
-:class: info
-A LiDAR enabled iPhone or iPad is necessary.
-:::
-
-### Setting up Polycam
-
-```{image} imgs/polycam_settings.png
-:width: 200
-:align: center
-:alt: polycam settings
-```
-
-Developer settings must be enabled in Polycam. To do this, navigate to the settings screen and select `Developer mode`. Note that this will only apply for future captures, you will not be able to process existing captures with nerfstudio.
-
-### Process data
-
-```{image} imgs/polycam_export.png
-:width: 400
-:align: center
-:alt: polycam export options
-```
-
-0. Capture data in LiDAR or Room mode.
-
-1. Tap `Process` to process the data in the Polycam app.
-
-2. Navigate to the export app pane.
-
-3. Select `raw data` to export a `.zip` file.
-
-4. Convert the Polycam data into the nerfstudio format using the following command:
-
-```bash
-ns-process-data polycam --data {OUTPUT_FILE.zip} --output-dir {output directory}
-```
-
-5. Train with nerfstudio!
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(kiri)=
-
-## KIRI Engine Capture
-
-Nerfstudio can trained from data processed by the [KIRI Engine app](https://www.kiriengine.com/). This works for both Android and iPhone and does not require a LiDAR supported device.
-
-:::{admonition} Note
-:class: info
-`ns-process-data` does not need to be run when using KIRI Engine.
-:::
-
-### Setting up KIRI Engine
-
-```{image} imgs/kiri_setup.png
-:width: 400
-:align: center
-:alt: KIRI Engine setup
-```
-
-After downloading the app, `Developer Mode` needs to be enabled. A toggle can be found in the settings menu.
-
-### Process data
-
-```{image} imgs/kiri_capture.png
-:width: 400
-:align: center
-:alt: KIRI Engine setup
-```
-
-1. Navigate to captures window.
-
-2. Select `Dev.` tab.
-
-3. Tap the `+` button to create a new capture.
-
-4. Choose `Camera pose` as the capture option.
-
-5. Capture the scene and provide a name.
-
-6. After processing is complete, export the scene. It will be sent to your email.
-
-7. Unzip the file and run the training script (`ns-process-data` is not necessary).
-
-```bash
-ns-train nerfacto --data {kiri output directory}
-```
-
-(record3d)=
-
-## Record3D Capture
-
-Nerfstudio can be trained directly from >=iPhone 12 Pro captures from the [Record3D app](https://record3d.app/). This uses the iPhone's LiDAR sensors to calculate camera poses, so COLMAP is not needed.
-
-Click on the image down below πŸ‘‡ for a 1-minute tutorial on how to run nerfstudio with Record3D from start to finish.
-
-[![How to easily use nerfstudio with Record3D](imgs/record3d_promo.png)](https://youtu.be/XwKq7qDQCQk 'How to easily use nerfstudio with Record3D')
-
-At a high level, you can follow these 3 steps:
-
-1. Record a video and export with the EXR + JPG sequence format.
-
-  
-  
-
-2. Then, move the exported capture folder from your iPhone to your computer.
-
-3. Convert the data to the nerfstudio format.
-
-```bash
-ns-process-data record3d --data {data directory} --output-dir {output directory}
-```
-
-4. Train with nerfstudio!
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(spectacularai)=
-
-## Spectacular AI
-
-Spectacular AI SDK and apps can be used to capture data from various devices:
-
- * iPhones (with LiDAR)
- * OAK-D cameras
- * RealSense D455/D435i
- * Azure Kinect DK
-
-The SDK also records IMU data, which is fused with camera and (if available) LiDAR/ToF data when computing the camera poses. This approach, VISLAM, is more robust than purely image based methods (e.g., COLMAP) and can work better and faster for difficult data (monotonic environments, fast motions, narrow FoV, etc.).
-
-Instructions:
-
-1. Installation. With the Nerfstudio Conda environment active, first install the Spectacular AI Python library
-
-```bash
-pip install spectacularAI[full]
-```
-
-2. Install FFmpeg. Linux: `apt install ffmpeg` (or similar, if using another package manager). Windows: [see here](https://www.editframe.com/guides/how-to-install-and-start-using-ffmpeg-in-under-10-minutes). FFmpeg must be in your `PATH` so that `ffmpeg` works on the command line.
-
-3. Data capture. See [here for specific instructions for each supported device](https://github.com/SpectacularAI/sdk-examples/tree/main/python/mapping#recording-data).
-  
-4. Process and export. Once you have recorded a dataset in Spectacular AI format and have it stored in `{data directory}` it can be converted into a Nerfstudio supported format with:
-
-```bash
-sai-cli process {data directory} --preview3d --key_frame_distance=0.05 {output directory}
-```
-The optional `--preview3d` flag shows a 3D preview of the point cloud and estimated trajectory live while VISLAM is running. The `--key_frame_distance` argument can be tuned based on the recorded scene size: 0.05 (5cm) is good for small scans and 0.15 for room-sized scans. If the processing gets slow, you can also try adding a --fast flag to `sai-cli process` to trade off quality for speed. 
-
-5. Train. No separate `ns-process-data` step is needed. The data in `{output directory}` can now be trained with Nerfstudio:
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(metashape)=
-
-## Metashape
-
-All images must use the same sensor type (but multiple sensors are supported).
-
-1. Align your images using Metashape. `File -> Workflow -> Align Photos...`
-
-```{image} https://user-images.githubusercontent.com/3310961/203389662-12760210-2b52-49d4-ab21-4f23bfa4a2b3.png
-:width: 400
-:align: center
-:alt: metashape alignment
-```
-
-2. Export the camera alignment as a `xml` file. `File -> Export -> Export Cameras...`
-
-```{image} https://user-images.githubusercontent.com/3310961/203385691-74565704-e4f6-4034-867e-5d8b940fc658.png
-:width: 400
-:align: center
-:alt: metashape export
-```
-
-3. Convert the data to the nerfstudio format.
-
-```bash
-ns-process-data metashape --data {data directory} --xml {xml file} --output-dir {output directory}
-```
-
-4. Train with nerfstudio!
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(realitycapture)=
-
-## RealityCapture
-
-1. Align your images using RealityCapture. `ALIGNMENT -> Align Images`
-
-2. Export the camera alignment as a `csv` file. Choose `Internal/External camera parameters`
-
-3. Convert the data to the nerfstudio format.
-
-```bash
-ns-process-data realitycapture --data {data directory} --csv {csv file} --output-dir {output directory}
-```
-
-4. Train with nerfstudio!
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(odm)=
-
-## ODM
-
-All images/videos must be captured with the same camera.
-
-1. Process a dataset using [ODM](https://github.com/OpenDroneMap/ODM#quickstart)
-
-```bash
-$ ls /path/to/dataset
-images
-odm_report
-odm_orthophoto
-...
-```
-
-2. Convert to nerfstudio format.
-
-```bash
-ns-process-data odm --data /path/to/dataset --output-dir {output directory}
-```
-
-4. Train!
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(aria)=
-
-## Aria
-
-1. Install projectaria_tools:
-
-```bash
-conda activate nerfstudio
-pip install projectaria-tools'[all]'
-```
-
-2. Download a VRS file from Project Aria glasses, and run Machine Perception Services to extract poses.
-
-3. Convert to nerfstudio format.
-
-```bash
-ns-process-data aria --vrs-file /path/to/vrs/file --mps-data-dir /path/to/mps/data --output-dir {output directory}
-```
-
-4. Train!
-
-```bash
-ns-train nerfacto --data {output directory}
-```
-
-(360_data)=
-
-## 360 Data (Equirectangular)
-
-Equirectangular data is data that has been taken by a 360 camera such as Insta360. Both equirectangular image sets and videos can be processed by nerfstudio.
-
-### Images
-
-For a set of equirectangular images, process the data using the following command:
-
-```bash
-ns-process-data images --camera-type equirectangular --images-per-equirect {8, or 14} --crop-factor {top bottom left right} --data {data directory} --output-dir {output directory}
-```
-
-The images-per-equirect argument is the number of images that will be sampled from each equirectangular image. We have found that 8 images per equirectangular image is sufficient for most use cases so it defaults to that. However, if you find that there isn't enough detail in the nerf or that colmap is having trouble aligning the images, you can try increasing the number of images per equirectangular image to 14. See the video section below for details on cropping.
-
-### Videos
-
-For videos we recommend taking a video with the camera held on top of your head. This will result in any unwanted capturer to just be in the bottom of each frame image and therefore can be cropped out.
-
-For a video, process the data using the following command:
-
-```bash
-ns-process-data video --camera-type equirectangular --images-per-equirect {8, or 14} --num-frames-target {num equirectangular frames to sample from} --crop-factor {top bottom left right} --data {data directory} --output-dir {output directory}
-```
-
-See the equirectangular images section above for a description of the `--images-per-equirect` argument.
-
-The `num-frames-target` argument is optional but it is recommended to set it to 3*(seconds of video) frames. For example, if you have a 30 second video, you would use `--num-frames-target 90` (3*30=90). This number was chosen from a bit of experimentation and seems to work well for most videos. It is by no means a hard rule and you can experiment with different values.
-
-The `crop-factor` argument is optional but often very helpful. This is because equirectangular videos taken by 360 cameras tend to have a portion of the bottom of the image that is the person who was holding the camera over their head.
-
-  
-
-The pixels representing the distorted hand and head are obviously not useful in training a nerf so we can remove it by cropping the bottom 20% of the image. This can be done by using the `--crop-factor 0 0.2 0 0` argument.
-
-If cropping only needs to be done from the bottom, you can use the `--crop-bottom [num]` argument which would be the same as doing `--crop-factor 0.0 [num] 0.0 0.0`
-
-## πŸ₯½ Render VR Video
-
-Stereo equirectangular rendering for VR video is supported as VR180 and omni-directional stereo (360 VR) Nerfstudio camera types for video and image rendering. 
-
-### Omni-directional Stereo (360 VR)
-This outputs two equirectangular renders vertically stacked, one for each eye. Omni-directional stereo (ODS) is a method to render VR 3D 360 videos, and may introduce slight depth distortions for close objects. For additional information on how ODS works, refer to this [writeup](https://developers.google.com/vr/jump/rendering-ods-content.pdf).
-
-

-
-

-
-
-### VR180
-This outputs two 180 deg equirectangular renders horizontally stacked, one for each eye. VR180 is a video format for VR 3D 180 videos. Unlike in omnidirectional stereo, VR180 content only displays front facing content. 
-
-

-
-

-
-### Setup instructions
-To render for VR video it is essential to adjust the NeRF to have an approximately true-to-life real world scale (adjustable in the camera path) to ensure that the scene depth and IPD (distance between the eyes) is appropriate for the render to be viewable in VR. You can adjust the scene scale with the [Nerfstudio Blender Add-on](https://docs.nerf.studio/extensions/blender_addon.html) by appropriately scaling a point cloud representation of the NeRF.
-Results may be unviewable if the scale is not set appropriately. The IPD is set at 64mm by default but only is accurate when the NeRF scene is true to scale.
-
-For good quality renders, it is recommended to render at high resolutions (For ODS: 4096x2048 per eye, or 2048x1024 per eye. For VR180: 4096x4096 per eye or 2048x2048 per eye). Render resolutions for a single eye are specified in the camera path. For VR180, resolutions must be in a 1:1 aspect ratio. For ODS, resolutions must be in a 2:1 aspect ratio. The final stacked render output will automatically be constructed (with aspect ratios for VR180 as 2:1 and ODS as 1:1).
-
-:::{admonition} Note
-:class: info
-If you are rendering an image sequence, it is recommended to render as png instead of jpeg, since the png will appear clearer. However, file sizes can be significantly larger with png.
-:::
-
-To render with the VR videos camera:
-1. Use the [Nerfstudio Blender Add-on](https://docs.nerf.studio/extensions/blender_addon.html) to set the scale of the NeRF scene and create the camera path
-    - Export a point cloud representation of the NeRF
-   - Import the point cloud representation in Blender and enable the Nerfstudio Blender Add-on
-    - Create a reference object such as a cube which may be 1x1x1 meter. You could also create a cylinder and scale it to an appropriate height of a viewer.
-    - Now scale the point cloud representation accordingly to match the reference object. This is to ensure that the NeRF scene is scaled as close to real life.
-    - To place the camera at the correct height from the ground in the scene, you can create a cylinder representing the viewer vertically scaled to the viewer’s height, and place the camera at eye level.
-    - Animate the camera movement as needed
-    - Create the camera path JSON file with the Nerfstudio Blender Add-on
-
-2. Edit the JSON camera path file
-
-    **Omni-directional Stereo (360 VR)**
-      - Open the camera path JSON file and specify the `camera_type` as `omnidirectional`
-      - Specify the `render_height` and `render_width` to the resolution of a single eye. The width:height aspect ratio must be 2:1. Recommended resolutions are 4096x2048 or 2048x1024.
-        

-        
-        

-
-
-    **VR180**
-      - Open the camera path JSON file and specify the `camera_type` as `vr180`
-      - Specify the `render_height` and `render_width` to the resolution of a single eye. The width:height aspect ratio must be 1:1. Recommended resolutions are 4096x4096 or 2048x2048.
-      

-      
-      

-
-    - Save the camera path and render the NeRF
-
-
-:::{admonition} Note
-:class: info
-If the depth of the scene is unviewable and looks too close or expanded when viewing the render in VR, the scale of the NeRF may be set too small. If there is almost no discernible depth, the scale of the NeRF may be too large. Getting the right scale may take some experimentation, so it is recommended to either render at a much lower resolution or just one frame to ensure the depth and render is viewable in the VR headset.
-:::
-
-#### Additional Notes
-- Rendering with VR180 or ODS can take significantly longer than traditional renders due to higher resolutions and needing to render a left and right eye view for each frame. Render times may grow exponentially with larger resolutions.
-- When rendering VR180 or ODS content, Nerfstudio will first render the left eye, then the right eye, and finally vertically stack the renders. During this process, Nerfstudio will create a temporary folder to store the left and right eye renders and delete this folder once the final renders are stacked.
-- If rendering content where the camera is stationary for many frames, it is recommended to only render once at that position and extend the time in a video editor since ODS renders can take a lot of time to render.
-- It is recommended to render a preliminary render at a much lower resolution or frame rate to test and ensure that the depth and camera position look accurate in VR.
- - The IPD can be modified in the `cameras.py` script as the variable `vr_ipd` (default is 64 mm).
- - Compositing with Blender Objects and VR180 or ODS Renders
-   - Configure the Blender camera as panoramic and equirectangular. For the VR180 Blender camera, set the panoramic longitude min and max to -90 and 90.
-   - Change the Stereoscopy mode to "Parallel" set the Interocular Distance to 0.064 m. 
diff --git a/docs/quickstart/data_conventions.md b/docs/quickstart/data_conventions.md
deleted file mode 100644
index a929ff53..00000000
--- a/docs/quickstart/data_conventions.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# Data conventions
-
-## Coordinate conventions
-
-Here we explain the coordinate conventions for using our repo.
-
-### Camera/view space
-
-We use the OpenGL/Blender (and original NeRF) coordinate convention for cameras. +X is right, +Y is up, and +Z is pointing back and away from the camera. -Z is the look-at direction. Other codebases may use the COLMAP/OpenCV convention, where the Y and Z axes are flipped from ours but the +X axis remains the same.
-
-### World space
-
-Our world space is oriented such that the up vector is +Z. The XY plane is parallel to the ground plane. In the viewer, you'll notice that red, green, and blue vectors correspond to X, Y, and Z respectively.
-
-

-
-## Dataset format
-
-Our explanation here is for the nerfstudio data format. The `transforms.json` has a similar format to [Instant NGP](https://github.com/NVlabs/instant-ngp).
-
-### Camera intrinsics
-
-If all of the images share the same camera intrinsics, the values can be placed at the top of the file.
-
-```json
-{
-  "camera_model": "OPENCV_FISHEYE", // camera model type [OPENCV, OPENCV_FISHEYE]
-  "fl_x": 1072.0, // focal length x
-  "fl_y": 1068.0, // focal length y
-  "cx": 1504.0, // principal point x
-  "cy": 1000.0, // principal point y
-  "w": 3008, // image width
-  "h": 2000, // image height
-  "k1": 0.0312, // first radial distortion parameter, used by [OPENCV, OPENCV_FISHEYE]
-  "k2": 0.0051, // second radial distortion parameter, used by [OPENCV, OPENCV_FISHEYE]
-  "k3": 0.0006, // third radial distortion parameter, used by [OPENCV_FISHEYE]
-  "k4": 0.0001, // fourth radial distortion parameter, used by [OPENCV_FISHEYE]
-  "p1": -6.47e-5, // first tangential distortion parameter, used by [OPENCV]
-  "p2": -1.37e-7, // second tangential distortion parameter, used by [OPENCV]
-  "frames": // ... per-frame intrinsics and extrinsics parameters
-}
-```
-
-Per-frame intrinsics can also be defined in the `frames` field. If defined for a field (ie. `fl_x`), all images must have per-image intrinsics defined for that field. Per-frame `camera_model` is not supported.
-
-```json
-{
-  // ...
-  "frames": [
-    {
-      "fl_x": 1234
-    }
-  ]
-}
-```
-
-### Camera extrinsics
-
-For a transform matrix, the first 3 columns are the +X, +Y, and +Z defining the camera orientation, and the X, Y, Z values define the origin. The last row is to be compatible with homogeneous coordinates.
-
-```json
-{
-  // ...
-  "frames": [
-    {
-      "file_path": "images/frame_00001.jpeg",
-      "transform_matrix": [
-        // [+X0 +Y0 +Z0 X]
-        // [+X1 +Y1 +Z1 Y]
-        // [+X2 +Y2 +Z2 Z]
-        // [0.0 0.0 0.0 1]
-        [1.0, 0.0, 0.0, 0.0],
-        [0.0, 1.0, 0.0, 0.0],
-        [0.0, 0.0, 1.0, 0.0],
-        [0.0, 0.0, 0.0, 1.0]
-      ]
-      // Additional per-frame info
-    }
-  ]
-}
-```
-
-### Depth images
-
-To train with depth supervision, you can also provide a `depth_file_path` for each frame in your `transforms.json` and use one of the methods that support additional depth losses (e.g., depth-nerfacto). The depths are assumed to be 16-bit or 32-bit and to be in millimeters to remain consistent with [Polyform](https://github.com/PolyCam/polyform). Zero-value in the depth image is treated as unknown depth. You can adjust this scaling factor using the `depth_unit_scale_factor` parameter in `NerfstudioDataParserConfig`. Note that by default, we resize the depth images to match the shape of the RGB images.
-
-```json
-{
-  "frames": [
-    {
-      // ...
-      "depth_file_path": "depth/0001.png"
-    }
-  ]
-}
-```
-
-### Masks
-
-:::{admonition} Warning
-:class: Warning
-
-The current implementation of masking is inefficient and will cause large memory allocations.
-:::
-
-There may be parts of the training image that should not be used during training (ie. moving objects such as people). These images can be masked out using an additional mask image that is specified in the `frame` data.
-
-```json
-{
-  "frames": [
-    {
-      // ...
-      "mask_path": "masks/mask.jpeg"
-    }
-  ]
-}
-```
-
-The following mask requirements must be met:
-
-- Must be 1 channel with only black and white pixels
-- Must be the same resolution as the training image
-- Black corresponds to regions to ignore
-- If used, all images must have a mask
diff --git a/docs/quickstart/existing_dataset.md b/docs/quickstart/existing_dataset.md
deleted file mode 100644
index 81ae0ebc..00000000
--- a/docs/quickstart/existing_dataset.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# Using existing data
-
-Nerfstudio comes with built-in support for a number of datasets, which can be downloaded with the [`ns-download-data` command][cli]. Each of the built-in datasets comes ready to use with various Nerfstudio methods (e.g. the recommended default Nerfacto), allowing you to get started in the blink of an eye.
-
-[cli]: https://docs.nerf.studio/reference/cli/ns_download_data.html
-[paper]: https://arxiv.org/pdf/2302.04264.pdf
-
-## Example
-
-Here are a few examples of downloading different scenes. Please see the [Training Your First NeRF](first_nerf.md) documentation for more details on how to train a model with them.
-
-```bash
-# Download all scenes from the Blender dataset, including the "classic" Lego model
-ns-download-data blender
-
-# Download the subset of data used in the SIGGRAPH 2023 Nerfstudio paper
-ns-download-data nerfstudio --capture-name nerfstudio-dataset
-
-# Download a few room-scale scenes from the EyefulTower dataset at different resolutions
-ns-download-data eyefultower --capture-name riverview seating_area apartment --resolution-name jpeg_1k jpeg_2k
-
-# Download the full D-NeRF dataset of dynamic synthetic scenes
-ns-download-data dnerf
-```
-
-## Dataset Summary
-
-Many of these datasets are used as baselines to evaluate new research in novel view synthesis, such as in the [original Nerfstudio paper][paper]. Scenes from these datasets lie at dramatically different points in the space of images, across axes such as photorealism (synthetic vs real), dynamic range (LDR vs HDR), scale (number of images), and resolution. The tables below describe some of this variation, and hopefully make it easier to pick an appropriate dataset for your research or application.
-
-| Dataset | Synthetic | Real | LDR | HDR | Scenes | Image Count1 | Image Resolution2 |
-| :-: | :-: | :-: | :-: | :-: | :------: | :-: | :-: |
-| [Blender][blender] | βœ”οΈ |  | βœ”οΈ |  | 8 | βž–βž•οΈβž–βž– | βž•οΈβž–βž–βž–βž– |
-| [D-NeRF][dnerf] | βœ”οΈ |  | βœ”οΈ |  | 8 | βž•οΈβž–βž–βž– | βž•οΈβž–βž–βž–βž– |
-| [EyefulTower][eyefultower] |  | βœ”οΈ | βœ”οΈ | βœ”οΈ | 11 | βž–βž•οΈβž•οΈβž•οΈ | βž–βž•οΈβž•οΈβž•οΈβž•οΈ |
-| [Mill 19][mill19] |  | βœ”οΈ | βœ”οΈ |  | 2 | βž–βž–βž•οΈβž– | βž–βž–βž–βž•οΈβž– |
-| [NeRF-OSR][nerfosr] |  | βœ”οΈ | βœ”οΈ |  | 9 | βž•βž•οΈβž•οΈβž– | βž–βž•οΈβž–βž•οΈβž– |
-| [Nerfstudio][nerfstudio] |  | βœ”οΈ | βœ”οΈ |  | 18 | βž•βž•οΈβž•οΈβž– | βž•οΈβž•οΈβž•οΈβž–βž– |
-| [PhotoTourism][phototourism] |  | βœ”οΈ | βœ”οΈ |  | 10 | βž–βž•οΈβž•οΈβž– | βž–βž•οΈβž–βž–βž– |
-| [Record3D][record3d] |  | βœ”οΈ | βœ”οΈ |  | 1 | βž–βž–βž•οΈβž– | βž•οΈβž–βž–βž–βž– |
-| [SDFStudio][sdfstudio] | βœ”οΈ | βœ”οΈ | βœ”οΈ |  | 45 | βž•οΈβž•οΈβž•οΈβž– | βž•οΈβž–βž•οΈβž–βž– |
-| [sitcoms3D][sitcoms3d] |  | βœ”οΈ | βœ”οΈ |  | 10 | βž•οΈβž–βž–βž– | βž•οΈβž•οΈβž–βž–βž– |
-
-In the tables below, each dataset was placed into a bucket based on the table's chosen property. If a box contains a βœ”οΈ, the corresponding dataset will have *at least* one scene falling into the corresponding bucket for that property, though there may be multiple scenes at different points within the range.
-
-
-1: Condensed version of the "Scene Size: Number of RGB Images" table below. 

-2: Condensed version of the "Scene RGB Resolutions: `max(width, height)`" table below.
-
-
-### Scene Size: Number of RGB Images
-
-| Dataset | < 250 | 250 - 999 | 1000 - 3999 | β‰₯ 4000 |
-| :-: | :-: | :-: | :-: | :-: |
-| [Blender][blender] |  | βœ”οΈ |  |  |
-| [D-NeRF][dnerf] | βœ”οΈ |  |  |  |
-| [EyefulTower][eyefultower] |  | βœ”οΈ | βœ”οΈ | βœ”οΈ |
-| [Mill 19][mill19] |  |  | βœ”οΈ |  |
-| [NeRF-OSR][nerfosr] | βœ”οΈ | βœ”οΈ | βœ”οΈ |  |
-| [Nerfstudio][nerfstudio] | βœ”οΈ | βœ”οΈ | βœ”οΈ |  |
-| [PhotoTourism][phototourism] |  | βœ”οΈ | βœ”οΈ |  |
-| [Record3D][record3d] |  |  | βœ”οΈ |  |
-| [SDFStudio][sdfstudio] | βœ”οΈ | βœ”οΈ | βœ”οΈ |  |
-| [sitcoms3D][sitcoms3d] | βœ”οΈ |  |  |
-
-### Scene RGB Resolutions: `max(width, height)`
-
-| Dataset | < 1000 | 1000 - 1999 | 2000 - 3999 | 4000 - 7999 | β‰₯ 8000 |
-| :-: | :-: | :-: | :-: | :-: | :-: |
-| [Blender][blender] | βœ”οΈ |  |  |  |  |
-| [D-NeRF][dnerf] | βœ”οΈ |  |  |  |  |
-| [EyefulTower][eyefultower] |  | βœ”οΈ | βœ”οΈ | βœ”οΈ | βœ”οΈ |
-| [Mill 19][mill19] |  |  |  | βœ”οΈ |  |
-| [NeRF-OSR][nerfosr] |  | βœ”οΈ |  | βœ”οΈ |  |
-| [Nerfstudio][nerfstudio] | βœ”οΈ | βœ”οΈ | βœ”οΈ |  |  |
-| [PhotoTourism][phototourism] |  | βœ”οΈ |  |  |  |
-| [Record3D][record3d] | βœ”οΈ |  |  |  |  |
-| [SDFStudio][sdfstudio] | βœ”οΈ |  | βœ”οΈ |  |  |
-| [sitcoms3D][sitcoms3d] | βœ”οΈ | βœ”οΈ |  |  |  |
-
-[blender]: https://github.com/bmild/nerf?tab=readme-ov-file#project-page--video--paper--data
-[dnerf]: https://github.com/albertpumarola/D-NeRF?tab=readme-ov-file#download-dataset
-[eyefultower]: https://github.com/facebookresearch/EyefulTower
-[mill19]: https://github.com/cmusatyalab/mega-nerf?tab=readme-ov-file#mill-19
-[nerfosr]: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/
-[nerfstudio]: https://github.com/nerfstudio-project/nerfstudio
-[phototourism]: https://www.cs.ubc.ca/~kmyi/imw2020/data.html
-[record3d]: https://record3d.app/
-[sdfstudio]: https://github.com/autonomousvision/sdfstudio/blob/master/docs/sdfstudio-data.md#Existing-dataset
-[sitcoms3d]: https://github.com/ethanweber/sitcoms3D/blob/master/METADATA.md
diff --git a/docs/quickstart/export_geometry.md b/docs/quickstart/export_geometry.md
deleted file mode 100644
index 2ffd5189..00000000
--- a/docs/quickstart/export_geometry.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Export geometry
-
-Here we document how to export point clouds and meshes from nerfstudio. The main command you'll be working with is `ns-export`. Our point clouds are exported as `.ply` files and the textured meshes are exported as `.obj` files.
-
-## Exporting a mesh
-
-### 1. TSDF Fusion
-
-TSDF (truncated signed distance function) Fusion is a meshing algorithm that uses depth maps to extract a surface as a mesh. This method works for all models.
-
-```python
-ns-export tsdf --load-config CONFIG.yml --output-dir OUTPUT_DIR
-```
-
-### 2. Poisson surface reconstruction
-
-Poisson surface reconstruction gives the highest quality meshes. See the steps below to use Poisson surface reconstruction in our repo.
-
-> **Note:**
-> This will only work with a Model that computes or predicts normals, e.g., nerfacto.
-
-1. Train nerfacto with network settings that predict normals.
-
-```bash
-ns-train nerfacto --pipeline.model.predict-normals True
-```
-
-2. Export a mesh with the Poisson meshing algorithm.
-
-```bash
-ns-export poisson --load-config CONFIG.yml --output-dir OUTPUT_DIR
-```
-
-## Exporting a point cloud
-
-```bash
-ns-export pointcloud --help
-```
-
-## Other exporting methods
-
-Run the following command to see other export methods that may exist.
-
-```python
-ns-export --help
-```
-
-## Texturing an existing mesh with NeRF
-
-Say you want to simplify and/or smooth a mesh offline, and then you want to texture it with NeRF. You can do that with the following command. It will work for any mesh filetypes that [PyMeshLab](https://pymeshlab.readthedocs.io/en/latest/) can support, for example a `.ply`.
-
-```python
-python nerfstudio/scripts/texture.py --load-config CONFIG.yml --input-mesh-filename FILENAME --output-dir OUTPUT_DIR
-```
-
-## Dependencies
-
-Our dependencies are shipped with the pip package in the pyproject.toml file. These are the following:
-
-- [xatlas-python](https://github.com/mworchel/xatlas-python) for unwrapping meshes to a UV map
-- [pymeshlab](https://pymeshlab.readthedocs.io/en/latest/) for reducing the number of faces in a mesh
diff --git a/docs/quickstart/first_nerf.md b/docs/quickstart/first_nerf.md
deleted file mode 100644
index 67a84783..00000000
--- a/docs/quickstart/first_nerf.md
+++ /dev/null
@@ -1,171 +0,0 @@
-# Training your first model
-
-## Train and run viewer
-
-The following will train a _nerfacto_ model, our recommended model for real world scenes.
-
-```bash
-# Download some test data:
-ns-download-data nerfstudio --capture-name=poster
-# Train model
-ns-train nerfacto --data data/nerfstudio/poster
-```
-
-If everything works, you should see training progress like the following:
-
-

-    image
-

-
-Navigating to the link at the end of the terminal will load the webviewer. If you are running on a remote machine, you will need to port forward the websocket port (defaults to 7007).
-
-

-    image
-

-
-:::{admonition} Note
-:class: note
-
-- You may have to change the port using `--viewer.websocket-port`.
-- All data configurations must go at the end. In this case, `nerfstudio-data` and all of its corresponding configurations come at the end after the model and viewer specification.
-  :::
-
-## Resume from checkpoint
-
-It is possible to load a pretrained model by running
-
-```bash
-ns-train nerfacto --data data/nerfstudio/poster --load-dir {outputs/.../nerfstudio_models}
-```
-
-## Visualize existing run
-
-Given a pretrained model checkpoint, you can start the viewer by running
-
-```bash
-ns-viewer --load-config {outputs/.../config.yml}
-```
-
-## Exporting Results
-
-Once you have a NeRF model you can either render out a video or export a point cloud.
-
-### Render Video
-
-First we must create a path for the camera to follow. This can be done in the viewer under the "RENDER" tab. Orient your 3D view to the location where you wish the video to start, then press "ADD CAMERA". This will set the first camera key frame. Continue to new viewpoints adding additional cameras to create the camera path. We provide other parameters to further refine your camera path. Once satisfied, press "RENDER" which will display a modal that contains the command needed to render the video. Kill the training job (or create a new terminal if you have lots of compute) and run the command to generate the video.
-
-Other video export options are available, learn more by running
-
-```bash
-ns-render --help
-```
-
-### Generate Point Cloud
-
-While NeRF models are not designed to generate point clouds, it is still possible. Navigate to the "EXPORT" tab in the 3D viewer and select "POINT CLOUD". If the crop option is selected, everything in the yellow square will be exported into a point cloud. Modify the settings as desired then run the command at the bottom of the panel in your command line.
-
-Alternatively you can use the CLI without the viewer. Learn about the export options by running
-
-```bash
-ns-export pointcloud --help
-```
-
-## Intro to nerfstudio CLI and Configs
-
-Nerfstudio allows customization of training and eval configs from the CLI in a powerful way, but there are some things to understand.
-
-The most demonstrative and helpful example of the CLI structure is the difference in output between the following commands:
-
-The following will list the supported models
-
-```bash
-ns-train --help
-```
-
-Applying `--help` after the model specification will provide the model and training specific arguments.
-
-```bash
-ns-train nerfacto --help
-```
-
-At the end of the command you can specify the dataparser used. By default we use the _nerfstudio-data_ dataparser. We include other dataparsers such as _Blender_, _NuScenes_, ect. For a list of dataparse specific arguments, add `--help` to the end of the command
-
-```bash
-ns-train nerfacto  nerfstudio-data --help
-```
-
-Each script will have some other minor quirks (like the training script dataparser subcommand needing to come after the model subcommand), read up on them [here](../reference/cli/index.md).
-
-## Comet / Tensorboard / WandB / Viewer
-
-We support four different methods to track training progress, using the viewer [tensorboard](https://www.tensorflow.org/tensorboard), [Weights and Biases](https://wandb.ai/site), and [Comet](https://comet.com/?utm_source=nerf&utm_medium=referral&utm_content=nerf_docs). You can specify which visualizer to use by appending `--vis {viewer, tensorboard, wandb, comet, viewer+wandb, viewer+tensorboard, viewer+comet}` to the training command. Simultaneously utilizing the viewer alongside wandb or tensorboard may cause stuttering issues during evaluation steps. The viewer only works for methods that are fast (ie. nerfacto, instant-ngp), for slower methods like NeRF, use the other loggers.
-
-## Evaluating Runs
-
-Calculate the psnr of your trained model and save to a json file.
-
-```bash
-ns-eval --load-config={PATH_TO_CONFIG} --output-path=output.json
-```
-
-We also provide a train and evaluation script that allows you to do benchmarking on the classical Blender dataset (see our [benchmarking workflow](../developer_guides/debugging_tools/benchmarking.md)).
-
-## Multi-GPU Training
-
-Here we explain how to use multi-GPU training. We are using [PyTorch Distributed Data Parallel (DDP)](https://pytorch.org/tutorials/beginner/dist_overview.html), so gradients are averaged over devices. If the loss scales depend on sample size (usually not the case in our implementation), we need to scale the learning rate with the number of GPUs used. Plotting will only be done for the first process. Note that you may want to play around with both learning rate and `_num_rays_per_batch` when using DDP. Below is a simple example for how you'd run the `nerfacto-big` method on the aspen scene (see above to download the data), with either 1 or 2 GPUs. The `nerfacto-big` method uses a larger model size than the `nerfacto` method, so it benefits more from multi-GPU training.
-
-First, download the aspen scene.
-
-```python
-ns-download-data nerfstudio --capture-name=aspen
-```
-
-```python
-# 1 GPU (8192 rays per GPU per batch)
-export CUDA_VISIBLE_DEVICES=0
-ns-train nerfacto-big --vis viewer+wandb --machine.num-devices 1 --pipeline.datamanager.train-num-rays-per-batch 4096 --data data/nerfstudio/aspen
-```
-
-You would observe about ~70k rays / sec on NVIDIA V100.
-
-```
-Step (% Done)       Train Iter (time)    ETA (time)           Train Rays / Sec
------------------------------------------------------------------------------------
-610 (0.61%)         115.968 ms           3 h, 12 m, 6 s       72.68 K
-620 (0.62%)         115.908 ms           3 h, 11 m, 58 s      72.72 K
-630 (0.63%)         115.907 ms           3 h, 11 m, 57 s      72.73 K
-640 (0.64%)         115.937 ms           3 h, 11 m, 59 s      72.71 K
-650 (0.65%)         115.853 ms           3 h, 11 m, 49 s      72.76 K
-660 (0.66%)         115.710 ms           3 h, 11 m, 34 s      72.85 K
-670 (0.67%)         115.797 ms           3 h, 11 m, 42 s      72.80 K
-680 (0.68%)         115.783 ms           3 h, 11 m, 39 s      72.81 K
-690 (0.69%)         115.756 ms           3 h, 11 m, 35 s      72.81 K
-700 (0.70%)         115.755 ms           3 h, 11 m, 34 s      72.81 K
-```
-
-By having more GPUs in the training, you can allocate batch size to multiple GPUs and average their gradients.
-
-```python
-# 2 GPUs (4096 rays per GPU per batch, effectively 8192 rays per batch)
-export CUDA_VISIBLE_DEVICES=0,1
-ns-train nerfacto --vis viewer+wandb --machine.num-devices 2 --pipeline.datamanager.train-num-rays-per-batch 4096 --data data/nerfstudio/aspen
-```
-
-You would get improved throughput (~100k rays / sec on two NVIDIA V100).
-
-```
-Step (% Done)       Train Iter (time)    ETA (time)           Train Rays / Sec
------------------------------------------------------------------------------------
-1910 (1.91%)        79.623 ms            2 h, 10 m, 10 s      104.92 K
-1920 (1.92%)        79.083 ms            2 h, 9 m, 16 s       105.49 K
-1930 (1.93%)        79.092 ms            2 h, 9 m, 16 s       105.48 K
-1940 (1.94%)        79.364 ms            2 h, 9 m, 42 s       105.21 K
-1950 (1.95%)        79.327 ms            2 h, 9 m, 38 s       105.25 K
-1960 (1.96%)        79.473 ms            2 h, 9 m, 51 s       105.09 K
-1970 (1.97%)        79.334 ms            2 h, 9 m, 37 s       105.26 K
-1980 (1.98%)        79.200 ms            2 h, 9 m, 23 s       105.38 K
-1990 (1.99%)        79.264 ms            2 h, 9 m, 28 s       105.29 K
-2000 (2.00%)        79.168 ms            2 h, 9 m, 18 s       105.40 K
-```
-
-During training, the "Train Rays / Sec" throughput represents the total number of training rays it processes per second, gradually increase the number of GPUs and observe how this throughput improves and eventually saturates.
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+++ /dev/null
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-# Installation
-
-## Prerequisites
-
-::::::{tab-set}
-:::::{tab-item} Linux
-
-Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
-
-:::::
-:::::{tab-item} Windows
-
-Install [Git](https://git-scm.com/downloads).
-
-Install Visual Studio 2022. This must be done before installing CUDA. The necessary components are included in the `Desktop Development with C++` workflow (also called `C++ Build Tools` in the BuildTools edition).
-
-Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
-
-:::::
-::::::
-
-## Create environment
-
-```bash
-conda create --name nerfstudio -y python=3.8
-conda activate nerfstudio
-python -m pip install --upgrade pip
-
-```
-
-## Dependencies
-
-(pytorch)=
-
-### PyTorch
-
-Note that if a PyTorch version prior to 2.0.1 is installed,
-the previous version of pytorch, functorch, and tiny-cuda-nn should be uninstalled.
-
-```bash
-pip uninstall torch torchvision functorch tinycudann
-```
-
-::::{tab-set}
-:::{tab-item} Torch 2.1.2 with CUDA 11.8 (recommended)
-
-Install PyTorch 2.1.2 with CUDA 11.8:
-
-```bash
-pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
-```
-
-To build the necessary CUDA extensions, `cuda-toolkit` is also required. We
-recommend installing with conda:
-
-```bash
-conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
-```
-
-:::
-:::{tab-item} Torch 2.0.1 with CUDA 11.7
-
-Install PyTorch 2.0.1 with CUDA 11.7:
-
-```bash
-pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
-```
-
-To build the necessary CUDA extensions, `cuda-toolkit` is also required. We
-recommend installing with conda:
-
-```bash
-conda install -c "nvidia/label/cuda-11.7.1" cuda-toolkit
-```
-
-:::
-::::
-
-### tiny-cuda-nn
-
-After pytorch and ninja, install the torch bindings for tiny-cuda-nn:
-
-```bash
-pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
-```
-
-## Installing nerfstudio
-
-**From pip**
-
-```bash
-pip install nerfstudio
-```
-
-**From source**
-Optional, use this command if you want the latest development version.
-
-```bash
-git clone https://github.com/nerfstudio-project/nerfstudio.git
-cd nerfstudio
-pip install --upgrade pip setuptools
-pip install -e .
-```
-
-:::{admonition} Note
-:class: info
-Below are optional installations, but makes developing with nerfstudio much more convenient.
-:::
-
-**Tab completion (bash & zsh)**
-
-This needs to be rerun when the CLI changes, for example if nerfstudio is updated.
-
-```bash
-ns-install-cli
-```
-
-**Development packages**
-
-```bash
-pip install -e .[dev]
-pip install -e .[docs]
-```
-
-## Use docker image
-
-Instead of installing and compiling prerequisites, setting up the environment and installing dependencies, a ready to use docker image is provided.
-
-### Prerequisites
-
-Docker ([get docker](https://docs.docker.com/get-docker/)) and nvidia GPU drivers ([get nvidia drivers](https://www.nvidia.de/Download/index.aspx?lang=de)), capable of working with CUDA 11.8, must be installed.
-The docker image can then either be pulled from [here](https://hub.docker.com/r/dromni/nerfstudio/tags) (replace  with the actual version, e.g. 0.1.18)
-
-```bash
-docker pull dromni/nerfstudio:
-```
-
-or be built from the repository using
-
-```bash
-docker build --tag nerfstudio -f Dockerfile .
-```
-
-To restrict to only CUDA architectures that you have available locally, use the `CUDA_ARCHITECTURES`
-build arg and look up [the compute capability for your GPU](https://developer.nvidia.com/cuda-gpus).
-For example, here's how to build with support for GeForce 30xx series GPUs:
-
-```bash
-docker build \
-    --build-arg CUDA_VERSION=11.8.0 \
-    --build-arg CUDA_ARCHITECTURES=86 \
-    --build-arg OS_VERSION=22.04 \
-    --tag nerfstudio-86 \
-    --file Dockerfile .
-```
-
-The user inside the container is called 'user' and is mapped to the local user with ID 1000 (usually the first non-root user on Linux systems).  
-If you suspect that your user might have a different id, override `USER_ID` during the build as follows:
-
-```bash
-docker build \
-    --build-arg USER_ID=$(id -u) \
-    --file Dockerfile .
-```
-
-### Using an interactive container
-
-The docker container can be launched with an interactive terminal where nerfstudio commands can be entered as usual. Some parameters are required and some are strongly recommended for usage as following:
-
-```bash
-docker run --gpus all \                                         # Give the container access to nvidia GPU (required).
-            -u $(id -u) \                                       # To prevent abusing of root privilege, please use custom user privilege to start.
-            -v /folder/of/your/data:/workspace/ \               # Mount a folder from the local machine into the container to be able to process them (required).
-            -v /home//.cache/:/home/user/.cache/ \   # Mount cache folder to avoid re-downloading of models everytime (recommended).
-            -p 7007:7007 \                                      # Map port from local machine to docker container (required to access the web interface/UI).
-            --rm \                                              # Remove container after it is closed (recommended).
-            -it \                                               # Start container in interactive mode.
-            --shm-size=12gb \                                   # Increase memory assigned to container to avoid memory limitations, default is 64 MB (recommended).
-            dromni/nerfstudio:                             # Docker image name if you pulled from docker hub.
-            <--- OR --->
-            nerfstudio                                          # Docker image tag if you built the image from the Dockerfile by yourself using the command from above.
-```
-
-### Call nerfstudio commands directly
-
-Besides, the container can also directly be used by adding the nerfstudio command to the end.
-
-```bash
-docker run --gpus all -u $(id -u) -v /folder/of/your/data:/workspace/ -v /home//.cache/:/home/user/.cache/ -p 7007:7007 --rm -it --shm-size=12gb  # Parameters.
-            dromni/nerfstudio: \                           # Docker image name
-            ns-process-data video --data /workspace/video.mp4   # Smaple command of nerfstudio.
-```
-
-### Note
-
-- The container works on Linux and Windows, depending on your OS some additional setup steps might be required to provide access to your GPU inside containers.
-- Paths on Windows use backslash '\\' while unix based systems use a frontslash '/' for paths, where backslashes might require an escape character depending on where they are used (e.g. C:\\\\folder1\\\\folder2...). Alternatively, mounts can be quoted (e.g. `-v 'C:\local_folder:/docker_folder'`). Ensure to use the correct paths when mounting folders or providing paths as parameters.
-- Always use full paths, relative paths are known to create issues when being used in mounts into docker.
-- Everything inside the container, what is not in a mounted folder (workspace in the above example), will be permanently removed after destroying the container. Always do all your tasks and output folder in workdir!
-- The container currently is based on nvidia/cuda:11.8.0-devel-ubuntu22.04, consequently it comes with CUDA 11.8 which must be supported by the nvidia driver. No local CUDA installation is required or will be affected by using the docker image.
-- The docker image (respectively Ubuntu 22.04) comes with Python3.10, no older version of Python is installed.
-- If you call the container with commands directly, you still might want to add the interactive terminal ('-it') flag to get live log outputs of the nerfstudio scripts. In case the container is used in an automated environment the flag should be discarded.
-- The current version of docker is built for multi-architecture (CUDA architectures) use. The target architecture(s) must be defined at build time for Colmap and tinyCUDNN to be able to compile properly. If your GPU architecture is not covered by the following table you need to replace the number in the line `ARG CUDA_ARCHITECTURES=90;89;86;80;75;70;61;52;37` to your specific architecture. It also is a good idea to remove all architectures but yours (e.g. `ARG CUDA_ARCHITECTURES=86`) to speedup the docker build process a lot.
-- To avoid memory issues or limitations during processing, it is recommended to use either `--shm-size=12gb` or `--ipc=host` to increase the memory available to the docker container. 12gb as in the example is only a suggestion and may be replaced by other values depending on your hardware and requirements.
-
-**Currently supported CUDA architectures in the docker image**
-
-(tiny-cuda-arch-list)=
-
-| GPU             | CUDA arch |
-| --------------- | --------- |
-| H100            | 90        |
-| 40X0            | 89        |
-| 30X0            | 86        |
-| A100            | 80        |
-| 20X0            | 75        |
-| TITAN V / V100  | 70        |
-| 10X0 / TITAN Xp | 61        |
-| 9X0             | 52        |
-| K80             | 37        |
-
-## Installation FAQ
-
-- [ImportError: DLL load failed while importing \_89_C](tiny-cuda-mismatch-arch)
-- [tiny-cuda-nn installation errors out with cuda mismatch](tiny-cuda-mismatch-error)
-- [tiny-cuda-nn installation errors out with no CUDA toolset found](tiny-cuda-integration-error)
-- [Installation errors, File "setup.py" not found](pip-install-error)
-- [Runtime errors, "len(sources) > 0".](cuda-sources-error)
-
- 

-
-(tiny-cuda-mismatch-arch)=
-
-**ImportError: DLL load failed while importing \_89_C**
-
-This occurs with certain GPUs that have CUDA architecture versions (89 in the example above) for which tiny-cuda-nn does not automatically compile support.
-
-**Solution**:
-
-Reinstall tiny-cuda-nn with the following command:
-
-::::::{tab-set}
-:::::{tab-item} Linux
-
-```bash
-TCNN_CUDA_ARCHITECTURES=XX pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
-```
-
-:::::
-:::::{tab-item} Windows
-
-```bash
-set TCNN_CUDA_ARCHITECTURES=XX
-pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
-```
-
-:::::
-::::::
-
-Where XX is the architecture version listed [here](tiny-cuda-arch-list). Ie. for a 4090 GPU use `TCNN_CUDA_ARCHITECTURES=89`
-
- 

-
-(tiny-cuda-mismatch-error)=
-
-**tiny-cuda-nn installation errors out with cuda mismatch**
-
-While installing tiny-cuda, you run into: `The detected CUDA version mismatches the version that was used to compile PyTorch (10.2). Please make sure to use the same CUDA versions.`
-
-**Solution**:
-
-Reinstall PyTorch with the correct CUDA version.
-See [pytorch](pytorch) under Dependencies, above.
-
- 

-
-(tiny-cuda-integration-error)=
-
-**(Windows) tiny-cuda-nn installation errors out with no CUDA toolset found**
-
-While installing tiny-cuda on Windows, you run into: `No CUDA toolset found.`
-
-**Solution**:
-
-Confirm that you have Visual Studio installed.
-
-Make sure CUDA Visual Studio integration is enabled. This should be done automatically by the CUDA installer if it is run after Visual Studio is installed. You can also manually enable integration.
-
-::::{tab-set}
-:::{tab-item} Visual Studio 2019
-
-To manually enable integration for Visual Studio 2019, copy all 4 files from
-
-```
-C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\extras\visual_studio_integration\MSBuildExtensions
-```
-
-to
-
-```
-C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\MSBuild\Microsoft\VC\v160\BuildCustomizations
-```
-
-:::
-:::{tab-item} Visual Studio 2022
-
-To manually enable integration for Visual Studio 2022, copy all 4 files from
-
-```
-C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\extras\visual_studio_integration\MSBuildExtensions
-```
-
-to
-
-```
-C:\Program Files\Microsoft Visual Studio\2022\[Community, Professional, Enterprise, or BuildTools]\MSBuild\Microsoft\VC\v160\BuildCustomizations
-```
-
-:::
-::::
-
- 

-
-(pip-install-error)=
-
-**Installation errors, File "setup.py" not found**
-
-When installing dependencies and nerfstudio with `pip install -e .`, you run into: `ERROR: File "setup.py" not found. Directory cannot be installed in editable mode`
-
-**Solution**:
-This can be fixed by upgrading pip to the latest version:
-
-```
-python -m pip install --upgrade pip
-```
-
- 

-
-(cuda-sources-error)=
-
-**Runtime errors: "len(sources) > 0", "ctype = \_C.ContractionType(type.value) ; TypeError: 'NoneType' object is not callable".**
-
-When running `train.py `, an error occurs when installing cuda files in the backend code.
-
-**Solution**:
-This is a problem with not being able to detect the correct CUDA version, and can be fixed by updating the CUDA path environment variables:
-
-```
-export CUDA_HOME=/usr/local/cuda
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
-export PATH=$PATH:$CUDA_HOME/bin
-```
diff --git a/docs/quickstart/viewer_quickstart.rst b/docs/quickstart/viewer_quickstart.rst
deleted file mode 100644
index 3e50b582..00000000
--- a/docs/quickstart/viewer_quickstart.rst
+++ /dev/null
@@ -1,95 +0,0 @@
-Using the viewer
-================
-
-The nerfstudio web-based viewer makes it easy to view training in real-time, and to create content videos from your trained models 🌟!
-
-Connecting to the viewer
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-The nerfstudio viewer is launched automatically with each :code:`ns-train` training run! It can also be run separately with :code:`ns-viewer`.
-To access a viewer, we need to enter the server's address and port in a web browser.
-
-Accessing on a local machine
-""""""""""""""""""""""""""""
-
-You should be able to click the link obtained while running a script to open the viewer in your browser. This should typically look something like :code:`http://localhost:7007`.
-
-Accessing over an SSH connection
-""""""""""""""""""""""""""""""""
-
-If you are training on a remote machine, the viewer will still let you view your trainings. You will need to forward traffic from your viewing host to the listening port on the host running ns-train (7007 by default). You can achieve this by securely tunneling traffic on the specified port through an ssh session. In a terminal window on the viewing host, issue the following command:
-
-..  code-block:: bash
-
-   ssh -L 7007:127.0.0.1:7007 @
-
-
-..  admonition:: Note
-
-    You can now simply open the link (same one shown in image above) in your browser on your local machine and it should connect!. So long as you don't close this terminal window with this specific active ssh connection, the port will remain open for all your other ssh sessions.
-
-    For example, if you do this in a new terminal window, any existing ssh sessions (terminal windows, VSCode remote connection, etc) will still be able to access the port, but if you close this terminal window, the port will be closed.
-
-
-..  warning::
-    If the port is being used, you will need to switch the port using the `--viewer.websocket-port` flag tied to the model subcommand.
-
-
-Accessing via a share link
-""""""""""""""""""""""""""
-
-To connect to remote machines without requiring an SSH connection, we also support creating share URLs.
-This can be generated from the "Share:" icon in the GUI, or from the CLI by specifying the :code:`--viewer.make-share-url True` argument.
-This will create a publically accessible link that you can share with others to view your training progress.
-This is useful for sharing training progress with collaborators, or for viewing training progress on a remote machine without requiring an SSH connection.
-However, it will introduce some latency in the viewer.
-
-
-..  seealso::
-
-  For a more in-depth developer overview on how to hack with the viewer, see our `developer guide `_
-
-
-Legacy viewer tutorial video
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In the tutorial video below, we walk you through how you can turn a simple capture into a 3D video πŸ“Έ 🎬
-
-This video is for the legacy viewer, which was the default in :code:`nerfstudio<1.0`. It has some visual differences from the current viewer. However, the core functionality is the same.
-The legacy viewer can still be enabled with the :code:`--vis viewer_legacy` option, for example, :code:`ns-train nerfacto --vis viewer_legacy`.
-
-.. raw:: html
-
-   
-
-For specific sections, click the links below to navigate to the associated portion of the video.
-
-Getting started
-"""""""""""""""
-
-* `Hello from nerfstudio `_
-* `Preprocess your video `_
-* `Launching training and viewer `_
-
-Viewer basics
-"""""""""""""""
-
-* `Viewer scene introduction `_
-* `Moving around in viewer `_
-* `Overview of Controls Panel - train speed/output options `_
-* `Overview of Scene Panel - toggle visibility `_
-
-Creating camera trajectories
-""""""""""""""""""""""""""""
-
-* `Creating a custom camera path `_
-* `Camera spline options - cycle, speed, smoothness `_
-* `Camera options - move, add, view `_
-
-Rendering a video
-"""""""""""""""""
-
-* `Preview camera trajectory `_
-* `How to render final video `_
-
-|
diff --git a/docs/reference/api/cameras.rst b/docs/reference/api/cameras.rst
deleted file mode 100644
index 7653852c..00000000
--- a/docs/reference/api/cameras.rst
+++ /dev/null
@@ -1,46 +0,0 @@
-.. _cameras:
-
-Cameras
-============
-
-Cameras
-----------------
-
-.. automodule:: nerfstudio.cameras.cameras
-   :members:
-   :show-inheritance:
-
-Camera Optimizers
------------------
-
-.. automodule:: nerfstudio.cameras.camera_optimizers
-   :members:
-   :show-inheritance:
-
-Camera Paths
-----------------
-
-.. automodule:: nerfstudio.cameras.camera_paths
-   :members:
-   :show-inheritance:
-
-Camera Utils
-----------------
-
-.. automodule:: nerfstudio.cameras.camera_utils
-   :members:
-   :show-inheritance:
-
-Lie Groups
-----------------
-
-.. automodule:: nerfstudio.cameras.lie_groups
-   :members:
-   :show-inheritance:
-
-Rays
-----------------
-
-.. automodule:: nerfstudio.cameras.rays
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/config.rst b/docs/reference/api/config.rst
deleted file mode 100644
index d5885a22..00000000
--- a/docs/reference/api/config.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _configs:
-
-Configs
-============
-
-.. automodule:: nerfstudio.configs.base_config
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/data/datamanagers.rst b/docs/reference/api/data/datamanagers.rst
deleted file mode 100644
index 43e5d6cc..00000000
--- a/docs/reference/api/data/datamanagers.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-.. _datamanagers:
-
-Datamanagers
-============
-
-Base
-----------------
-
-.. automodule:: nerfstudio.data.datamanagers.base_datamanager
-   :members:
\ No newline at end of file
diff --git a/docs/reference/api/data/dataparsers.rst b/docs/reference/api/data/dataparsers.rst
deleted file mode 100644
index e35f83ea..00000000
--- a/docs/reference/api/data/dataparsers.rst
+++ /dev/null
@@ -1,117 +0,0 @@
-.. _dataparser:
-
-Data Parsers
-============
-
-* `Base Data Parser`_
-* `ARKitScenes`_
-* `Blender`_
-* `D-NeRF`_
-* `dycheck`_
-* `Instant-NGP`_
-* `Minimal`_
-* `NeRF-OSR`_
-* `Nerfstudio`_
-* `nuScenes`_
-* `Phototourism`_
-* `ScanNet`_
-* `SDFStudio`_
-* `sitcoms3D`_
-
-
-Base Data Parser
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.base_dataparser
-   :members:
-
-ARKitScenes
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.arkitscenes_dataparser
-   :members:
-   :show-inheritance:
-
-Blender
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.blender_dataparser
-   :members:
-   :show-inheritance:
-
-D-NeRF
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.dnerf_dataparser
-   :members:
-   :show-inheritance:
-
-dycheck
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.dycheck_dataparser
-   :members:
-   :show-inheritance:
-
-Instant-NGP
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.instant_ngp_dataparser
-   :members:
-   :show-inheritance:
-
-Minimal
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.minimal_dataparser
-   :members:
-   :show-inheritance:
-
-NeRF-OSR
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.nerfosr_dataparser
-   :members:
-   :show-inheritance:
-
-Nerfstudio
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.nerfstudio_dataparser
-   :members:
-   :show-inheritance:
-
-nuScenes
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.nuscenes_dataparser
-   :members:
-   :show-inheritance:
-
-Phototourism
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.phototourism_dataparser
-   :members:
-   :show-inheritance:
-
-ScanNet
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.scannet_dataparser
-   :members:
-   :show-inheritance:
-
-SDFStudio
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.sdfstudio_dataparser
-   :members:
-   :show-inheritance:
-
-sitcoms3D
-----------------
-
-.. automodule:: nerfstudio.data.dataparsers.sitcoms3d_dataparser
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/data/datasets.rst b/docs/reference/api/data/datasets.rst
deleted file mode 100644
index ee4fd1ab..00000000
--- a/docs/reference/api/data/datasets.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-.. _datasets:
-
-Datasets
-============
-
-Base
-----------------
-
-.. automodule:: nerfstudio.data.datasets.base_dataset
-   :members:
-
-SDF Dataset
-----------------
-
-.. automodule:: nerfstudio.data.datasets.sdf_dataset
-   :members:
-   :show-inheritance:
-
-Semantic Dataset
-----------------
-
-.. automodule:: nerfstudio.data.datasets.semantic_dataset
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/data/index.rst b/docs/reference/api/data/index.rst
deleted file mode 100644
index 6e10f938..00000000
--- a/docs/reference/api/data/index.rst
+++ /dev/null
@@ -1,26 +0,0 @@
-.. _dataset:
-
-Data
-============
-
-.. toctree::
-   :titlesonly:
-
-   dataparsers
-   datamanagers
-   datasets
-   utils
-
-Pixel Samplers
-----------------
-
-.. automodule:: nerfstudio.data.pixel_samplers
-   :members:
-   :show-inheritance:
-
-Scene Box
-----------------
-
-.. automodule:: nerfstudio.data.scene_box
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/data/utils.rst b/docs/reference/api/data/utils.rst
deleted file mode 100644
index 5f225e6d..00000000
--- a/docs/reference/api/data/utils.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-.. _datautils:
-
-Utils
-============
-
-Base
-----------------
-
-.. automodule:: nerfstudio.data.utils.colmap_parsing_utils
-   :members:
-
-Data
-----------------
-
-.. automodule:: nerfstudio.data.utils.data_utils
-   :members:
-
-Dataloader
-----------------
-
-.. automodule:: nerfstudio.data.utils.dataloaders
-   :members:
-
-Nerfstudio Collate
--------------------
-
-.. automodule:: nerfstudio.data.utils.nerfstudio_collate
-   :members:
\ No newline at end of file
diff --git a/docs/reference/api/field_components/embeddings.rst b/docs/reference/api/field_components/embeddings.rst
deleted file mode 100644
index f4bd2b67..00000000
--- a/docs/reference/api/field_components/embeddings.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _embeddings:
-
-Embeddings
-===================
-
-.. automodule:: nerfstudio.field_components.embedding
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/field_components/encodings.rst b/docs/reference/api/field_components/encodings.rst
deleted file mode 100644
index 2fef31ad..00000000
--- a/docs/reference/api/field_components/encodings.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _encodings:
-
-Encodings
-===================
-
-.. automodule:: nerfstudio.field_components.encodings
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/field_components/field_heads.rst b/docs/reference/api/field_components/field_heads.rst
deleted file mode 100644
index 49abedc9..00000000
--- a/docs/reference/api/field_components/field_heads.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _field_heads:
-
-Field Heads
-===================
-
-.. automodule:: nerfstudio.field_components.field_heads
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/field_components/index.rst b/docs/reference/api/field_components/index.rst
deleted file mode 100644
index e5f207a9..00000000
--- a/docs/reference/api/field_components/index.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. _field_modules:
-
-Field Modules
-===================
-
-TODO: High level description of field modules and how they connect together.
-
-.. toctree::
-
-   encodings
-   embeddings
-   field_heads
-   mlp
-   spatial_distortions
\ No newline at end of file
diff --git a/docs/reference/api/field_components/mlp.rst b/docs/reference/api/field_components/mlp.rst
deleted file mode 100644
index 80be3a62..00000000
--- a/docs/reference/api/field_components/mlp.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _mlp:
-
-MLP
-===================
-
-.. automodule:: nerfstudio.field_components.mlp
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/field_components/spatial_distortions.rst b/docs/reference/api/field_components/spatial_distortions.rst
deleted file mode 100644
index ee74610d..00000000
--- a/docs/reference/api/field_components/spatial_distortions.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _spatial_distortions:
-
-Spatial Distortions
-=====================
-
-.. automodule:: nerfstudio.field_components.spatial_distortions
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/fields.rst b/docs/reference/api/fields.rst
deleted file mode 100644
index 33bde75c..00000000
--- a/docs/reference/api/fields.rst
+++ /dev/null
@@ -1,60 +0,0 @@
-.. _fields:
-
-Fields
-============
-
-Base
-----------------
-
-.. automodule:: nerfstudio.fields.base_field
-   :members:
-   :show-inheritance:
-
-Density
-----------------
-
-.. automodule:: nerfstudio.fields.density_fields
-   :members:
-   :show-inheritance:
-
-Nerfacto
-----------------
-
-.. automodule:: nerfstudio.fields.nerfacto_field
-   :members:
-   :show-inheritance:
-
-Nerf-W
-----------------
-
-.. automodule:: nerfstudio.fields.nerfw_field
-   :members:
-   :show-inheritance:
-
-SDF
-----------------
-
-.. automodule:: nerfstudio.fields.sdf_field
-   :members:
-   :show-inheritance:
-
-Semantic NeRF
-----------------
-
-.. automodule:: nerfstudio.fields.semantic_nerf_field
-   :members:
-   :show-inheritance:
-
-TensoRF
-----------------
-
-.. automodule:: nerfstudio.fields.tensorf_field
-   :members:
-   :show-inheritance:
-
-Vanilla NeRF
-----------------
-
-.. automodule:: nerfstudio.fields.vanilla_nerf_field
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/index.rst b/docs/reference/api/index.rst
deleted file mode 100644
index a6691fec..00000000
--- a/docs/reference/api/index.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-.. _reference:
-
-API
-============
-
-TODO: Explanation of each component
-
-.. toctree::
-
-    cameras
-    config
-    data/index
-    fields
-    field_components/index
-    models
-    model_components/index
-    optimizers
-    plugins
-    utils/index
-    viewer
diff --git a/docs/reference/api/model_components/index.rst b/docs/reference/api/model_components/index.rst
deleted file mode 100644
index 18a11d7f..00000000
--- a/docs/reference/api/model_components/index.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-.. _graph_modules:
-
-Model components
-===================
-
-.. toctree::
-
-   ray_sampler
-   losses
-   renderers
\ No newline at end of file
diff --git a/docs/reference/api/model_components/losses.rst b/docs/reference/api/model_components/losses.rst
deleted file mode 100644
index 8a6a15a6..00000000
--- a/docs/reference/api/model_components/losses.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _losses:
-
-Losses
-===================
-
-.. automodule:: nerfstudio.model_components.losses
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/model_components/ray_sampler.rst b/docs/reference/api/model_components/ray_sampler.rst
deleted file mode 100644
index 821e85c5..00000000
--- a/docs/reference/api/model_components/ray_sampler.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _ray_sampler:
-
-Ray Sampler
-===================
-
-.. automodule:: nerfstudio.model_components.ray_samplers
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/model_components/renderers.rst b/docs/reference/api/model_components/renderers.rst
deleted file mode 100644
index 76f9301a..00000000
--- a/docs/reference/api/model_components/renderers.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _renderers:
-
-Renderers
-============
-
-.. automodule:: nerfstudio.model_components.renderers
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/models.rst b/docs/reference/api/models.rst
deleted file mode 100644
index 0b0819d5..00000000
--- a/docs/reference/api/models.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-.. _graphs:
-
-Models
-============
-
-Base
-----------------
-
-.. automodule:: nerfstudio.models.base_model
-   :members:
-   :show-inheritance:
-
-Instant NGP
-----------------
-
-.. automodule:: nerfstudio.models.instant_ngp
-   :members:
-   :show-inheritance:
-
-Semantic NeRF-W
-----------------
-
-.. automodule:: nerfstudio.models.semantic_nerfw
-   :members:
-   :show-inheritance:
-
-NeRF
-----------------
-
-.. automodule:: nerfstudio.models.vanilla_nerf
-   :members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/reference/api/optimizers.rst b/docs/reference/api/optimizers.rst
deleted file mode 100644
index c5799aac..00000000
--- a/docs/reference/api/optimizers.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-.. _engine:
-
-Engine
-============
-
-Optimizers
-----------------
-
-.. automodule:: nerfstudio.engine.optimizers
-   :members:
-   :show-inheritance:
-
-Schedulers
-----------------
-
-.. automodule:: nerfstudio.engine.schedulers
-   :members:
-   :show-inheritance:
-
-Trainer
-----------------
-
-.. automodule:: nerfstudio.engine.trainer
-   :members:
-   :show-inheritance:
-
-Callbacks
-----------------
-
-.. automodule:: nerfstudio.engine.callbacks
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/plugins.rst b/docs/reference/api/plugins.rst
deleted file mode 100644
index bb850455..00000000
--- a/docs/reference/api/plugins.rst
+++ /dev/null
@@ -1,25 +0,0 @@
-.. _plugins:
-
-Plugins
-============
-
-Method Registry
-----------------------
-
-.. automodule:: nerfstudio.plugins.registry
-   :members:
-   :show-inheritance:
-
-DataParser Registry
-----------------------
-
-.. automodule:: nerfstudio.plugins.registry_dataparser
-   :members:
-   :show-inheritance:
-
-Types
-----------------------
-
-.. automodule:: nerfstudio.plugins.types
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/utils/colormaps.rst b/docs/reference/api/utils/colormaps.rst
deleted file mode 100644
index cf25c2aa..00000000
--- a/docs/reference/api/utils/colormaps.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _colormaps:
-
-Colormaps
-----------------
-
-.. automodule:: nerfstudio.utils.colormaps
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/utils/colors.rst b/docs/reference/api/utils/colors.rst
deleted file mode 100644
index daae790c..00000000
--- a/docs/reference/api/utils/colors.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _colors:
-
-Colors
-------------
-
-.. automodule:: nerfstudio.utils.colors
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/utils/index.rst b/docs/reference/api/utils/index.rst
deleted file mode 100644
index 08c83d15..00000000
--- a/docs/reference/api/utils/index.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-.. _utils:
-
-Utils
-===================
-
-.. toctree::
-
-   colors
-   math
-   colormaps
-   tensor_dataclass
\ No newline at end of file
diff --git a/docs/reference/api/utils/math.rst b/docs/reference/api/utils/math.rst
deleted file mode 100644
index 5ddd8295..00000000
--- a/docs/reference/api/utils/math.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _math:
-
-Math
-============
-
-.. automodule:: nerfstudio.utils.math
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/utils/tensor_dataclass.rst b/docs/reference/api/utils/tensor_dataclass.rst
deleted file mode 100644
index 82ce89ab..00000000
--- a/docs/reference/api/utils/tensor_dataclass.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _tensor_dataclass:
-
-TensorDataclass
-=================
-
-.. automodule:: nerfstudio.utils.tensor_dataclass
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/api/viewer.rst b/docs/reference/api/viewer.rst
deleted file mode 100644
index 06166246..00000000
--- a/docs/reference/api/viewer.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _viewer:
-
-Viewer
-============
-
-.. automodule:: nerfstudio.viewer.viewer_elements
-   :members:
-   :show-inheritance:
diff --git a/docs/reference/cli/index.md b/docs/reference/cli/index.md
deleted file mode 100644
index 4daee655..00000000
--- a/docs/reference/cli/index.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# CLI
-
-We provide a command line interface for training your own NeRFs (no coding necessary). You can learn more about each command by using the `--help` argument.
-
-## Commands
-
-Here are the popular commands that we offer. If you've cloned the repo, you can also look at the [pyproject.toml file](https://github.com/nerfstudio-project/nerfstudio/blob/main/pyproject.toml) at the `[project.scripts]` section for details.
-
-| Command                              | Description                            | Filename                                      |
-| ------------------------------------ | -------------------------------------- | --------------------------------------------- |
-| [ns-install-cli](ns_install_cli)     | Install tab completion for all scripts | nerfstudio/scripts/completions/install.py     |
-| [ns-process-data](ns_process_data)   | Generate a dataset from your own data  | nerfstudio/scripts/process_data.py            |
-| [ns-download-data](ns_download_data) | Download existing captures             | nerfstudio/scripts/downloads/download_data.py |
-| [ns-train](ns_train)                 | Generate a NeRF                        | nerfstudio/scripts/train.py                   |
-| [ns-viewer](ns_viewer)               | View a trained NeRF                    | nerfstudio/scripts/viewer/run_viewer.py       |
-| [ns-eval](ns_eval)                   | Run evaluation metrics for your Model  | nerfstudio/scripts/eval.py                    |
-| [ns-render](ns_render)               | Render out a video of your NeRF        | nerfstudio/scripts/render.py                  |
-| [ns-export](ns_export)               | Export a NeRF into other formats       | nerfstudio/scripts/exporter.py                |
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-ns_install_cli
-ns_process_data
-ns_download_data
-ns_train
-ns_render
-ns_viewer
-ns_export
-ns_eval
-```
diff --git a/docs/reference/cli/ns_download_data.md b/docs/reference/cli/ns_download_data.md
deleted file mode 100644
index 1105ef12..00000000
--- a/docs/reference/cli/ns_download_data.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# ns-download-data
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.downloads.download_data
-    :func: get_parser_fn
-    :prog: ns-download-data
-    :nodefault:
-```
diff --git a/docs/reference/cli/ns_eval.md b/docs/reference/cli/ns_eval.md
deleted file mode 100644
index 86e4aa0b..00000000
--- a/docs/reference/cli/ns_eval.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# ns-eval
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.eval
-    :func: get_parser_fn
-    :prog: ns-eval
-    :nodefault:
-```
diff --git a/docs/reference/cli/ns_export.md b/docs/reference/cli/ns_export.md
deleted file mode 100644
index 83921191..00000000
--- a/docs/reference/cli/ns_export.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# ns-export
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.exporter
-    :func: get_parser_fn
-    :prog: ns-export
-    :nodefault:
-```
diff --git a/docs/reference/cli/ns_install_cli.md b/docs/reference/cli/ns_install_cli.md
deleted file mode 100644
index 30fa9598..00000000
--- a/docs/reference/cli/ns_install_cli.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# ns-install-cli
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.completions.install
-    :func: get_parser_fn
-    :prog: ns-install-cli
-    :nodefault:
-```
diff --git a/docs/reference/cli/ns_process_data.md b/docs/reference/cli/ns_process_data.md
deleted file mode 100644
index 9f9540d4..00000000
--- a/docs/reference/cli/ns_process_data.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# ns-process-data
-
-:::{admonition} Note
-:class: warning
-Make sure to have [COLMAP](https://colmap.github.io) and [FFmpeg](https://ffmpeg.org/download.html) installed.  
-You may also want to install [hloc](https://github.com/cvg/Hierarchical-Localization) (optional) for more feature detector and matcher options.
-:::
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.process_data
-    :func: get_parser_fn
-    :prog: ns-process-data
-    :nodefault:
-```
diff --git a/docs/reference/cli/ns_render.md b/docs/reference/cli/ns_render.md
deleted file mode 100644
index e006e62e..00000000
--- a/docs/reference/cli/ns_render.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# ns-render
-
-:::{admonition} Note
-:class: warning
-Make sure to have [FFmpeg](https://ffmpeg.org/download.html) installed.
-:::
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.render
-    :func: get_parser_fn
-    :prog: ns-render
-    :nodefault:
-```
diff --git a/docs/reference/cli/ns_train.md b/docs/reference/cli/ns_train.md
deleted file mode 100644
index 7b8a5262..00000000
--- a/docs/reference/cli/ns_train.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# ns-train
-
-Primary interface for training a NeRF model. `--help` is your friend when navigating command arguments. We also recommend installing the tab completion `ns-install-cli`.
-
-```bash
-usage: ns-train {method} [method args] {dataparser} [dataparser args]
-```
-
-If you are using a nerfstudio data set, the minimal command is:
-
-```bash
-ns-train nerfacto --data YOUR_DATA
-```
-
-To learn about the available methods:
-
-```bash
-ns-train --help
-```
-
-To learn about a methods parameters:
-
-```bash
-ns-train {method} --help
-```
-
-By default the nerfstudio dataparser is used. If you would like to use a different dataparser it can be specified after all of the method arguments. For a list of dataparser options:
-
-```bash
-ns-train {method} {dataparser} --help
-```
diff --git a/docs/reference/cli/ns_viewer.md b/docs/reference/cli/ns_viewer.md
deleted file mode 100644
index bb633355..00000000
--- a/docs/reference/cli/ns_viewer.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# ns-viewer
-
-```{eval-rst}
-.. argparse::
-    :module: nerfstudio.scripts.viewer.run_viewer
-    :func: get_parser_fn
-    :prog: ns-viewer
-    :nodefault:
-```
diff --git a/docs/reference/contributing.md b/docs/reference/contributing.md
deleted file mode 100644
index 818a18db..00000000
--- a/docs/reference/contributing.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Contributing
-
-**πŸ’ We're excited to have you join the nerfstudio family πŸ’**
-
-We welcome community contributions to Nerfstudio! Whether you want to fix bugs, improve the documentation, or introduce new features, we appreciate your input.
-
-Bug fixes and documentation improvements are highly valuable to us. If you come across any bugs or find areas where the documentation can be enhanced, please don't hesitate to submit a pull request (PR) with your proposed changes. We'll gladly review and integrate them into the project.
-
-For larger feature additions, we kindly request you to reach out to us on [Discord](https://discord.gg/uMbNqcraFc) in the `#contributing` channel and create an issue on GitHub. This will allow us to discuss the feature in more detail and ensure that it aligns with the goals and direction of the repository. We cannot guarantee that the feature will be added to Nerfstudio.
-
-In addition to code contributions, we also encourage contributors to add their own methods to our documentation. For more information on how to contribute new methods, please refer to the documentation [here](../developer_guides/new_methods.md).
-
-## Overview
-
-Below are the various tooling features our team uses to maintain this codebase.
-
-| Tooling              | Support                                                    |
-| -------------------- | ---------------------------------------------------------- |
-| Formatting & Linting | [Ruff](https://beta.ruff.rs/docs/)                         |
-| Type checking        | [Pyright](https://github.com/microsoft/pyright)            |
-| Testing              | [pytest](https://docs.pytest.org/en/7.1.x/)                |
-| Docs                 | [Sphinx](https://www.sphinx-doc.org/en/master/)            |
-| Docstring style      | [Google](https://google.github.io/styleguide/pyguide.html) |
-| JS Linting           | [eslint](https://eslint.org/)                              |
-
-## Requirements
-
-To install the required packages and register the pre-commit hook:
-
-```bash
-pip install -e .[dev]
-pip install -e .[docs]
-pre-commit install
-```
-
-This will ensure you have the required packages to run the tests, linter, build the docs, etc.
-The pre-commit hook will ensure your commits comply with the repository's code style rules.
-
-You may also need to install [pandoc](https://pandoc.org/). If you are using `conda` you can run the following:
-
-```bash
-conda install -c conda-forge pandoc
-```
-
-## Committing code
-
-1. Make your modifications ✏️
-2. Perform local checks βœ…
-
-   To ensure that you will be passing all tests and checks on github, you will need to run the following command:
-
-   ```bash
-   ns-dev-test
-   ```
-
-   This will perform the following checks and actions:
-
-   - Formatting and linting: Ensures code is consistently and properly formatted.
-   - Type checking: Ensures static type safety.
-   - Pytests: Runs pytests locally to make sure added code does not break existing logic.
-   - Documentation build: Builds docs locally. Ensures changes do not result in warnings/errors.
-   - Licensing: Automatically adds licensing headers to the correct files.
-
-   :::{admonition} Attention
-   :class: attention
-   In order to merge changes to the code base, all of these checks must be passing. If you pass these tests locally, you will likely pass on github servers as well (results in a green checkmark next to your commit).
-   :::
-
-3. Open pull request! πŸ’Œ
-
-:::{admonition} Note
-:class: info
-
-We will not review the pull request until it is passing all checks.
-:::
-
-## Maintaining documentation
-
-### Building
-
-Run the following to build the documentation:
-
-```bash
-python nerfstudio/scripts/docs/build_docs.py
-```
-
-:::{admonition} Tip
-:class: info
-
-- Rerun `make html` when documentation changes are made
-- `make clean` is necessary if the documentation structure changes.
-  :::
-
-### Auto build
-
-As you change or add models/components, the auto-generated [Reference API](https://docs.nerf.studio/reference/api/index.html) may change.
-If you want the code to build on save you can use [sphinx autobuild](https://github.com/executablebooks/sphinx-autobuild).
-
-:::{admonition} Tip
-:class: info
-
-If changes to the structure are made, the build files may be incorrect.
-:::
-
-```bash
-pip install sphinx-autobuild
-sphinx-autobuild docs docs/_build/html
-```
-
-### Adding notebooks
-
-We support jupyter notebooks in our documentation. To improve the readability, the following custom tags can be added to the top of each code cell to hide or collapse the code.
-
-| Tag           | Effect                                               |
-| ------------- | ---------------------------------------------------- |
-| # HIDDEN      | Hide code block and output                           |
-| # COLLAPSED   | Collapse the code in a dropdown but show the results |
-| # OUTPUT_ONLY | Only show the cell's output                          |
diff --git a/nerfstudio/cameras/camera_optimizers.py b/nerfstudio/cameras/camera_optimizers.py
index 6ab8cc2a..5bcd24be 100644
--- a/nerfstudio/cameras/camera_optimizers.py
+++ b/nerfstudio/cameras/camera_optimizers.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,7 +21,7 @@
 
 import functools
 from dataclasses import dataclass, field
-from typing import Literal, Optional, Type, Union
+from typing import Literal, Optional, Tuple, Type, Union
 
 import torch
 import tyro
@@ -110,6 +111,10 @@ def __init__(
         else:
             assert_never(self.config.mode)
 
+    def _get_pose_adjustment(self) -> Float[Tensor, "num_cameras 6"]:
+        """Get the pose adjustment."""
+        return self.pose_adjustment
+
     def forward(
         self,
         indices: Int[Tensor, "camera_indices"],
@@ -127,9 +132,9 @@ def forward(
         if self.config.mode == "off":
             pass
         elif self.config.mode == "SO3xR3":
-            outputs.append(exp_map_SO3xR3(self.pose_adjustment[indices, :]))
+            outputs.append(exp_map_SO3xR3(self._get_pose_adjustment()[indices, :]))
         elif self.config.mode == "SE3":
-            outputs.append(exp_map_SE3(self.pose_adjustment[indices, :]))
+            outputs.append(exp_map_SE3(self._get_pose_adjustment()[indices, :]))
         else:
             assert_never(self.config.mode)
         # Detach non-trainable indices by setting to identity transform
@@ -149,7 +154,11 @@ def apply_to_raybundle(self, raybundle: RayBundle) -> None:
         if self.config.mode != "off":
             correction_matrices = self(raybundle.camera_indices.squeeze())  # type: ignore
             raybundle.origins = raybundle.origins + correction_matrices[:, :3, 3]
-            raybundle.directions = torch.bmm(correction_matrices[:, :3, :3], raybundle.directions[..., None]).squeeze()
+            raybundle.directions = (
+                torch.bmm(correction_matrices[:, :3, :3], raybundle.directions[..., None])
+                .squeeze()
+                .to(raybundle.origins)
+            )
 
     def apply_to_camera(self, camera: Cameras) -> None:
         """Apply the pose correction to the raybundle"""
@@ -164,9 +173,10 @@ def apply_to_camera(self, camera: Cameras) -> None:
     def get_loss_dict(self, loss_dict: dict) -> None:
         """Add regularization"""
         if self.config.mode != "off":
+            pose_adjustment = self._get_pose_adjustment()
             loss_dict["camera_opt_regularizer"] = (
-                self.pose_adjustment[:, :3].norm(dim=-1).mean() * self.config.trans_l2_penalty
-                + self.pose_adjustment[:, 3:].norm(dim=-1).mean() * self.config.rot_l2_penalty
+                pose_adjustment[:, :3].norm(dim=-1).mean() * self.config.trans_l2_penalty
+                + pose_adjustment[:, 3:].norm(dim=-1).mean() * self.config.rot_l2_penalty
             )
 
     def get_correction_matrices(self):
@@ -176,8 +186,9 @@ def get_correction_matrices(self):
     def get_metrics_dict(self, metrics_dict: dict) -> None:
         """Get camera optimizer metrics"""
         if self.config.mode != "off":
-            metrics_dict["camera_opt_translation"] = self.pose_adjustment[:, :3].norm()
-            metrics_dict["camera_opt_rotation"] = self.pose_adjustment[:, 3:].norm()
+            pose_adjustment = self._get_pose_adjustment()
+            metrics_dict["camera_opt_translation"] = pose_adjustment[:, :3].norm()
+            metrics_dict["camera_opt_rotation"] = pose_adjustment[:, 3:].norm()
 
     def get_param_groups(self, param_groups: dict) -> None:
         """Get camera optimizer parameters"""
@@ -187,3 +198,37 @@ def get_param_groups(self, param_groups: dict) -> None:
             param_groups["camera_opt"] = camera_opt_params
         else:
             assert len(camera_opt_params) == 0
+
+
+@dataclass
+class ScaledCameraOptimizerConfig(CameraOptimizerConfig):
+    """Configuration of axis-masked optimization for camera poses."""
+
+    _target: Type = field(default_factory=lambda: ScaledCameraOptimizer)
+
+    weights: Tuple[float, float, float, float, float, float] = (1.0, 1.0, 1.0, 1.0, 1.0, 1.0)
+
+    trans_l2_penalty: Tuple[float, float, float] = (1e-2, 1e-2, 1e-2)  # TODO: this is l1
+
+
+class ScaledCameraOptimizer(CameraOptimizer):
+    """Camera optimizer that masks which components can be optimized."""
+
+    def __init__(self, config: ScaledCameraOptimizerConfig, **kwargs) -> None:
+        super().__init__(config, **kwargs)
+        self.config: ScaledCameraOptimizerConfig = self.config
+        self.register_buffer("weights", torch.tensor(self.config.weights, dtype=torch.float32))
+        self.trans_penalty = torch.tensor(self.config.trans_l2_penalty, dtype=torch.float32, device=self.device)
+
+    def _get_pose_adjustment(self) -> Float[Tensor, "num_cameras 6"]:
+        """Get the pose adjustment."""
+        return self.pose_adjustment * self.weights
+
+    def get_loss_dict(self, loss_dict: dict) -> None:
+        """Add regularization"""
+        if self.config.mode != "off":
+            pose_adjustment = self._get_pose_adjustment()
+            self.trans_penalty = self.trans_penalty.to(pose_adjustment.device)
+            loss_dict["camera_opt_regularizer"] = (
+                pose_adjustment[:, :3].abs() * self.trans_penalty
+            ).mean() + pose_adjustment[:, 3:].norm(dim=-1).mean() * self.config.rot_l2_penalty
diff --git a/nerfstudio/cameras/camera_paths.py b/nerfstudio/cameras/camera_paths.py
index f0c5f0a1..1f734621 100644
--- a/nerfstudio/cameras/camera_paths.py
+++ b/nerfstudio/cameras/camera_paths.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,6 +19,7 @@
 
 from typing import Any, Dict, Optional, Tuple
 
+import numpy as np
 import torch
 
 import nerfstudio.utils.poses as pose_utils
@@ -39,17 +41,67 @@ def get_interpolated_camera_path(cameras: Cameras, steps: int, order_poses: bool
     """
     Ks = cameras.get_intrinsics_matrices()
     poses = cameras.camera_to_worlds
-    poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps, order_poses=order_poses)
+    poses, Ks = get_interpolated_poses_many(
+        poses, Ks, steps_per_transition=steps, order_poses=order_poses, include_last=False
+    )
 
-    cameras = Cameras(
+    return Cameras(
         fx=Ks[:, 0, 0],
         fy=Ks[:, 1, 1],
         cx=Ks[0, 0, 2],
         cy=Ks[0, 1, 2],
         camera_type=cameras.camera_type[0],
         camera_to_worlds=poses,
+        height=cameras.height[0],
+        width=cameras.width[0],
+    )
+
+
+def get_interpolated_spiral_camera_path(cameras: Cameras, steps: int, radius: float, rotations: float) -> Cameras:
+    """Generate a camera path between two cameras with a circular spiral pattern.
+
+    Args:
+        cameras: Cameras object containing intrinsics of all cameras.
+        steps: The number of steps to interpolate between the two cameras.
+        radius: The radius of the circular spiral around the straight path.
+        rotations: The number of rotations along the spiral path.
+
+    Returns:
+        A new set of cameras along a circular spiral path.
+    """
+    Ks = cameras.get_intrinsics_matrices()
+    poses = cameras.camera_to_worlds
+    poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps, include_last=False)
+
+    # Calculate the angles for the circular spiral
+    angles = np.linspace(0, 2 * np.pi * rotations, len(poses))
+
+    direction = poses[0, :3, 3] - poses[-1, :3, 3]
+    direction = direction / np.linalg.norm(direction)
+
+    # Calculate the normal vector (orthogonal to the direction vector and the z-axis)
+    normal = np.cross(np.array([0, 0, 1]), direction)
+    normal = normal / np.linalg.norm(normal)
+
+    # Calculate a second orthogonal vector in the plane perpendicular to the direction vector
+    ortho = np.cross(direction, normal)
+    ortho = ortho / np.linalg.norm(ortho)
+
+    # Calculate the offsets in the plane perpendicular to the direction vector
+    offsets = radius * (np.cos(angles)[:, None] * normal[None] + np.sin(angles)[:, None] * ortho[None])
+
+    # Add the offsets to the current position
+    poses[:, :3, 3] += offsets
+
+    return Cameras(
+        fx=Ks[:, 0, 0],
+        fy=Ks[:, 1, 1],
+        cx=Ks[0, 0, 2],
+        cy=Ks[0, 1, 2],
+        camera_to_worlds=poses,
+        height=cameras.height[0],
+        width=cameras.width[0],
     )
-    return cameras
 
 
 def get_spiral_path(
@@ -117,6 +169,8 @@ def get_spiral_path(
         cy=camera.cy[0],
         camera_to_worlds=new_c2ws,
         times=times,
+        height=camera.height[0],
+        width=camera.width[0],
     )
 
 
diff --git a/nerfstudio/cameras/camera_utils.py b/nerfstudio/cameras/camera_utils.py
index 185a852a..5f336a0f 100644
--- a/nerfstudio/cameras/camera_utils.py
+++ b/nerfstudio/cameras/camera_utils.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,6 +22,7 @@
 
 import numpy as np
 import torch
+import torch.nn.functional as F
 from jaxtyping import Float
 from numpy.typing import NDArray
 from torch import Tensor
@@ -47,6 +49,96 @@ def unit_vector(data: NDArray, axis: Optional[int] = None) -> np.ndarray:
     return data
 
 
+# from https://github.com/naver/roma/blob/0da38a34f36c0c917c0a15ea36eee561c6096719/roma/mappings.py#L329
+def rotmat_to_unitquat(R):
+    """
+    Converts rotation matrix to unit quaternion representation.
+
+    Args:
+        R (...x3x3 tensor): batch of rotation matrices.
+    Returns:
+        batch of unit quaternions (...x4 tensor, XYZW convention).
+    """
+    batch_shape = R.shape[:-2]
+    matrix = R.reshape((-1, 3, 3))
+    num_rotations, D1, D2 = matrix.shape
+    assert (D1, D2) == (3, 3), "Input should be a Bx3x3 tensor."
+
+    # Adapted from SciPy:
+    # https://github.com/scipy/scipy/blob/7cb3d751756907238996502b92709dc45e1c6596/scipy/spatial/transform/rotation.py#L480
+
+    decision_matrix = torch.empty((num_rotations, 4), dtype=matrix.dtype, device=matrix.device)
+    decision_matrix[:, :3] = matrix.diagonal(dim1=1, dim2=2)
+    decision_matrix[:, -1] = decision_matrix[:, :3].sum(dim=1)
+    choices = decision_matrix.argmax(dim=1)
+
+    quat = torch.empty((num_rotations, 4), dtype=matrix.dtype, device=matrix.device)
+
+    ind = torch.nonzero(choices != 3, as_tuple=True)[0]
+    i = choices[ind]
+    j = (i + 1) % 3
+    k = (j + 1) % 3
+
+    quat[ind, i] = 1 - decision_matrix[ind, -1] + 2 * matrix[ind, i, i]
+    quat[ind, j] = matrix[ind, j, i] + matrix[ind, i, j]
+    quat[ind, k] = matrix[ind, k, i] + matrix[ind, i, k]
+    quat[ind, 3] = matrix[ind, k, j] - matrix[ind, j, k]
+
+    ind = torch.nonzero(choices == 3, as_tuple=True)[0]
+    quat[ind, 0] = matrix[ind, 2, 1] - matrix[ind, 1, 2]
+    quat[ind, 1] = matrix[ind, 0, 2] - matrix[ind, 2, 0]
+    quat[ind, 2] = matrix[ind, 1, 0] - matrix[ind, 0, 1]
+    quat[ind, 3] = 1 + decision_matrix[ind, -1]
+
+    quat = quat / torch.norm(quat, dim=1)[:, None]
+    return quat.reshape(batch_shape + (4,))
+
+
+def unitquat_to_rotmat(quat):
+    """
+    Converts unit quaternion into rotation matrix representation.
+
+    Args:
+        quat (...x4 tensor, XYZW convention): batch of unit quaternions.
+            No normalization is applied before computation.
+    Returns:
+        batch of rotation matrices (...x3x3 tensor).
+    """
+    # Adapted from SciPy:
+    # https://github.com/scipy/scipy/blob/adc4f4f7bab120ccfab9383aba272954a0a12fb0/scipy/spatial/transform/rotation.py#L912
+    x = quat[..., 0]
+    y = quat[..., 1]
+    z = quat[..., 2]
+    w = quat[..., 3]
+
+    x2 = x * x
+    y2 = y * y
+    z2 = z * z
+    w2 = w * w
+
+    xy = x * y
+    zw = z * w
+    xz = x * z
+    yw = y * w
+    yz = y * z
+    xw = x * w
+
+    matrix = torch.empty(quat.shape[:-1] + (3, 3), dtype=quat.dtype, device=quat.device)
+
+    matrix[..., 0, 0] = x2 - y2 - z2 + w2
+    matrix[..., 1, 0] = 2 * (xy + zw)
+    matrix[..., 2, 0] = 2 * (xz - yw)
+
+    matrix[..., 0, 1] = 2 * (xy - zw)
+    matrix[..., 1, 1] = -x2 + y2 - z2 + w2
+    matrix[..., 2, 1] = 2 * (yz + xw)
+
+    matrix[..., 0, 2] = 2 * (xz + yw)
+    matrix[..., 1, 2] = 2 * (yz - xw)
+    matrix[..., 2, 2] = -x2 - y2 + z2 + w2
+    return matrix
+
+
 def quaternion_from_matrix(matrix: NDArray, isprecise: bool = False) -> np.ndarray:
     """Return quaternion from rotation matrix.
 
@@ -138,6 +230,172 @@ def quaternion_slerp(
     return q0
 
 
+# adapted from https://github.com/naver/roma/blob/0da38a34f36c0c917c0a15ea36eee561c6096719/roma/utils.py#L333
+def unitquat_slerp_fast(q0, q1, steps, shortest_arc=True):
+    """
+    Spherical linear interpolation between two unit quaternions.
+    This function requires less computations than :func:`roma.utils.unitquat_slerp`,
+    but is **unsuitable for extrapolation (i.e.** ``steps`` **must be within [0,1])**.
+
+    Args:
+        q0, q1 (Ax4 tensor): batch of unit quaternions (A may contain multiple dimensions).
+        steps (tensor of shape A): interpolation steps within 0.0 and 1.0, 0.0 corresponding to q0 and 1.0 to q1 (A may contain multiple dimensions).
+        shortest_arc (boolean): if True, interpolation will be performed along the shortest arc on SO(3) from `q0` to `q1` or `-q1`.
+    Returns:
+        batch of interpolated quaternions (BxAx4 tensor).
+    """
+    batch_shape = q0.shape[:-1]
+    q0 = q0.reshape((-1, 4))
+    batch_shape1 = q1.shape[:-1]
+    q1 = q1.reshape((-1, 4))
+
+    assert batch_shape == batch_shape1
+    assert batch_shape == steps.shape
+    # omega is the 'angle' between both quaternions
+    cos_omega = torch.sum(q0 * q1, dim=-1)
+    if shortest_arc:
+        # Flip some quaternions to perform shortest arc interpolation.
+        q1 = q1.clone()
+        q1[cos_omega < 0, :] = -q1[cos_omega < 0, :]
+        cos_omega = torch.abs(cos_omega)
+    # True when q0 and q1 are close.
+    nearby_quaternions = cos_omega > (1.0 - 1e-3)
+    nearby_quaternions_idx = nearby_quaternions.nonzero(as_tuple=True)[0]
+
+    # General approach
+    omega = torch.acos(cos_omega)
+    alpha = torch.sin((1 - steps) * omega)
+    beta = torch.sin(steps * omega)
+    # Use linear interpolation for nearby quaternions
+    alpha[nearby_quaternions_idx] = 1 - steps[nearby_quaternions_idx]
+    beta[nearby_quaternions_idx] = steps[nearby_quaternions_idx]
+    # Interpolation
+    q = alpha.unsqueeze(-1) * q0 + beta.unsqueeze(-1) * q1
+    # Normalization of the output
+    q = q / torch.norm(q, dim=-1, keepdim=True)
+    return q.reshape(batch_shape + (4,))
+
+
+def quat_product(p, q):
+    """
+    Returns the product of two quaternions.
+
+    Args:
+        p, q (...x4 tensor, XYZW convention): batch of quaternions.
+    Returns:
+        batch of quaternions (...x4 tensor, XYZW convention).
+    """
+    # Adapted from SciPy:
+    # https://github.com/scipy/scipy/blob/adc4f4f7bab120ccfab9383aba272954a0a12fb0/scipy/spatial/transform/rotation.py#L153
+
+    vector = p[..., None, 3] * q[..., :3] + q[..., None, 3] * p[..., :3] + torch.cross(p[..., :3], q[..., :3], dim=-1)
+    last = p[..., 3] * q[..., 3] - torch.sum(p[..., :3] * q[..., :3], dim=-1)
+    return torch.cat((vector, last[..., None]), dim=-1)
+
+
+def unitquat_to_rotvec(quat, shortest_arc=True):
+    """
+    Converts unit quaternion into rotation vector representation.
+
+    Based on the representation of a rotation of angle :math:`{\\theta}` and unit axis :math:`(x,y,z)`
+    by the unit quaternions :math:`\pm [\sin({\\theta} / 2) (x i + y j + z k) + \cos({\\theta} / 2)]`.
+
+    Args:
+        quat (...x4 tensor, XYZW convention): batch of unit quaternions.
+            No normalization is applied before computation.
+        shortest_arc (bool): if True, the function returns the smallest rotation vectors corresponding
+            to the input 3D rotations, i.e. rotation vectors with a norm smaller than :math:`\pi`.
+            If False, the function may return rotation vectors of norm larger than :math:`\pi`, depending on the sign of the input quaternions.
+    Returns:
+        batch of rotation vectors (...x3 tensor).
+    Note:
+        Behavior is undefined for inputs ``quat=torch.as_tensor([0.0, 0.0, 0.0, -1.0])`` and ``shortest_arc=False``,
+        as any rotation vector of angle :math:`2 \pi` could be a valid representation in such case.
+    """
+    batch_shape = quat.shape[:-1]
+    quat = quat.reshape((-1, 4))
+    # We perform a copy to support auto-differentiation.
+    quat = quat.clone()
+    # Adapted from SciPy:
+    # https://github.com/scipy/scipy/blob/adc4f4f7bab120ccfab9383aba272954a0a12fb0/scipy/spatial/transform/rotation.py#L1006-L1073
+    if shortest_arc:
+        # Enforce w > 0 to ensure 0 <= angle <= pi.
+        # (Otherwise angle can be arbitrary within ]-2pi, 2pi]).
+        quat[quat[:, 3] < 0] *= -1
+    half_angle = torch.atan2(torch.norm(quat[:, :3], dim=1), quat[:, 3])
+    angle = 2 * half_angle
+    small_angle = torch.abs(angle) <= 1e-3
+    large_angle = ~small_angle
+
+    num_rotations = len(quat)
+    scale = torch.empty(num_rotations, dtype=quat.dtype, device=quat.device)
+    scale[small_angle] = 2 + angle[small_angle] ** 2 / 12 + 7 * angle[small_angle] ** 4 / 2880
+    scale[large_angle] = angle[large_angle] / torch.sin(half_angle[large_angle])
+
+    rotvec = scale[:, None] * quat[:, :3]
+    return rotvec.reshape(batch_shape + (3,))
+
+
+def rotvec_to_unitquat(rotvec):
+    """
+    Converts rotation vector into unit quaternion representation.
+
+    Args:
+        rotvec (...x3 tensor): batch of rotation vectors.
+    Returns:
+        batch of unit quaternions (...x4 tensor, XYZW convention).
+    """
+    batch_shape = rotvec.shape[:-1]
+    rotvec = rotvec.reshape((-1, 3))
+    num_rotations, D = rotvec.shape
+    assert D == 3, "Input should be a Bx3 tensor."
+
+    # Adapted from SciPy:
+    # https://github.com/scipy/scipy/blob/adc4f4f7bab120ccfab9383aba272954a0a12fb0/scipy/spatial/transform/rotation.py#L621
+
+    norms = torch.norm(rotvec, dim=-1)
+    small_angle = norms <= 1e-3
+    large_angle = ~small_angle
+
+    scale = torch.empty((num_rotations,), device=rotvec.device, dtype=rotvec.dtype)
+    scale[small_angle] = 0.5 - norms[small_angle] ** 2 / 48 + norms[small_angle] ** 4 / 3840
+    scale[large_angle] = torch.sin(norms[large_angle] / 2) / norms[large_angle]
+
+    quat = torch.empty((num_rotations, 4), device=rotvec.device, dtype=rotvec.dtype)
+    quat[:, :3] = scale[:, None] * rotvec
+    quat[:, 3] = torch.cos(norms / 2)
+    return quat.reshape(batch_shape + (4,))
+
+
+def unitquat_slerp(q0, q1, steps, shortest_arc=True):
+    """
+    Spherical linear interpolation between two unit quaternions.
+
+    Args:
+        q0, q1 (Ax4 tensor): batch of unit quaternions (A may contain multiple dimensions).
+        steps (tensor of shape A): interpolation steps, 0.0 corresponding to q0 and 1.0 to q1 (A may contain multiple dimensions).
+        shortest_arc (boolean): if True, interpolation will be performed along the shortest arc on SO(3) from `q0` to `q1` or `-q1`.
+    Returns:
+        batch of interpolated quaternions (Ax4 tensor).
+    Note:
+        When considering quaternions as rotation representations,
+        one should keep in mind that spherical interpolation is not necessarily performed along the shortest arc,
+        depending on the sign of ``torch.sum(q0*q1,dim=-1)``.
+
+        Behavior is undefined when using ``shortest_arc=False`` with antipodal quaternions.
+    """
+    # Relative rotation
+    q0_conj = q0.clone()
+    q0_conj[..., :3] *= -1
+    rel_q = quat_product(q0_conj, q1)
+    rel_rotvec = unitquat_to_rotvec(rel_q, shortest_arc=shortest_arc)
+    # Relative rotations to apply
+    rel_rotvecs = steps.reshape(steps.shape + (1,)) * rel_rotvec
+    rots = rotvec_to_unitquat(rel_rotvecs.reshape(-1, 3)).reshape(*rel_rotvecs.shape[:-1], 4)
+    interpolated_q = quat_product(q0, rots.to(q0))
+    return interpolated_q
+
+
 def quaternion_matrix(quaternion: NDArray) -> np.ndarray:
     """Return homogeneous rotation matrix from quaternion.
 
@@ -160,7 +418,52 @@ def quaternion_matrix(quaternion: NDArray) -> np.ndarray:
     )
 
 
-def get_interpolated_poses(pose_a: NDArray, pose_b: NDArray, steps: int = 10) -> List[float]:
+# from pytorch3d
+def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
+    """
+    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
+    using Gram--Schmidt orthogonalization per Section B of [1].
+    Args:
+        d6: 6D rotation representation, of size (*, 6)
+
+    Returns:
+        batch of rotation matrices of size (*, 3, 3)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+
+    a1, a2 = d6[..., :3], d6[..., 3:]
+    b1 = F.normalize(a1, dim=-1)
+    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
+    b2 = F.normalize(b2, dim=-1)
+    b3 = torch.cross(b1, b2, dim=-1)
+    return torch.stack((b1, b2, b3), dim=-2)
+
+
+# from pytorch3d
+def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
+    by dropping the last row. Note that 6D representation is not unique.
+    Args:
+        matrix: batch of rotation matrices of size (*, 3, 3)
+
+    Returns:
+        6D rotation representation, of size (*, 6)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+    batch_dim = matrix.size()[:-2]
+    return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
+
+
+def get_interpolated_poses(pose_a: NDArray, pose_b: NDArray, steps: int = 10, include_last=True) -> List[float]:
     """Return interpolation of poses with specified number of steps.
     Args:
         pose_a: first pose
@@ -171,7 +474,8 @@ def get_interpolated_poses(pose_a: NDArray, pose_b: NDArray, steps: int = 10) ->
     quat_a = quaternion_from_matrix(pose_a[:3, :3])
     quat_b = quaternion_from_matrix(pose_b[:3, :3])
 
-    ts = np.linspace(0, 1, steps)
+    max_t = 1 if include_last else 1 - 1 / steps
+    ts = np.linspace(0, max_t, steps)
     quats = [quaternion_slerp(quat_a, quat_b, t) for t in ts]
     trans = [(1 - t) * pose_a[:3, 3] + t * pose_b[:3, 3] for t in ts]
 
@@ -185,7 +489,7 @@ def get_interpolated_poses(pose_a: NDArray, pose_b: NDArray, steps: int = 10) ->
 
 
 def get_interpolated_k(
-    k_a: Float[Tensor, "3 3"], k_b: Float[Tensor, "3 3"], steps: int = 10
+    k_a: Float[Tensor, "3 3"], k_b: Float[Tensor, "3 3"], steps: int = 10, include_last=True
 ) -> List[Float[Tensor, "3 4"]]:
     """
     Returns interpolated path between two camera poses with specified number of steps.
@@ -199,7 +503,8 @@ def get_interpolated_k(
         List of interpolated camera poses
     """
     Ks: List[Float[Tensor, "3 3"]] = []
-    ts = np.linspace(0, 1, steps)
+    max_t = 1 if include_last else 1 - 1 / steps
+    ts = np.linspace(0, max_t, steps)
     for t in ts:
         new_k = k_a * (1.0 - t) + k_b * t
         Ks.append(new_k)
@@ -247,6 +552,7 @@ def get_interpolated_poses_many(
     Ks: Float[Tensor, "num_poses 3 3"],
     steps_per_transition: int = 10,
     order_poses: bool = False,
+    include_last: bool = True,
 ) -> Tuple[Float[Tensor, "num_poses 3 4"], Float[Tensor, "num_poses 3 3"]]:
     """Return interpolated poses for many camera poses.
 
@@ -268,9 +574,13 @@ def get_interpolated_poses_many(
     for idx in range(poses.shape[0] - 1):
         pose_a = poses[idx].cpu().numpy()
         pose_b = poses[idx + 1].cpu().numpy()
-        poses_ab = get_interpolated_poses(pose_a, pose_b, steps=steps_per_transition)
+        poses_ab = get_interpolated_poses(pose_a, pose_b, steps=steps_per_transition, include_last=include_last)
         traj += poses_ab
-        k_interp += get_interpolated_k(Ks[idx], Ks[idx + 1], steps=steps_per_transition)
+        k_interp += get_interpolated_k(Ks[idx], Ks[idx + 1], steps=steps_per_transition, include_last=include_last)
+
+    if not include_last:
+        traj.append(poses[-1].cpu().numpy())
+        k_interp.append(Ks[-1])
 
     traj = np.stack(traj, axis=0)
     k_interp = torch.stack(k_interp, dim=0)
@@ -294,7 +604,9 @@ def normalize_with_norm(x: torch.Tensor, dim: int) -> Tuple[torch.Tensor, torch.
         Tuple of normalized tensor and corresponding norm.
     """
 
-    norm = torch.maximum(torch.linalg.vector_norm(x, dim=dim, keepdims=True), torch.tensor([_EPS]).to(x))
+    norm = torch.maximum(
+        torch.linalg.vector_norm(x, dim=dim, keepdims=True), torch.tensor([_EPS], device=x.device, dtype=x.dtype)
+    )
     return x / norm, norm
 
 
diff --git a/nerfstudio/cameras/cameras.py b/nerfstudio/cameras/cameras.py
index e390360b..c865cd13 100644
--- a/nerfstudio/cameras/cameras.py
+++ b/nerfstudio/cameras/cameras.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -918,6 +919,15 @@ def _compute_rays_for_vr180(
         else:
             metadata = {"directions_norm": directions_norm[0].detach()}
 
+        if self.metadata and "rolling_shutter_offsets" in self.metadata and "velocities" in self.metadata:
+            cam_idx = camera_indices.squeeze(-1)
+            heights, rows = self.height[cam_idx], coords[..., 0:1]
+            duration = self.metadata["rolling_shutter_offsets"][cam_idx].diff()
+            time_offsets = rows / heights * duration + self.metadata["rolling_shutter_offsets"][cam_idx][..., 0:1]
+            origins = origins + self.metadata["velocities"][cam_idx] * time_offsets
+            times = times + time_offsets
+            del metadata["rolling_shutter_offsets"]  # it has served its purpose
+
         return RayBundle(
             origins=origins,
             directions=directions,
@@ -925,6 +935,7 @@ def _compute_rays_for_vr180(
             camera_indices=camera_indices,
             times=times,
             metadata=metadata,
+            fars=torch.ones_like(pixel_area) * 1_000_000,
         )
 
     def to_json(
diff --git a/nerfstudio/cameras/lidars.py b/nerfstudio/cameras/lidars.py
new file mode 100644
index 00000000..811439ce
--- /dev/null
+++ b/nerfstudio/cameras/lidars.py
@@ -0,0 +1,540 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Camera Models
+"""
+import os
+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from jaxtyping import Float, Int
+from matplotlib import pyplot as plt
+from torch import Tensor
+from torch.nn import Parameter
+
+import nerfstudio.utils.math
+import nerfstudio.utils.poses as pose_utils
+from nerfstudio.cameras import camera_utils
+from nerfstudio.cameras.rays import RayBundle
+from nerfstudio.data.scene_box import SceneBox
+from nerfstudio.utils.misc import strtobool, torch_compile
+from nerfstudio.utils.tensor_dataclass import TensorDataclass
+
+TORCH_DEVICE = Union[torch.device, str]  # pylint: disable=invalid-name
+
+HORIZONTAL_BEAM_DIVERGENCE = 3.0e-3  # radians, or meters at a distance of 1m
+VERTICAL_BEAM_DIVERGENCE = 1.5e-3  # radians, or meters at a distance of 1m
+
+
+class LidarType(Enum):
+    """Supported Lidar Types."""
+
+    VELODYNE16 = auto()
+    VELODYNE_HDL32E = auto()
+    VELODYNE_VLP32C = auto()
+    VELODYNE64E = auto()
+    VELODYNE128 = auto()
+    PANDAR64 = auto()
+
+
+LIDAR_MODEL_TO_TYPE = {
+    "VELODYNE16": LidarType.VELODYNE16,
+    "VELODYNE_HDL32E": LidarType.VELODYNE_HDL32E,
+    "VELODYNE_VLP32C": LidarType.VELODYNE_VLP32C,
+    "VELODYNE64E": LidarType.VELODYNE64E,
+    "VELODYNE128": LidarType.VELODYNE128,
+    "PANDAR64": LidarType.PANDAR64,
+}
+
+
+@dataclass(init=False)
+class Lidars(TensorDataclass):
+    """Dataparser outputs for the lidar dataset and the ray generator.
+
+    If a single value is provided, it is broadcasted to all lidars.
+
+    Args:
+        lidar_to_worlds: Lidar to world matrices. Tensor of per-image l2w matrices, in [R | t] format
+        lidar_type: Type of lidar sensor. This will be an int corresponding to the LidarType enum.
+        assume_ego_compensated: Whether or not to assume that points are ego-compensated.
+        times: Timestamps for each lidar
+        n_points: Number of points in each lidar
+        metadata: Additional metadata or data needed for interpolation, will mimic shape of the lidars
+            and will be broadcasted to the rays generated from any derivative RaySamples we create with this
+    """
+
+    lidar_to_worlds: Float[Tensor, "*num_lidars 3 4"]
+    lidar_type: Float[Tensor, "*num_lidars 1"]
+    times: Optional[Int[Tensor, "*num_lidars 1"]]
+    metadata: Optional[Dict]
+    horizontal_beam_divergence: Float[Tensor, "*num_lidars 1"]
+    vertical_beam_divergence: Float[Tensor, "*num_lidars 1"]
+
+    def __init__(
+        self,
+        lidar_to_worlds: Float[Tensor, "*batch_l2ws 3 4"],
+        lidar_type: Optional[
+            Union[
+                Int[Tensor, "*batch_lidar_types 1"],
+                int,
+                List[LidarType],
+                LidarType,
+            ]
+        ] = LidarType.VELODYNE_VLP32C,
+        assume_ego_compensated: bool = True,
+        times: Optional[Float[Tensor, "*num_lidars"]] = None,
+        metadata: Optional[Dict] = None,
+        horizontal_beam_divergence: Optional[Union[Float, Float[Tensor, "*num_lidars 1"]]] = None,
+        vertical_beam_divergence: Optional[Union[Float, Float[Tensor, "*num_lidars 1"]]] = None,
+        valid_lidar_distance_threshold: float = 1e3,
+    ) -> None:
+        """Initializes the Lidars object.
+
+        Note on Input Tensor Dimensions: All of these tensors have items of dimensions Shaped[Tensor, "3 4"]
+        (in the case of the c2w matrices), Shaped[Tensor, "6"] (in the case of distortion params), or
+        Shaped[Tensor, "1"] (in the case of the rest of the elements). The dimensions before that are
+        considered the batch dimension of that tensor (batch_c2ws, batch_fxs, etc.). We will broadcast
+        all the tensors to be the same batch dimension. This means you can use any combination of the
+        input types in the function signature and it won't break. Your batch size for all tensors
+        must be broadcastable to the same size, and the resulting number of batch dimensions will be
+        the batch dimension with the largest number of dimensions.
+        """
+
+        # This will notify the tensordataclass that we have a field with more than 1 dimension
+        self._field_custom_dimensions = {"lidar_to_worlds": 2}
+
+        self.lidar_to_worlds = lidar_to_worlds
+
+        # @dataclass's post_init will take care of broadcasting
+        self.lidar_type = self._init_get_lidar_type(lidar_type)
+        self.times = self._init_get_times(times)
+
+        self.metadata = metadata
+
+        self.horizontal_beam_divergence = self._init_get_beam_divergence(
+            horizontal_beam_divergence, HORIZONTAL_BEAM_DIVERGENCE
+        )
+        self.vertical_beam_divergence = self._init_get_beam_divergence(
+            vertical_beam_divergence, VERTICAL_BEAM_DIVERGENCE
+        )
+
+        self.__post_init__()  # This will do the dataclass post_init and broadcast all the tensors
+
+        self._use_nerfacc = strtobool(os.environ.get("INTERSECT_WITH_NERFACC", "TRUE"))
+        self.assume_ego_compensated = assume_ego_compensated
+
+        self.valid_lidar_distance_threshold = valid_lidar_distance_threshold
+
+    def _init_get_beam_divergence(
+        self,
+        beam_divergence: Union[None, Float, Float[Tensor, "*num_lidars 1"]],
+        default: float,
+    ) -> Float[Tensor, "*num_lidars 1"]:
+        if beam_divergence is None:
+            beam_divergence = torch.ones_like(self.lidar_type) * default
+        elif isinstance(beam_divergence, float):
+            beam_divergence = torch.ones_like(self.lidar_type) * beam_divergence
+        elif isinstance(beam_divergence, torch.Tensor):
+            if beam_divergence.ndim == 0 or beam_divergence.shape[-1] != 1:
+                beam_divergence = beam_divergence.unsqueeze(-1)
+        else:
+            raise ValueError(f"beam_divergence must be None, float, or tensor, got {type(beam_divergence)}")
+
+        return beam_divergence
+
+    def _init_get_lidar_type(
+        self,
+        lidar_type: Union[
+            Int[Tensor, "*batch_lidar_types 1"], Int[Tensor, "*batch_lidar_types"], int, List[LidarType], LidarType
+        ],
+    ) -> Int[Tensor, "*num_lidars 1"]:
+        """
+        Parses the __init__() argument lidar_type
+
+        Lidar Type Calculation:
+        If LidarType, convert to int and then to tensor, then broadcast to all lidars
+        If List of LidarTypes, convert to ints and then to tensor, then broadcast to all lidars
+        If int, first go to tensor and then broadcast to all lidars
+        If tensor, broadcast to all lidars
+
+        Args:
+            lidar_type: lidar_type argument from __init__()
+        """
+        if isinstance(lidar_type, LidarType):
+            lidar_type = torch.tensor([lidar_type.value], device=self.device)
+        elif isinstance(lidar_type, List) and isinstance(lidar_type[0], LidarType):
+            lidar_type = torch.tensor([[c.value] for c in lidar_type], device=self.device)
+        elif isinstance(lidar_type, int):
+            lidar_type = torch.tensor([lidar_type], device=self.device)
+        elif isinstance(lidar_type, torch.Tensor):
+            assert not torch.is_floating_point(
+                lidar_type
+            ), f"lidar_type tensor must be of type int, not: {lidar_type.dtype}"
+            lidar_type = lidar_type.to(self.device)
+            if lidar_type.ndim == 0 or lidar_type.shape[-1] != 1:
+                lidar_type = lidar_type.unsqueeze(-1)
+            # assert torch.all(
+            #     lidar_type.view(-1)[0] == lidar_type
+            # ), "Batched lidars of different lidar_types will be allowed in the future."
+        else:
+            raise ValueError(
+                'Invalid lidar_type. Must be LidarType, List[LidarType], int, or torch.Tensor["num_lidars"]. \
+                    Received: '
+                + str(type(lidar_type))
+            )
+        return lidar_type
+
+    def _init_get_times(self, times: Union[None, torch.Tensor]) -> Union[None, torch.Tensor]:
+        if times is None:
+            times = None
+        elif isinstance(times, torch.Tensor):
+            if times.ndim == 0 or times.shape[-1] != 1:
+                times = times.unsqueeze(-1).to(self.device)
+        else:
+            raise ValueError(f"times must be None or a tensor, got {type(times)}")
+
+        return times
+
+    @property
+    def device(self) -> TORCH_DEVICE:
+        """Returns the device that the camera is on."""
+        return self.lidar_to_worlds.device
+
+    @property
+    def is_jagged(self) -> bool:
+        """
+        Returns whether or not the lidars are "jagged" (i.e. number of point vary between lidars)
+        TODO: base assumption is that all lidars have different number of points
+        """
+        return all(self.n_points.view(-1)[0] == self.n_points)
+
+    def generate_rays(  # pylint: disable=too-many-statements
+        self,
+        lidar_indices: Union[Int[Tensor, "*num_rays num_lidars_batch_dims"], int],
+        coords: Optional[Float[Tensor, "*num_rays 2"]] = None,
+        lidar_opt_to_lidar: Optional[Float[Tensor, "*num_rays 3 4"]] = None,
+        keep_shape: Optional[bool] = None,
+        aabb_box: Optional[SceneBox] = None,
+        points: Optional[Float[Tensor, "*num_rays point_dim"]] = None,
+    ) -> RayBundle:
+        """Generates rays for the given camera indices.
+
+        This function will standardize the input arguments and then call the _generate_rays_from_coords function
+        to generate the rays. Our goal is to parse the arguments and then get them into the right shape:
+            - lidar_indices: (num_rays:..., num_lidars_batch_dims)
+            - coords: (num_rays:..., 2)
+            - lidar_opt_to_lidar: (num_rays:..., 3, 4) or None
+
+        Read the docstring for _generate_rays_from_coords for more information on how we generate the rays
+        after we have standardized the arguments.
+
+        We are only concerned about different combinations of lidar_indices and coords matrices, and the following
+        are the 4 cases we have to deal with:
+            1. isinstance(lidar_indices, int) and coords == None
+                - In this case we broadcast our lidar_indices / coords shape (h, w, 1 / 2 respectively)
+            2. isinstance(lidar_indices, int) and coords != None
+                - In this case, we broadcast lidar_indices to the same batch dim as coords
+            3. not isinstance(lidar_indices, int) and coords == None
+                - In this case, we will need to set coords so that it is of shape (h, w, num_rays, 2), and broadcast
+                    all our other args to match the new definition of num_rays := (h, w) + num_rays
+            4. not isinstance(lidar_indices, int) and coords != None
+                - In this case, we have nothing to do, only check that the arguments are of the correct shape
+
+        There is one more edge case we need to be careful with: when we have "jagged lidars" (ie: different heights
+        and widths for each camera). This isn't problematic when we specify coords, since coords is already a tensor.
+        When coords == None (ie: when we render out the whole image associated with this camera), we run into problems
+        since there's no way to stack each coordinate map as all coordinate maps are all different shapes. In this case,
+        we will need to flatten each individual coordinate map and concatenate them, giving us only one batch dimension,
+        regardless of the number of prepended extra batch dimensions in the lidar_indices tensor.
+
+
+        Args:
+            lidar_indices: Camera indices of the flattened lidars object to generate rays for.
+            coords: Coordinates of the pixels to generate rays for. If None, the full image will be rendered.
+            lidar_opt_to_lidar: Optional transform for the camera to world matrices.
+            distortion_params_delta: Optional delta for the distortion parameters.
+            keep_shape: If None, then we default to the regular behavior of flattening if lidars is jagged, otherwise
+                keeping dimensions. If False, we flatten at the end. If True, then we keep the shape of the
+                lidar_indices and coords tensors (if we can).
+            disable_distortion: If True, disables distortion.
+            aabb_box: if not None will calculate nears and fars of the ray according to aabb box intesection
+
+        Returns:
+            Rays for the given camera indices and coords.
+        """
+        # Check the argument types to make sure they're valid and all shaped correctly
+        assert isinstance(lidar_indices, (torch.Tensor, int)), "lidar_indices must be a tensor or int"
+        assert (
+            coords is None or isinstance(coords, torch.Tensor) or points is not None
+        ), "coords must be a tensor or None, or points must be specified"
+        assert lidar_opt_to_lidar is None or isinstance(lidar_opt_to_lidar, torch.Tensor)
+        if isinstance(lidar_indices, torch.Tensor) and isinstance(coords, torch.Tensor):
+            num_rays_shape = lidar_indices.shape[:-1]
+            errormsg = "Batch dims of inputs must match when inputs are all tensors"
+            if coords is not None:
+                assert coords.shape == num_rays_shape, errormsg
+            assert lidar_opt_to_lidar is None or lidar_opt_to_lidar.shape[:-2] == num_rays_shape, errormsg
+
+        # If zero dimensional, we need to unsqueeze to get a batch dimension and then squeeze later
+        if not self.shape:
+            lidars = self.reshape((1,))
+            assert torch.all(
+                torch.tensor(lidar_indices == 0) if isinstance(lidar_indices, int) else lidar_indices == 0
+            ), "Can only index into single camera with no batch dimensions if index is zero"
+        else:
+            lidars = self
+
+        # If the camera indices are an int, then we need to make sure that the camera batch is 1D
+        if isinstance(lidar_indices, int):
+            assert (
+                len(lidars.shape) == 1
+            ), "lidar_indices must be a tensor if lidars are batched with more than 1 batch dimension"
+            lidar_indices = torch.tensor([lidar_indices], device=lidars.device)
+
+        assert lidar_indices.shape[-1] == len(
+            lidars.shape
+        ), "lidar_indices must have shape (num_rays:..., num_lidars_batch_dims)"
+
+        assert (coords is None) or (points is None), "Cannot specify both coords and points"
+        if points is not None:
+            raybundle = lidars._generate_rays_from_points(lidar_indices, points, lidar_opt_to_lidar)
+        elif coords is not None:
+            raise NotImplementedError("No lidar modeling yet")
+        else:
+            raise NotImplementedError("No lidar modeling yet")
+
+        # If we have mandated that we don't keep the shape, then we flatten
+        if keep_shape is False:
+            raybundle = raybundle.flatten()
+
+        if aabb_box:
+            with torch.no_grad():
+                tensor_aabb = Parameter(aabb_box.aabb.flatten(), requires_grad=False)
+
+                rays_o = raybundle.origins.contiguous()
+                rays_d = raybundle.directions.contiguous()
+
+                tensor_aabb = tensor_aabb.to(rays_o.device)
+                shape = rays_o.shape
+
+                rays_o = rays_o.reshape((-1, 3))
+                rays_d = rays_d.reshape((-1, 3))
+
+                t_min, t_max = nerfstudio.utils.math.intersect_aabb(rays_o, rays_d, tensor_aabb)
+
+                t_min = t_min.reshape([shape[0], shape[1], 1])
+                t_max = t_max.reshape([shape[0], shape[1], 1])
+
+                raybundle.nears = t_min
+                raybundle.fars = t_max
+
+        # TODO: We should have to squeeze the last dimension here if we started with zero batch dims, but never have to,
+        # so there might be a rogue squeeze happening somewhere, and this may cause some unintended behaviour
+        # that we haven't caught yet with tests
+        return raybundle
+
+    # pylint: disable=too-many-statements
+
+    def _generate_rays_from_points(
+        self,
+        lidar_indices: Int[Tensor, "*num_rays num_lidars_batch_dims"],
+        points: Float[Tensor, "*num_points point_dim"],
+        lidar_opt_to_lidar: Optional[Float[Tensor, "*num_rays 3 4"]] = None,
+    ) -> RayBundle:
+        # Make sure we're on the right devices
+        lidar_indices = lidar_indices.to(self.device)
+        points = points.to(self.device)
+
+        # Checking to make sure everything is of the right shape and type
+        num_rays_shape = lidar_indices.shape[:-1]
+        assert lidar_indices.shape == num_rays_shape + (self.ndim,)
+        assert lidar_opt_to_lidar is None or lidar_opt_to_lidar.shape == num_rays_shape + (3, 4)
+        l2w = self.lidar_to_worlds[lidar_indices.squeeze(-1)]  # (..., 3, 4)
+        assert l2w.shape == num_rays_shape + (3, 4)
+        if lidar_opt_to_lidar is not None:
+            l2w = pose_utils.multiply(l2w, lidar_opt_to_lidar)
+
+        points_world = transform_points_pairwise(points[..., :3], l2w)
+        origins = l2w[..., :3, 3]  # (..., 3)
+        assert origins.shape == num_rays_shape + (3,)
+        if points.shape[-1] >= 5 and self.metadata and "velocities" in self.metadata:
+            # Offset the point origins according to timediff and velocity
+            origins = origins + points[..., 4:5] * self.metadata["velocities"][lidar_indices.squeeze(-1)]
+            if not self.assume_ego_compensated:
+                # offset the world points according to velocity too
+                points_world = points_world + points[..., 4:5] * self.metadata["velocities"][lidar_indices.squeeze(-1)]
+        directions = points_world - origins
+        directions, distance = camera_utils.normalize_with_norm(directions, -1)
+        assert directions.shape == num_rays_shape + (3,)
+
+        # norms of the vector going between adjacent coords, giving us dx and dy per output ray
+        dx = self.horizontal_beam_divergence[lidar_indices.squeeze(-1)]  # ("num_rays":...,)
+        dy = self.vertical_beam_divergence[lidar_indices.squeeze(-1)]  # ("num_rays":...,)
+        pixel_area = dx * dy  # ("num_rays":..., 1)
+        assert pixel_area.shape == num_rays_shape + (1,)
+
+        metadata = (
+            self._apply_fn_to_dict(self.metadata, lambda x: x[lidar_indices.squeeze(-1)])
+            if self.metadata is not None
+            else None
+        )
+        if metadata is not None:
+            metadata["directions_norm"] = distance.detach()
+        else:
+            metadata = {"directions_norm": distance.detach()}
+        metadata["is_lidar"] = torch.ones_like(distance, dtype=torch.bool)
+        metadata["did_return"] = distance.detach() < self.valid_lidar_distance_threshold
+
+        times = self.times[lidar_indices, 0] if self.times is not None else None
+        times = times + points[..., 4:5]
+
+        return RayBundle(
+            origins=origins,
+            directions=directions,
+            pixel_area=pixel_area,
+            camera_indices=lidar_indices,
+            times=times,
+            metadata=metadata,
+            fars=torch.ones_like(pixel_area) * 1_000_000,  # TODO: is this cheating?
+        )
+
+    def _generate_rays_from_coords(
+        self,
+        lidar_indices: Int[Tensor, "*num_rays num_lidars_batch_dims"],
+        coords: Float[Tensor, "num_rays 2"],
+        lidar_opt_to_lidar: Optional[Float[Tensor, "*num_rays 3 4"]] = None,
+    ) -> RayBundle:
+        """Generates rays for the given camera indices and coords where self isn't jagged
+
+        This is a fairly complex function, so let's break this down slowly.
+
+        Shapes involved:
+            - num_rays: This is your output raybundle shape. It dictates the number and shape of the rays generated
+            - num_lidars_batch_dims: This is the number of dimensions of our camera
+
+        Args:
+            lidar_indices: Camera indices of the flattened lidars object to generate rays for.
+                The shape of this is such that indexing into lidar_indices["num_rays":...] will return the
+                index into each batch dimension of the camera in order to get the correct camera specified by
+                "num_rays".
+
+                Example:
+                    >>> lidars = lidars(...)
+                    >>> lidars.shape
+                        (2, 3, 4)
+
+                    >>> lidar_indices = torch.tensor([0, 0, 0]) # We need an axis of length 3 since lidars.ndim == 3
+                    >>> lidar_indices.shape
+                        (3,)
+                    >>> coords = torch.tensor([1,1])
+                    >>> coords.shape
+                        (2,)
+                    >>> out_rays = lidars.generate_rays(lidar_indices=lidar_indices, coords = coords)
+                        # This will generate a RayBundle with a single ray for the
+                        # camera at lidars[0,0,0] at image coordinates (1,1), so out_rays.shape == ()
+                    >>> out_rays.shape
+                        ()
+
+                    >>> lidar_indices = torch.tensor([[0,0,0]])
+                    >>> lidar_indices.shape
+                        (1, 3)
+                    >>> coords = torch.tensor([[1,1]])
+                    >>> coords.shape
+                        (1, 2)
+                    >>> out_rays = lidars.generate_rays(lidar_indices=lidar_indices, coords = coords)
+                        # This will generate a RayBundle with a single ray for the
+                        # camera at lidars[0,0,0] at point (1,1), so out_rays.shape == (1,)
+                        # since we added an extra dimension in front of lidar_indices
+                    >>> out_rays.shape
+                        (1,)
+
+                If you want more examples, check tests/lidars/test_lidars and the function check_generate_rays_shape
+
+                The bottom line is that for lidar_indices: (num_rays:..., num_lidars_batch_dims), num_rays is the
+                output shape and if you index into the output RayBundle with some indices [i:...], if you index into
+                lidar_indices with lidar_indices[i:...] as well, you will get a 1D tensor containing the batch
+                indices into the original lidars object corresponding to that ray (ie: you will get the camera
+                from our batched lidars corresponding to the ray at RayBundle[i:...]).
+
+            coords: Coordinates of the pixels to generate rays for. If None, the full image will be rendered, meaning
+                height and width get prepended to the num_rays dimensions. Indexing into coords with [i:...] will
+                get you the image coordinates [x, y] of that specific ray located at output RayBundle[i:...].
+
+            lidar_opt_to_lidar: Optional transform for the camera to world matrices.
+                In terms of shape, it follows the same rules as coords, but indexing into it with [i:...] gets you
+                the 2D camera to world transform matrix for the camera optimization at RayBundle[i:...].
+
+            distortion_params_delta: Optional delta for the distortion parameters.
+                In terms of shape, it follows the same rules as coords, but indexing into it with [i:...] gets you
+                the 1D tensor with the 6 distortion parameters for the camera optimization at RayBundle[i:...].
+
+            disable_distortion: If True, disables distortion.
+
+        Returns:
+            Rays for the given camera indices and coords. RayBundle.shape == num_rays
+        """
+        raise NotImplementedError("No lidar modeling yet")
+
+
+def transform_points(points, transform):
+    """Transforms points by a transform."""
+    points = points.clone()
+    rotations = transform[..., :3, :3]
+    translations = transform[..., :3, 3]
+    points[:, :3] = points[:, :3] @ rotations.swapaxes(-2, -1) + translations
+    return points
+
+
+@torch_compile(dynamic=True, mode="reduce-overhead", backend="eager")
+def transform_points_pairwise(points, transforms, with_translation=True):
+    """Transform points by a pairwise transform."""
+    # points: (*, 3)
+    # transforms: (*, 4, 4)
+    # return: (*, 3)
+    # points = points.clone()
+    rotations = transforms[..., :3, :3]
+    translations = transforms[..., :3, 3]
+
+    if with_translation:
+        return (points.unsqueeze(-2) @ rotations.swapaxes(-2, -1)).squeeze(-2) + translations
+    else:
+        return (points.unsqueeze(-2) @ rotations.swapaxes(-2, -1)).squeeze(-2)
+
+
+@torch_compile(dynamic=True, mode="reduce-overhead", backend="eager")
+def transform_points_by_batch(points, transforms, with_translation=True):
+    """Transform points by a batch of transforms."""
+    # points: (N, 3)
+    # transforms: (B, 4, 4)
+    # return: (B, N, 3)
+    batch_size = transforms.shape[0]
+    points = points.clone().unsqueeze(0).repeat(batch_size, 1, 1)
+    rotations = transforms[:, :3, :3]
+    translations = transforms[:, :3, 3]
+
+    if with_translation:
+        return points @ rotations.permute(0, 2, 1) + translations.unsqueeze(1)
+    else:
+        return points @ rotations.permute(0, 2, 1)
+
+
+def intensity_to_rgb(intensities: np.ndarray) -> np.ndarray:  # N -> N x 3
+    """Converts intensities to RGB values."""
+    # use log-scale for better visualization
+    log_intensities = np.log(1 + intensities * 255) / np.log(256)
+    return plt.cm.inferno(log_intensities)[:, :3]
diff --git a/nerfstudio/cameras/rays.py b/nerfstudio/cameras/rays.py
index 33476f92..d96eb948 100644
--- a/nerfstudio/cameras/rays.py
+++ b/nerfstudio/cameras/rays.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,13 +18,13 @@
 """
 import random
 from dataclasses import dataclass, field
-from typing import Callable, Dict, Literal, Optional, Tuple, Union, overload
+from typing import Callable, Dict, List, Literal, Optional, Tuple, Union, overload
 
 import torch
 from jaxtyping import Float, Int, Shaped
 from torch import Tensor
 
-from nerfstudio.utils.math import Gaussians, conical_frustum_to_gaussian
+from nerfstudio.utils.math import Gaussians, GaussiansStd, conical_frustum_to_gaussian, multisampled_frustum_to_gaussian
 from nerfstudio.utils.tensor_dataclass import TensorDataclass
 
 TORCH_DEVICE = Union[str, torch.device]
@@ -69,14 +70,14 @@ def set_offsets(self, offsets):
         """Sets offsets for this frustum for computing positions"""
         self.offsets = offsets
 
-    def get_gaussian_blob(self) -> Gaussians:
+    def get_conical_gaussian_blob(self) -> Gaussians:
         """Calculates guassian approximation of conical frustum.
 
         Returns:
             Conical frustums approximated by gaussian distribution.
         """
         # Cone radius is set such that the square pixel_area matches the cone area.
-        cone_radius = torch.sqrt(self.pixel_area) / 1.7724538509055159  # r = sqrt(pixel_area / pi)
+        cone_radius = torch.sqrt(self.pixel_area / torch.pi)
         if self.offsets is not None:
             raise NotImplementedError()
         return conical_frustum_to_gaussian(
@@ -87,6 +88,41 @@ def get_gaussian_blob(self) -> Gaussians:
             radius=cone_radius,
         )
 
+    def get_multisampled_gaussian_blob(self, rand: bool = False) -> GaussiansStd:
+        """Calculates Gaussian approximation of conical frustum via multisampling.
+        Returns:
+            Conical frustums approximated by multisampled gaussian distribution.
+        """
+        # Cone radius is set such that the square pixel_area matches the cone area.
+        cone_radius = torch.sqrt(self.pixel_area / torch.pi)
+        if self.offsets is not None:
+            raise NotImplementedError()
+        return multisampled_frustum_to_gaussian(
+            origins=self.origins,
+            directions=self.directions,
+            starts=self.starts,
+            ends=self.ends,
+            radius=cone_radius,
+            rand=rand,
+        )
+
+    def get_fast_isotropic_gaussian(self, num_multisamples: int) -> GaussiansStd:
+        """This is a sloppy but very fast approximation of the gaussian blob.
+
+        Args:
+            num_multisamples: Number of samples to use for the gaussian blob.
+
+        """
+        if self.offsets is not None:
+            raise NotImplementedError()
+        multisample_dist = (self.ends - self.starts) / (num_multisamples + 1)
+        ts = torch.arange(1, num_multisamples + 1, device=self.ends.device, dtype=self.ends.dtype)
+        t = self.starts + ts.unsqueeze(0) * multisample_dist
+        mean = self.origins.unsqueeze(-2) + self.directions.unsqueeze(-2) * t.unsqueeze(-1)
+        frust_crossection_area = self.pixel_area.unsqueeze(-2) * t.unsqueeze(-1).pow(2)
+        std = (frust_crossection_area * multisample_dist.unsqueeze(-2)).pow(1 / 3)  # TODO: * 0.5?
+        return GaussiansStd(mean=mean, std=std)
+
     @classmethod
     def get_mock_frustum(cls, device: Optional[TORCH_DEVICE] = "cpu") -> "Frustums":
         """Helper function to generate a placeholder frustum.
@@ -95,11 +131,11 @@ def get_mock_frustum(cls, device: Optional[TORCH_DEVICE] = "cpu") -> "Frustums":
             A size 1 frustum with meaningless values.
         """
         return Frustums(
-            origins=torch.ones((1, 3)).to(device),
-            directions=torch.ones((1, 3)).to(device),
-            starts=torch.ones((1, 1)).to(device),
-            ends=torch.ones((1, 1)).to(device),
-            pixel_area=torch.ones((1, 1)).to(device),
+            origins=torch.ones((1, 3), device=device),
+            directions=torch.ones((1, 3), device=device),
+            starts=torch.ones((1, 1), device=device),
+            ends=torch.ones((1, 1), device=device),
+            pixel_area=torch.ones((1, 1), device=device),
         )
 
 
@@ -121,10 +157,34 @@ class RaySamples(TensorDataclass):
     """Function to convert bins to euclidean distance."""
     metadata: Optional[Dict[str, Shaped[Tensor, "*bs latent_dims"]]] = None
     """additional information relevant to generating ray samples"""
-
     times: Optional[Float[Tensor, "*batch 1"]] = None
     """Times at which rays are sampled"""
 
+    def __init__(
+        self,
+        frustums: Frustums,
+        camera_indices: Optional[Int[Tensor, "*bs 1"]] = None,
+        deltas: Optional[Float[Tensor, "*bs 1"]] = None,
+        spacing_starts: Optional[Float[Tensor, "*bs num_samples 1"]] = None,
+        spacing_ends: Optional[Float[Tensor, "*bs num_samples 1"]] = None,
+        spacing_to_euclidean_fn: Optional[Callable] = None,
+        metadata: Optional[Dict[str, Shaped[Tensor, "*bs latent_dims"]]] = None,
+        times: Optional[Float[Tensor, "*batch 1"]] = None,
+    ) -> None:
+        # This will notify the tensordataclass that we have a field with more than 1 dimension
+        self._field_custom_dimensions = {"world2box": 2}
+
+        self.frustums = frustums
+        self.camera_indices = camera_indices
+        self.deltas = deltas
+        self.spacing_starts = spacing_starts
+        self.spacing_ends = spacing_ends
+        self.spacing_to_euclidean_fn = spacing_to_euclidean_fn
+        self.metadata = metadata
+        self.times = times
+
+        self.__post_init__()
+
     def get_weights(self, densities: Float[Tensor, "*batch num_samples 1"]) -> Float[Tensor, "*batch num_samples 1"]:
         """Return weights based on predicted densities
 
@@ -209,6 +269,8 @@ class RayBundle(TensorDataclass):
     """Additional metadata or data needed for interpolation, will mimic shape of rays"""
     times: Optional[Float[Tensor, "*batch 1"]] = None
     """Times at which rays are sampled"""
+    termination_distances: Optional[Float[Tensor, "*batch 1"]] = None
+    """Distance along ray where ray is measured to terminate. -1 if unknown and inf if ray does not terminate."""
 
     def set_camera_indices(self, camera_index: int) -> None:
         """Sets all the camera indices to a specific camera index.
@@ -293,3 +355,39 @@ def get_ray_samples(
         )
 
         return ray_samples
+
+
+@overload
+def merge_raysamples(ray_samples: List[RaySamples], sort: Literal[True]) -> Tuple[RaySamples, Int[Tensor, "num_rays"]]:
+    ...
+
+
+@overload
+def merge_raysamples(ray_samples: List[RaySamples], sort: Literal[False]) -> RaySamples:
+    ...
+
+
+def merge_raysamples(
+    ray_samples: List[RaySamples], sort: bool = False
+) -> Union[RaySamples, Tuple[RaySamples, Int[Tensor, "num_rays"]]]:
+    """Merges two RaySamples objects, and make sure rays are nicely packed."""
+    assert len(ray_samples[0].shape) == 1, "This function is overkill for batched ray samples, use .cat() instead."
+    merged = ray_samples[0].cat(
+        ray_samples[1:], dim=0, ignore_fields={"spacing_to_euclidean_fn", "spacing_starts", "spacing_ends"}
+    )
+    assert merged.metadata is not None and "ray_indices" in merged.metadata, "metadata must contain ray_indices"
+
+    if sort:
+        if merged.frustums.ends.device.type == "mps":
+            ends = merged.frustums.ends.cpu().double()
+            ray_indices = merged.metadata["ray_indices"].cpu()
+        else:
+            ends = merged.frustums.ends.double()
+            ray_indices = merged.metadata["ray_indices"]
+        score = ray_indices + (ends / (ends.max() + 1.0))
+        sorting = torch.argsort(score[..., 0], dim=0)
+        if merged.frustums.ends.device.type == "mps":
+            sorting.to(merged.frustums.ends.device)
+        return merged[sorting], sorting
+    else:
+        return merged
diff --git a/nerfstudio/configs/base_config.py b/nerfstudio/configs/base_config.py
index f5ffddda..fa969754 100644
--- a/nerfstudio/configs/base_config.py
+++ b/nerfstudio/configs/base_config.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,7 +15,6 @@
 
 """Base Configs"""
 
-
 from __future__ import annotations
 
 from dataclasses import dataclass, field
@@ -152,3 +152,5 @@ class ViewerConfig(PrintableConfig):
     """Scale for the camera frustums in the viewer."""
     default_composite_depth: bool = True
     """The default value for compositing depth. Turn off if you want to see the camera frustums without occlusions."""
+    max_points_per_cloud: Optional[int] = 2000
+    """Max number of points per point cloud."""
diff --git a/nerfstudio/configs/dataparser_configs.py b/nerfstudio/configs/dataparser_configs.py
index d9e11b30..efe37b56 100644
--- a/nerfstudio/configs/dataparser_configs.py
+++ b/nerfstudio/configs/dataparser_configs.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,40 +21,22 @@
 
 import tyro
 
-from nerfstudio.data.dataparsers.arkitscenes_dataparser import ARKitScenesDataParserConfig
+from nerfstudio.data.dataparsers.argoverse2_dataparser import Argoverse2DataParserConfig
 from nerfstudio.data.dataparsers.base_dataparser import DataParserConfig
-from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
-from nerfstudio.data.dataparsers.colmap_dataparser import ColmapDataParserConfig
-from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
-from nerfstudio.data.dataparsers.dycheck_dataparser import DycheckDataParserConfig
-from nerfstudio.data.dataparsers.instant_ngp_dataparser import InstantNGPDataParserConfig
+from nerfstudio.data.dataparsers.kittimot_dataparser import KittiMotDataParserConfig
 from nerfstudio.data.dataparsers.minimal_dataparser import MinimalDataParserConfig
-from nerfstudio.data.dataparsers.nerfosr_dataparser import NeRFOSRDataParserConfig
-from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig
 from nerfstudio.data.dataparsers.nuscenes_dataparser import NuScenesDataParserConfig
-from nerfstudio.data.dataparsers.phototourism_dataparser import PhototourismDataParserConfig
-from nerfstudio.data.dataparsers.scannet_dataparser import ScanNetDataParserConfig
-from nerfstudio.data.dataparsers.scannetpp_dataparser import ScanNetppDataParserConfig
-from nerfstudio.data.dataparsers.sdfstudio_dataparser import SDFStudioDataParserConfig
-from nerfstudio.data.dataparsers.sitcoms3d_dataparser import Sitcoms3DDataParserConfig
+from nerfstudio.data.dataparsers.pandaset_dataparser import PandaSetDataParserConfig
+from nerfstudio.data.dataparsers.zod_dataparser import ZodDataParserConfig
 from nerfstudio.plugins.registry_dataparser import discover_dataparsers
 
 dataparsers = {
-    "nerfstudio-data": NerfstudioDataParserConfig(),
     "minimal-parser": MinimalDataParserConfig(),
-    "arkit-data": ARKitScenesDataParserConfig(),
-    "blender-data": BlenderDataParserConfig(),
-    "instant-ngp-data": InstantNGPDataParserConfig(),
+    "kittimot-data": KittiMotDataParserConfig(),
     "nuscenes-data": NuScenesDataParserConfig(),
-    "dnerf-data": DNeRFDataParserConfig(),
-    "phototourism-data": PhototourismDataParserConfig(),
-    "dycheck-data": DycheckDataParserConfig(),
-    "scannet-data": ScanNetDataParserConfig(),
-    "sdfstudio-data": SDFStudioDataParserConfig(),
-    "nerfosr-data": NeRFOSRDataParserConfig(),
-    "sitcoms3d-data": Sitcoms3DDataParserConfig(),
-    "scannetpp-data": ScanNetppDataParserConfig(),
-    "colmap": ColmapDataParserConfig(),
+    "argoverse2-data": Argoverse2DataParserConfig(),
+    "zod-data": ZodDataParserConfig(),
+    "pandaset-data": PandaSetDataParserConfig(),
 }
 
 external_dataparsers, _ = discover_dataparsers()
diff --git a/nerfstudio/configs/experiment_config.py b/nerfstudio/configs/experiment_config.py
index c93530fc..83e19ffd 100644
--- a/nerfstudio/configs/experiment_config.py
+++ b/nerfstudio/configs/experiment_config.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -75,6 +76,8 @@ class ExperimentConfig(InstantiateConfig):
     """Relative path to save all checkpoints."""
     load_scheduler: bool = True
     """Whether to load the scheduler state_dict to resume training, if it exists."""
+    load_optimizer: bool = True
+    """Whether to load the optimizer state_dict to resume training, if it exists."""
 
     def is_viewer_legacy_enabled(self) -> bool:
         """Checks if the legacy viewer is enabled."""
diff --git a/nerfstudio/configs/external_methods.py b/nerfstudio/configs/external_methods.py
index a0982f09..06579495 100644
--- a/nerfstudio/configs/external_methods.py
+++ b/nerfstudio/configs/external_methods.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -42,171 +43,20 @@ class ExternalMethod:
 
 external_methods = []
 
-# Instruct-NeRF2NeRF
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]Instruct-NeRF2NeRF[/bold yellow]
-For more information visit: https://docs.nerf.studio/nerfology/methods/in2n.html
-
-To enable Instruct-NeRF2NeRF, you must install it first by running:
-  [grey]pip install git+https://github.com/ayaanzhaque/instruct-nerf2nerf[/grey]""",
-        configurations=[
-            ("in2n", "Instruct-NeRF2NeRF. Full model, used in paper"),
-            ("in2n-small", "Instruct-NeRF2NeRF. Half precision model"),
-            ("in2n-tiny", "Instruct-NeRF2NeRF. Half prevision with no LPIPS"),
-        ],
-        pip_package="git+https://github.com/ayaanzhaque/instruct-nerf2nerf",
-    )
-)
-
-# K-Planes
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]K-Planes[/bold yellow]
-For more information visit https://docs.nerf.studio/nerfology/methods/kplanes.html
-
-To enable K-Planes, you must install it first by running:
-  [grey]pip install kplanes-nerfstudio[/grey]""",
-        configurations=[
-            ("kplanes", "K-Planes model tuned to static blender scenes"),
-            ("kplanes-dynamic", "K-Planes model tuned to dynamic DNeRF scenes"),
-        ],
-        pip_package="kplanes-nerfstudio",
-    )
-)
-
-# LERF
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]LERF[/bold yellow]
-For more information visit: https://docs.nerf.studio/nerfology/methods/lerf.html
-
-To enable LERF, you must install it first by running:
-  [grey]pip install git+https://github.com/kerrj/lerf[/grey]""",
-        configurations=[
-            ("lerf-big", "LERF with OpenCLIP ViT-L/14"),
-            ("lerf", "LERF with OpenCLIP ViT-B/16, used in paper"),
-            ("lerf-lite", "LERF with smaller network and less LERF samples"),
-        ],
-        pip_package="git+https://github.com/kerrj/lerf",
-    )
-)
-
-# Tetra-NeRF
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]Tetra-NeRF[/bold yellow]
-For more information visit: https://docs.nerf.studio/nerfology/methods/tetranerf.html
-
-To enable Tetra-NeRF, you must install it first. Please follow the instructions here:
-  https://github.com/jkulhanek/tetra-nerf/blob/master/README.md#installation""",
-        configurations=[
-            ("tetra-nerf-original", "Tetra-NeRF. Official implementation from the paper"),
-            ("tetra-nerf", "Tetra-NeRF. Different sampler - faster and better"),
-        ],
-    )
-)
-
-# NeRFPlayer
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]NeRFPlayer[/bold yellow]
-For more information visit: https://docs.nerf.studio/nerfology/methods/nerfplayer.html
-
-To enable NeRFPlayer, you must install it first by running:
-  [grey]pip install git+https://github.com/lsongx/nerfplayer-nerfstudio[/grey]""",
-        configurations=[
-            ("nerfplayer-nerfacto", "NeRFPlayer with nerfacto backbone"),
-            ("nerfplayer-ngp", "NeRFPlayer with instang-ngp-bounded backbone"),
-        ],
-    )
-)
-
-# Volinga
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]Volinga[/bold yellow]
-For more information visit: https://docs.nerf.studio/extensions/unreal_engine.html
-
-To enable Volinga, you must install it first by running:
-  [grey]pip install git+https://github.com/Volinga/volinga-model[/grey]""",
-        configurations=[
-            (
-                "volinga",
-                "Real-time rendering model from Volinga. Directly exportable to NVOL format at https://volinga.ai/",
-            ),
-        ],
-        pip_package="git+https://github.com/Volinga/volinga-model",
-    )
-)
-
-# Instruct-GS2GS
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]Instruct-GS2GS[/bold yellow]
-For more information visit: https://docs.nerf.studio/nerfology/methods/igs2gs.html
-
-To enable Instruct-GS2GS, you must install it first by running:
-  [grey]pip install git+https://github.com/cvachha/instruct-gs2gs[/grey]""",
-        configurations=[("igs2gs", "Instruct-GS2GS. Full model, used in paper")],
-        pip_package="git+https://github.com/cvachha/instruct-gs2gs",
-    )
-)
-
-# PyNeRF
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]PyNeRF[/bold yellow]
-For more information visit https://docs.nerf.studio/nerfology/methods/pynerf.html
-
-To enable PyNeRF, you must install it first by running:
-  [grey]pip install git+https://github.com/hturki/pynerf[/grey]""",
-        configurations=[
-            ("pynerf", "PyNeRF with proposal network. The default parameters are suited for outdoor scenes."),
-            (
-                "pynerf-synthetic",
-                "PyNeRF with proposal network. The default parameters are suited for synthetic scenes.",
-            ),
-            (
-                "pynerf-occupancy-grid",
-                "PyNeRF with occupancy grid. The default parameters are suited for synthetic scenes.",
-            ),
-        ],
-        pip_package="git+https://github.com/hturki/pynerf",
-    )
-)
-
-# SeaThru-NeRF
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]Seathru-NeRF[/bold yellow]
-For more information visit https://docs.nerf.studio/nerfology/methods/seathru_nerf.html
-
-To enable Seathru-NeRF, you must install it first by running:
-  [grey]pip install git+https://github.com/AkerBP/seathru_nerf[/grey]""",
-        configurations=[
-            ("seathru-nerf", "SeaThru-NeRF for underwater scenes."),
-            ("seathru-nerf-lite", "SeaThru-NeRF for underwater scenes (smaller networks and batches)."),
-        ],
-        pip_package="git+https://github.com/AkerBP/seathru_nerf",
-    )
-)
-
-# Zip-NeRF
-external_methods.append(
-    ExternalMethod(
-        """[bold yellow]Zip-NeRF[/bold yellow]
-For more information visit https://docs.nerf.studio/nerfology/methods/zipnerf.html
-
-To enable Zip-NeRF, you must install it first by running:
-  [grey]pip install git+https://github.com/SuLvXiangXin/zipnerf-pytorch#subdirectory=extensions/cuda 
-  and pip install git+https://github.com/SuLvXiangXin/zipnerf-pytorch[/grey]""",
-        configurations=[
-            ("zipnerf", "A pytorch implementation of 'Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields'")
-        ],
-        pip_package="pip install git+https://github.com/SuLvXiangXin/zipnerf-pytorch",
-    )
-)
+# TODO: add UniSim
+# external_methods.append(
+#     ExternalMethod(
+#         """[bold yellow]UniSim[/bold yellow]
+
+# To enable UniSim, you must install it first by running:
+#   [grey]pip install git+https://github.com/.../UniSim[/grey]""",
+#         configurations=[
+#             ("unisim", "UniSim reproduction, as specified in the paper"),
+#             ("unisim-tweaked", "UniSim with tweaked hyperparameters"),
+#         ],
+#         pip_package="git+https://github.com/.../unisim",
+#     )
+# )
 
 
 @dataclass
diff --git a/nerfstudio/configs/method_configs.py b/nerfstudio/configs/method_configs.py
index a7882399..9c6986fd 100644
--- a/nerfstudio/configs/method_configs.py
+++ b/nerfstudio/configs/method_configs.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,68 +20,36 @@
 from __future__ import annotations
 
 from collections import OrderedDict
+from copy import deepcopy
 from typing import Dict, Union
 
 import tyro
 
-from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig
-from nerfstudio.configs.base_config import ViewerConfig
+from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig, ScaledCameraOptimizerConfig
+from nerfstudio.configs.base_config import LoggingConfig, ViewerConfig
 from nerfstudio.configs.external_methods import ExternalMethodDummyTrainerConfig, get_external_methods
-from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager, VanillaDataManagerConfig
+from nerfstudio.data.datamanagers.ad_datamanager import ADDataManagerConfig
 from nerfstudio.data.datamanagers.full_images_datamanager import FullImageDatamanagerConfig
 from nerfstudio.data.datamanagers.parallel_datamanager import ParallelDataManagerConfig
-from nerfstudio.data.datamanagers.random_cameras_datamanager import RandomCamerasDataManagerConfig
-from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
-from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
-from nerfstudio.data.dataparsers.instant_ngp_dataparser import InstantNGPDataParserConfig
-from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig
-from nerfstudio.data.dataparsers.phototourism_dataparser import PhototourismDataParserConfig
-from nerfstudio.data.dataparsers.sdfstudio_dataparser import SDFStudioDataParserConfig
-from nerfstudio.data.dataparsers.sitcoms3d_dataparser import Sitcoms3DDataParserConfig
-from nerfstudio.data.datasets.depth_dataset import DepthDataset
-from nerfstudio.data.datasets.sdf_dataset import SDFDataset
-from nerfstudio.data.datasets.semantic_dataset import SemanticDataset
-from nerfstudio.data.pixel_samplers import PairPixelSamplerConfig
-from nerfstudio.engine.optimizers import AdamOptimizerConfig, RAdamOptimizerConfig
-from nerfstudio.engine.schedulers import (
-    CosineDecaySchedulerConfig,
-    ExponentialDecaySchedulerConfig,
-    MultiStepSchedulerConfig,
-)
+from nerfstudio.data.dataparsers.ad_dataparser import ADDataParserConfig
+from nerfstudio.engine.optimizers import AdamOptimizerConfig, AdamWOptimizerConfig, RAdamOptimizerConfig
+from nerfstudio.engine.schedulers import ExponentialDecaySchedulerConfig
 from nerfstudio.engine.trainer import TrainerConfig
-from nerfstudio.field_components.temporal_distortions import TemporalDistortionKind
-from nerfstudio.fields.sdf_field import SDFFieldConfig
-from nerfstudio.models.depth_nerfacto import DepthNerfactoModelConfig
-from nerfstudio.models.generfacto import GenerfactoModelConfig
-from nerfstudio.models.instant_ngp import InstantNGPModelConfig
-from nerfstudio.models.mipnerf import MipNerfModel
+from nerfstudio.models.lidar_nerfacto import LidarNerfactoModelConfig
 from nerfstudio.models.nerfacto import NerfactoModelConfig
-from nerfstudio.models.neus import NeuSModelConfig
-from nerfstudio.models.neus_facto import NeuSFactoModelConfig
-from nerfstudio.models.semantic_nerfw import SemanticNerfWModelConfig
+from nerfstudio.models.neurad import NeuRADModelConfig
 from nerfstudio.models.splatfacto import SplatfactoModelConfig
-from nerfstudio.models.tensorf import TensoRFModelConfig
-from nerfstudio.models.vanilla_nerf import NeRFModel, VanillaModelConfig
+from nerfstudio.pipelines.ad_pipeline import ADPipelineConfig
 from nerfstudio.pipelines.base_pipeline import VanillaPipelineConfig
-from nerfstudio.pipelines.dynamic_batch import DynamicBatchPipelineConfig
 from nerfstudio.plugins.registry import discover_methods
 
 method_configs: Dict[str, Union[TrainerConfig, ExternalMethodDummyTrainerConfig]] = {}
 descriptions = {
-    "nerfacto": "Recommended real-time model tuned for real captures. This model will be continually updated.",
-    "depth-nerfacto": "Nerfacto with depth supervision.",
-    "instant-ngp": "Implementation of Instant-NGP. Recommended real-time model for unbounded scenes.",
-    "instant-ngp-bounded": "Implementation of Instant-NGP. Recommended for bounded real and synthetic scenes",
-    "mipnerf": "High quality model for bounded scenes. (slow)",
-    "semantic-nerfw": "Predicts semantic segmentations and filters out transient objects.",
-    "vanilla-nerf": "Original NeRF model. (slow)",
-    "tensorf": "tensorf",
-    "dnerf": "Dynamic-NeRF model. (slow)",
-    "phototourism": "Uses the Phototourism data.",
-    "generfacto": "Generative Text to NeRF model",
-    "neus": "Implementation of NeuS. (slow)",
-    "neus-facto": "Implementation of NeuS-Facto. (slow)",
+    "nerfacto": "Nerfstudio's default model.",
+    "nerfacto-lidar": "Nerfacto with lidar supervision.",
     "splatfacto": "Gaussian Splatting model",
+    "neurad": "Continuously improving version of NeuRAD.",
+    "neurad-paper": "NeuRAD with settings matching the paper.",
 }
 
 method_configs["nerfacto"] = TrainerConfig(
@@ -91,7 +60,7 @@
     mixed_precision=True,
     pipeline=VanillaPipelineConfig(
         datamanager=ParallelDataManagerConfig(
-            dataparser=NerfstudioDataParserConfig(),
+            dataparser=ADDataParserConfig(),
             train_num_rays_per_batch=4096,
             eval_num_rays_per_batch=4096,
         ),
@@ -127,7 +96,7 @@
     mixed_precision=True,
     pipeline=VanillaPipelineConfig(
         datamanager=ParallelDataManagerConfig(
-            dataparser=NerfstudioDataParserConfig(),
+            dataparser=ADDataParserConfig(),
             train_num_rays_per_batch=8192,
             eval_num_rays_per_batch=4096,
         ),
@@ -171,7 +140,7 @@
     mixed_precision=True,
     pipeline=VanillaPipelineConfig(
         datamanager=ParallelDataManagerConfig(
-            dataparser=NerfstudioDataParserConfig(),
+            dataparser=ADDataParserConfig(),
             train_num_rays_per_batch=16384,
             eval_num_rays_per_batch=4096,
         ),
@@ -211,21 +180,15 @@
     vis="viewer",
 )
 
-method_configs["depth-nerfacto"] = TrainerConfig(
-    method_name="depth-nerfacto",
+method_configs["nerfacto-lidar"] = TrainerConfig(
+    method_name="nerfacto-lidar",
     steps_per_eval_batch=500,
     steps_per_save=2000,
     max_num_iterations=30000,
     mixed_precision=True,
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            _target=VanillaDataManager[DepthDataset],
-            pixel_sampler=PairPixelSamplerConfig(),
-            dataparser=NerfstudioDataParserConfig(),
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=4096,
-        ),
-        model=DepthNerfactoModelConfig(
+    pipeline=ADPipelineConfig(
+        calc_fid_steps=(99999999,),
+        model=LidarNerfactoModelConfig(
             eval_num_rays_per_chunk=1 << 15,
             camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
         ),
@@ -239,352 +202,9 @@
             "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
             "scheduler": None,
         },
-        "camera_opt": {
-            "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-    vis="viewer",
-)
-
-method_configs["instant-ngp"] = TrainerConfig(
-    method_name="instant-ngp",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=DynamicBatchPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            dataparser=NerfstudioDataParserConfig(),
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=4096,
-        ),
-        model=InstantNGPModelConfig(eval_num_rays_per_chunk=8192),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
-        }
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 12),
-    vis="viewer",
-)
-
-method_configs["instant-ngp-bounded"] = TrainerConfig(
-    method_name="instant-ngp-bounded",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=DynamicBatchPipelineConfig(
-        datamanager=VanillaDataManagerConfig(dataparser=InstantNGPDataParserConfig(), train_num_rays_per_batch=8192),
-        model=InstantNGPModelConfig(
-            eval_num_rays_per_chunk=8192,
-            grid_levels=1,
-            alpha_thre=0.0,
-            cone_angle=0.0,
-            disable_scene_contraction=True,
-            near_plane=0.01,
-            background_color="black",
-        ),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
-        }
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 12),
-    vis="viewer",
-)
-#
-#
-method_configs["mipnerf"] = TrainerConfig(
-    method_name="mipnerf",
-    pipeline=VanillaPipelineConfig(
-        datamanager=ParallelDataManagerConfig(dataparser=NerfstudioDataParserConfig(), train_num_rays_per_batch=1024),
-        model=VanillaModelConfig(
-            _target=MipNerfModel,
-            loss_coefficients={"rgb_loss_coarse": 0.1, "rgb_loss_fine": 1.0},
-            num_coarse_samples=128,
-            num_importance_samples=128,
-            eval_num_rays_per_chunk=1024,
-        ),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
+        "cam_opt": {
+            "optimizer": AdamOptimizerConfig(lr=6e-4, eps=1e-15),
             "scheduler": None,
-        }
-    },
-)
-
-method_configs["semantic-nerfw"] = TrainerConfig(
-    method_name="semantic-nerfw",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            _target=VanillaDataManager[SemanticDataset],
-            dataparser=Sitcoms3DDataParserConfig(),
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=8192,
-        ),
-        model=SemanticNerfWModelConfig(eval_num_rays_per_chunk=1 << 16),
-    ),
-    optimizers={
-        "proposal_networks": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": None,
-        },
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": None,
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 16),
-    vis="viewer",
-)
-
-method_configs["vanilla-nerf"] = TrainerConfig(
-    method_name="vanilla-nerf",
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            dataparser=BlenderDataParserConfig(),
-        ),
-        model=VanillaModelConfig(_target=NeRFModel),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
-            "scheduler": None,
-        },
-        "temporal_distortion": {
-            "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
-            "scheduler": None,
-        },
-    },
-)
-
-method_configs["tensorf"] = TrainerConfig(
-    method_name="tensorf",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=False,
-    pipeline=VanillaPipelineConfig(
-        datamanager=ParallelDataManagerConfig(
-            dataparser=BlenderDataParserConfig(),
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=4096,
-        ),
-        model=TensoRFModelConfig(
-            regularization="tv",
-            camera_optimizer=CameraOptimizerConfig(mode="off"),
-        ),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=0.001),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=30000),
-        },
-        "encodings": {
-            "optimizer": AdamOptimizerConfig(lr=0.02),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.002, max_steps=30000),
-        },
-        "camera_opt": {
-            "optimizer": AdamOptimizerConfig(lr=1e-4, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-5, max_steps=5000),
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-    vis="viewer",
-)
-
-method_configs["dnerf"] = TrainerConfig(
-    method_name="dnerf",
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(dataparser=DNeRFDataParserConfig()),
-        model=VanillaModelConfig(
-            _target=NeRFModel,
-            enable_temporal_distortion=True,
-            temporal_distortion_params={"kind": TemporalDistortionKind.DNERF},
-        ),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
-            "scheduler": None,
-        },
-        "temporal_distortion": {
-            "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
-            "scheduler": None,
-        },
-    },
-)
-
-method_configs["phototourism"] = TrainerConfig(
-    method_name="phototourism",
-    steps_per_eval_batch=500,
-    steps_per_save=2000,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            dataparser=PhototourismDataParserConfig(),  # NOTE: one of the only differences with nerfacto
-            train_num_rays_per_batch=4096,
-            eval_num_rays_per_batch=4096,
-            # Large dataset, so using prior values from VariableResDataManager.
-            train_num_images_to_sample_from=40,
-            train_num_times_to_repeat_images=100,
-            eval_num_images_to_sample_from=40,
-            eval_num_times_to_repeat_images=100,
-        ),
-        model=NerfactoModelConfig(
-            eval_num_rays_per_chunk=1 << 15,
-            camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
-        ),
-    ),
-    optimizers={
-        "proposal_networks": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": None,
-        },
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": None,
-        },
-        "camera_opt": {
-            "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-    vis="viewer",
-)
-
-method_configs["generfacto"] = TrainerConfig(
-    method_name="generfacto",
-    experiment_name="",
-    steps_per_eval_batch=50,
-    steps_per_eval_image=50,
-    steps_per_save=200,
-    max_num_iterations=30000,
-    mixed_precision=True,
-    pipeline=VanillaPipelineConfig(
-        datamanager=RandomCamerasDataManagerConfig(
-            horizontal_rotation_warmup=3000,
-        ),
-        model=GenerfactoModelConfig(
-            eval_num_rays_per_chunk=1 << 15,
-            distortion_loss_mult=1.0,
-            interlevel_loss_mult=100.0,
-            max_res=256,
-            sphere_collider=True,
-            initialize_density=True,
-            taper_range=(0, 2000),
-            random_background=True,
-            proposal_warmup=2000,
-            proposal_update_every=0,
-            proposal_weights_anneal_max_num_iters=2000,
-            start_lambertian_training=500,
-            start_normals_training=2000,
-            opacity_loss_mult=0.001,
-            positional_prompting="discrete",
-            guidance_scale=25,
-        ),
-    ),
-    optimizers={
-        "proposal_networks": {
-            "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
-            "scheduler": None,
-        },
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
-            "scheduler": None,
-        },
-    },
-    viewer=ViewerConfig(),
-    vis="viewer",
-)
-
-method_configs["neus"] = TrainerConfig(
-    method_name="neus",
-    steps_per_eval_image=500,
-    steps_per_eval_batch=5000,
-    steps_per_save=20000,
-    steps_per_eval_all_images=1000000,  # set to a very large number so we don't eval with all images
-    max_num_iterations=100000,
-    mixed_precision=False,
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            _target=VanillaDataManager[SDFDataset],
-            dataparser=SDFStudioDataParserConfig(),
-            train_num_rays_per_batch=1024,
-            eval_num_rays_per_batch=1024,
-        ),
-        model=NeuSModelConfig(eval_num_rays_per_chunk=1024),
-    ),
-    optimizers={
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
-            "scheduler": CosineDecaySchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000),
-        },
-        "field_background": {
-            "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
-            "scheduler": CosineDecaySchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000),
-        },
-    },
-    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-    vis="viewer",
-)
-
-method_configs["neus-facto"] = TrainerConfig(
-    method_name="neus-facto",
-    steps_per_eval_image=5000,
-    steps_per_eval_batch=5000,
-    steps_per_save=2000,
-    steps_per_eval_all_images=1000000,  # set to a very large model so we don't eval with all images
-    max_num_iterations=20001,
-    mixed_precision=False,
-    pipeline=VanillaPipelineConfig(
-        datamanager=VanillaDataManagerConfig(
-            _target=VanillaDataManager[SDFDataset],
-            dataparser=SDFStudioDataParserConfig(),
-            train_num_rays_per_batch=2048,
-            eval_num_rays_per_batch=2048,
-        ),
-        model=NeuSFactoModelConfig(
-            # proposal network allows for significantly smaller sdf/color network
-            sdf_field=SDFFieldConfig(
-                use_grid_feature=True,
-                num_layers=2,
-                num_layers_color=2,
-                hidden_dim=256,
-                bias=0.5,
-                beta_init=0.8,
-                use_appearance_embedding=False,
-            ),
-            background_model="none",
-            eval_num_rays_per_chunk=2048,
-        ),
-    ),
-    optimizers={
-        "proposal_networks": {
-            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-            "scheduler": MultiStepSchedulerConfig(max_steps=20001, milestones=(10000, 1500, 18000)),
-        },
-        "fields": {
-            "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
-            "scheduler": CosineDecaySchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20001),
-        },
-        "field_background": {
-            "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
-            "scheduler": CosineDecaySchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20001),
         },
     },
     viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
@@ -601,7 +221,7 @@
     mixed_precision=False,
     pipeline=VanillaPipelineConfig(
         datamanager=FullImageDatamanagerConfig(
-            dataparser=NerfstudioDataParserConfig(load_3D_points=True),
+            dataparser=ADDataParserConfig(),  # load_3D_points=True),
             cache_images_type="uint8",
         ),
         model=SplatfactoModelConfig(),
@@ -650,7 +270,7 @@
     mixed_precision=False,
     pipeline=VanillaPipelineConfig(
         datamanager=FullImageDatamanagerConfig(
-            dataparser=NerfstudioDataParserConfig(load_3D_points=True),
+            dataparser=ADDataParserConfig(),  # load_3D_points=True),
             cache_images_type="uint8",
         ),
         model=SplatfactoModelConfig(
@@ -692,6 +312,109 @@
     vis="viewer",
 )
 
+method_configs["neurad"] = TrainerConfig(
+    method_name="neurad",
+    steps_per_eval_batch=500,
+    steps_per_eval_all_images=5000,
+    steps_per_save=2000,
+    max_num_iterations=20001,
+    mixed_precision=True,
+    pipeline=ADPipelineConfig(
+        calc_fid_steps=(99999999,),
+        datamanager=ADDataManagerConfig(dataparser=ADDataParserConfig(add_missing_points=True)),
+        model=NeuRADModelConfig(
+            eval_num_rays_per_chunk=1 << 15,
+            camera_optimizer=CameraOptimizerConfig(mode="off"),  # SO3xR3
+        ),
+    ),
+    optimizers={
+        "trajectory_opt": {
+            "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=20001, warmup_steps=2500),
+        },
+        "cnn": {
+            "optimizer": AdamWOptimizerConfig(lr=1e-3, eps=1e-15, weight_decay=1e-6),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=20001, warmup_steps=2500),
+        },
+        "fields": {
+            "optimizer": AdamWOptimizerConfig(lr=1e-2, eps=1e-15, weight_decay=1e-7),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-3, max_steps=20001, warmup_steps=500),
+        },
+        "hashgrids": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-3, max_steps=20001, warmup_steps=500),
+        },
+        "camera_opt": {
+            "optimizer": AdamOptimizerConfig(lr=1e-4, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-5, max_steps=20001, warmup_steps=2500),
+        },
+    },
+    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
+    vis="viewer",
+    logging=LoggingConfig(steps_per_log=100),
+)
+
+# With scaled camera optimizer (tuned for nuscenes)
+method_configs["neurad-scaleopt"] = deepcopy(method_configs["neurad"])
+method_configs["neurad-scaleopt"].method_name = "neurad-scaleopt"
+method_configs["neurad-scaleopt"].pipeline.model.camera_optimizer = ScaledCameraOptimizerConfig(
+    weights=(1.0, 1.0, 0.01, 0.01, 0.01, 1.0),  # xrot, yrot, zrot, xtrans, ytrans, ztrans
+    trans_l2_penalty=(1e-2, 1e-2, 1e-3),  # x, y, z
+)
+
+
+def _scaled_neurad_training(config: TrainerConfig, scale: float, newname: str) -> TrainerConfig:
+    config = deepcopy(config)
+    config.method_name = newname
+    config.max_num_iterations = int((config.max_num_iterations - 1) * scale + 1)
+    config.steps_per_eval_batch = int(config.steps_per_eval_batch * scale)
+    config.steps_per_eval_image = int(config.steps_per_eval_image * scale)
+    config.steps_per_eval_all_images = int(config.steps_per_eval_all_images * scale)
+    config.steps_per_save = int(config.steps_per_save * scale)
+    config.pipeline.calc_fid_steps = tuple(int(scale * s) for s in config.pipeline.calc_fid_steps)
+    for optimizer in config.optimizers.values():
+        optimizer["scheduler"].max_steps = int(optimizer["scheduler"].max_steps * scale)
+        optimizer["scheduler"].warmup_steps = int(optimizer["scheduler"].warmup_steps * scale)
+    return config
+
+
+# Bigger, better, longer, stronger
+method_configs["neurader"] = _scaled_neurad_training(method_configs["neurad"], 2.5, "neurader")
+method_configs["neurader"].method_name = "neurader"
+for optimizer in method_configs["neurader"].optimizers.values():
+    optimizer["optimizer"].lr *= 0.5
+    optimizer["scheduler"].lr_final *= 0.5
+model: NeuRADModelConfig = method_configs["neurader"].pipeline.model
+for field in (model.field, model.sampling.proposal_field_1, model.sampling.proposal_field_2):
+    field.grid.static.max_res *= 2
+    field.grid.static.base_res *= 2
+    field.grid.static.log2_hashmap_size += 1
+    field.grid.actor.log2_hashmap_size += 1
+
+method_configs["neurader-scaleopt"] = deepcopy(method_configs["neurader"])
+method_configs["neurader-scaleopt"].method_name = "neurader-scaleopt"
+method_configs["neurader-scaleopt"].pipeline.model.camera_optimizer = ScaledCameraOptimizerConfig(
+    weights=(1.0, 1.0, 0.01, 0.01, 0.01, 1.0)  # xrot, yrot, -zrot-, -xtrans-, -ytrans-, ztrans
+)
+
+# Even longer training
+method_configs["neuradest"] = _scaled_neurad_training(method_configs["neurader"], 3, "neuradest")
+method_configs["neuradest-scaleopt"] = _scaled_neurad_training(
+    method_configs["neurader-scaleopt"], 3, "neuradest-scaleopt"
+)
+
+# Configurations matching the paper (disable temporal appearance and actor flip)
+method_configs["neurad-paper"] = deepcopy(method_configs["neurad"])
+method_configs["neurad-paper"].method_name = "neurad-paper"
+method_configs["neurad-paper"].pipeline.model.use_temporal_appearance = False  # type: ignore
+for f in method_configs["neurad-paper"].pipeline.model.fields:  # type: ignore
+    f.flip_prob = 0.0
+method_configs["neurad-2x-paper"] = deepcopy(method_configs["neurader"])
+method_configs["neurad-2x-paper"].method_name = "neurad-paper"
+method_configs["neurad-2x-paper"].pipeline.model.use_temporal_appearance = False  # type: ignore
+for f in method_configs["neurad-paper"].pipeline.model.fields:  # type: ignore
+    f.flip_prob = 0.0
+
 
 def merge_methods(methods, method_descriptions, new_methods, new_descriptions, overwrite=True):
     """Merge new methods and descriptions into existing methods and descriptions.
diff --git a/nerfstudio/data/datamanagers/ad_datamanager.py b/nerfstudio/data/datamanagers/ad_datamanager.py
new file mode 100644
index 00000000..39fe6985
--- /dev/null
+++ b/nerfstudio/data/datamanagers/ad_datamanager.py
@@ -0,0 +1,125 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Type
+
+import torch
+from rich.console import Console
+
+from nerfstudio.cameras.lidars import transform_points
+from nerfstudio.data.datamanagers.image_lidar_datamanager import ImageLidarDataManager, ImageLidarDataManagerConfig
+from nerfstudio.data.datasets.base_dataset import InputDataset
+from nerfstudio.data.pixel_samplers import ScaledPatchSamplerConfig
+from nerfstudio.data.utils.data_utils import remove_dynamic_points
+
+CONSOLE = Console(width=120)
+
+
+@dataclass
+class ADDataManagerConfig(ImageLidarDataManagerConfig):
+    """A basic data manager"""
+
+    _target: Type = field(default_factory=lambda: ADDataManager)
+    """Target class to instantiate."""
+    train_num_lidar_rays_per_batch: int = 16384
+    """Number of lidar rays per batch to use per training iteration."""
+    train_num_rays_per_batch: int = 40960
+    """Number of camera rays per batch to use per training iteration (equals 40 32x32 patches)."""
+    eval_num_lidar_rays_per_batch: int = 8192
+    """Number of lidar rays per batch to use per eval iteration."""
+    eval_num_rays_per_batch: int = 40960
+    """Number of camera rays per batch to use per eval iteration (equals 40 32x32 patches)."""
+    downsample_factor: float = 1
+    """Downsample factor for the lidar. If <1, downsample will be used."""
+    image_divisible_by: int = 1
+    """If >1, images will be cropped to be divisible by this number."""
+    pixel_sampler: ScaledPatchSamplerConfig = field(default_factory=ScaledPatchSamplerConfig)
+    """AD models default to path-based pixel sampler."""
+
+
+class ADDataManager(ImageLidarDataManager):
+    """This extends the VanillaDataManager to support lidar data.
+
+    Args:
+        config: the DataManagerConfig used to instantiate class
+    """
+
+    config: ADDataManagerConfig
+
+    def create_eval_dataset(self) -> InputDataset:
+        dataset = super().create_eval_dataset()
+        # Maybe crop images
+        cams = dataset.cameras
+        for width in cams.width.unique().tolist():
+            width_crop = _find_smallest_crop(width, self.config.image_divisible_by)
+            cams.width[cams.width == width] = width - width_crop
+        for height in cams.height.unique().tolist():
+            height_crop = _find_smallest_crop(height, self.config.image_divisible_by)
+            cams.height[cams.height == height] = height - height_crop
+        return dataset
+
+    def change_patch_sampler(self, patch_scale: int, patch_size: int):
+        """Change the camera sample to sample rays in NxN patches."""
+        if self.config.train_num_rays_per_batch % (patch_size**2) != 0:
+            CONSOLE.print("[bold yellow]WARNING: num_rays should be divisible by patch_size^2.")
+        if patch_scale == self.eval_pixel_sampler.patch_scale and patch_size == self.eval_pixel_sampler.patch_size:
+            return
+
+        # Change train
+        for func_queue in self.func_queues:
+            func_queue.put((_worker_change_patch_sampler, (patch_scale, patch_size), {}))
+        self.clear_data_queue()  # remove any old, invalid, batch
+
+        # Change eval
+        self.eval_pixel_sampler.patch_scale = patch_scale
+        self.eval_pixel_sampler.patch_size = patch_size
+        if patch_scale % 2 == 0 and self.eval_ray_generator.image_coords[0, 0, 0] == 0.5:
+            self.eval_ray_generator.image_coords = self.eval_ray_generator.image_coords - 0.5
+
+    def get_accumulated_lidar_points(self, remove_dynamic: bool = False):
+        """Get the lidar points for the current batch."""
+        lidars = self.train_lidar_dataset.lidars
+        point_clouds = self.train_lidar_dataset.point_clouds
+        if remove_dynamic:
+            assert "trajectories" in self.train_lidar_dataset.metadata, "No trajectories found in dataset."
+
+            point_clouds = remove_dynamic_points(
+                point_clouds,
+                lidars.lidar_to_worlds,
+                lidars.times,
+                self.train_lidar_dataset.metadata["trajectories"],
+            )
+
+        return torch.cat(
+            [transform_points(pc[:, :3], l2w) for pc, l2w in zip(point_clouds, lidars.lidar_to_worlds)], dim=0
+        )
+
+
+def _find_smallest_crop(dim: int, divider: int):
+    crop_amount = 0
+    while dim % divider:
+        crop_amount += 1
+        dim -= 1
+    return crop_amount
+
+
+def _worker_change_patch_sampler(worker, patch_scale, patch_size):
+    worker.pixel_sampler.patch_scale = patch_scale
+    worker.pixel_sampler.patch_size = patch_size
+    # Ensure ray is generated from patch center (for odd scales center of pixel == center of patch)
+    if patch_scale % 2 == 0 and worker.ray_generator.image_coords[0, 0, 0] == 0.5:
+        worker.ray_generator.image_coords -= 0.5
diff --git a/nerfstudio/data/datamanagers/base_datamanager.py b/nerfstudio/data/datamanagers/base_datamanager.py
index cff03607..2fecdf27 100644
--- a/nerfstudio/data/datamanagers/base_datamanager.py
+++ b/nerfstudio/data/datamanagers/base_datamanager.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -52,8 +53,7 @@
 from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.configs.base_config import InstantiateConfig
 from nerfstudio.configs.dataparser_configs import AnnotatedDataParserUnion
-from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
-from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
+from nerfstudio.data.dataparsers.base_dataparser import DataParserConfig, DataparserOutputs
 from nerfstudio.data.datasets.base_dataset import InputDataset
 from nerfstudio.data.pixel_samplers import PatchPixelSamplerConfig, PixelSampler, PixelSamplerConfig
 from nerfstudio.data.utils.dataloaders import CacheDataloader, FixedIndicesEvalDataloader, RandIndicesEvalDataloader
@@ -177,9 +177,9 @@ def __init__(self):
         super().__init__()
         self.train_count = 0
         self.eval_count = 0
-        if self.train_dataset and self.test_mode != "inference":
+        if self.train_dataset is not None and self.test_mode != "inference":
             self.setup_train()
-        if self.eval_dataset and self.test_mode != "inference":
+        if self.eval_dataset is not None and self.test_mode != "inference":
             self.setup_eval()
 
     def forward(self):
@@ -302,6 +302,11 @@ def get_param_groups(self) -> Dict[str, List[Parameter]]:
         """
         return {}
 
+    @abstractmethod
+    def get_num_train_data(self) -> int:
+        """Get the number of training datapoints (e.g. images)."""
+        return 0
+
 
 @dataclass
 class VanillaDataManagerConfig(DataManagerConfig):
@@ -309,7 +314,7 @@ class VanillaDataManagerConfig(DataManagerConfig):
 
     _target: Type = field(default_factory=lambda: VanillaDataManager)
     """Target class to instantiate."""
-    dataparser: AnnotatedDataParserUnion = field(default_factory=BlenderDataParserConfig)
+    dataparser: AnnotatedDataParserUnion = field(default_factory=DataParserConfig)
     """Specifies the dataparser used to unpack the data."""
     train_num_rays_per_batch: int = 1024
     """Number of rays per batch to use per training iteration."""
@@ -517,12 +522,12 @@ def setup_eval(self):
         self.eval_ray_generator = RayGenerator(self.eval_dataset.cameras.to(self.device))
         # for loading full images
         self.fixed_indices_eval_dataloader = FixedIndicesEvalDataloader(
-            input_dataset=self.eval_dataset,
+            dataset=self.eval_dataset,
             device=self.device,
             num_workers=self.world_size * 4,
         )
         self.eval_dataloader = RandIndicesEvalDataloader(
-            input_dataset=self.eval_dataset,
+            dataset=self.eval_dataset,
             device=self.device,
             num_workers=self.world_size * 4,
         )
@@ -573,4 +578,10 @@ def get_param_groups(self) -> Dict[str, List[Parameter]]:
         Returns:
             A list of dictionaries containing the data manager's param groups.
         """
-        return {}
+        param_groups = {}
+
+        return param_groups
+
+    def get_num_train_data(self) -> int:
+        """Get the number of training datapoints (e.g. images)."""
+        return len(self.train_dataset.cameras)
diff --git a/nerfstudio/data/datamanagers/full_images_datamanager.py b/nerfstudio/data/datamanagers/full_images_datamanager.py
index 47925acc..a4b923d3 100644
--- a/nerfstudio/data/datamanagers/full_images_datamanager.py
+++ b/nerfstudio/data/datamanagers/full_images_datamanager.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 The Nerfstudio Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -39,8 +40,7 @@
 from nerfstudio.cameras.cameras import Cameras, CameraType
 from nerfstudio.configs.dataparser_configs import AnnotatedDataParserUnion
 from nerfstudio.data.datamanagers.base_datamanager import DataManager, DataManagerConfig, TDataset
-from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
-from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig
+from nerfstudio.data.dataparsers.base_dataparser import DataParserConfig, DataparserOutputs
 from nerfstudio.data.datasets.base_dataset import InputDataset
 from nerfstudio.utils.misc import get_orig_class
 from nerfstudio.utils.rich_utils import CONSOLE
@@ -49,7 +49,7 @@
 @dataclass
 class FullImageDatamanagerConfig(DataManagerConfig):
     _target: Type = field(default_factory=lambda: FullImageDatamanager)
-    dataparser: AnnotatedDataParserUnion = field(default_factory=NerfstudioDataParserConfig)
+    dataparser: AnnotatedDataParserUnion = field(default_factory=DataParserConfig)
     camera_res_scale_factor: float = 1.0
     """The scale factor for scaling spatial data such as images, mask, semantics
     along with relevant information about camera intrinsics
diff --git a/nerfstudio/data/datamanagers/image_lidar_datamanager.py b/nerfstudio/data/datamanagers/image_lidar_datamanager.py
new file mode 100644
index 00000000..3d01c231
--- /dev/null
+++ b/nerfstudio/data/datamanagers/image_lidar_datamanager.py
@@ -0,0 +1,423 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Lidar Datamanager.
+"""
+
+from __future__ import annotations
+
+import concurrent.futures
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
+
+import torch
+import torch.multiprocessing as mp
+from rich.progress import Console, track
+from typing_extensions import Literal
+
+from nerfstudio.cameras.lidars import Lidars
+from nerfstudio.cameras.rays import RayBundle
+from nerfstudio.data.datamanagers.base_datamanager import TDataset
+from nerfstudio.data.datamanagers.parallel_datamanager import ParallelDataManager, ParallelDataManagerConfig
+from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
+from nerfstudio.data.datasets.base_dataset import InputDataset
+from nerfstudio.data.datasets.lidar_dataset import LidarDataset
+from nerfstudio.data.pixel_samplers import LidarPointSampler, LidarPointSamplerConfig, PixelSampler
+from nerfstudio.data.utils.dataloaders import CacheDataloader, FixedIndicesEvalDataloader, RandIndicesEvalDataloader
+from nerfstudio.data.utils.nerfstudio_collate import nerfstudio_collate
+from nerfstudio.model_components.ray_generators import LidarRayGenerator, RayGenerator
+
+CONSOLE = Console(width=120)
+
+
+def lidar_variable_res_collate(batch: List[Dict]) -> Dict:
+    """Default collate function for the cached dataloader.
+    Args:
+        batch: Batch of samples from the dataset.
+    Returns:
+        Collated batch.
+    """
+    lidars = []
+    for data in batch:
+        lidars.append(data.pop("lidar"))
+    new_batch: dict = nerfstudio_collate(batch)
+    new_batch["lidar"] = lidars
+    return new_batch
+
+
+def lidar_packed_collate(batch: List[Dict]) -> Dict:
+    """Default collate function for the cached dataloader.
+    Args:
+        batch: Batch of samples from the dataset.
+    Returns:
+        Collated batch.
+    """
+    lidars = []
+    for i, data in enumerate(batch):
+        batch[i]["points_per_lidar"] = data["lidar"].shape[0]
+        lidars.append(data.pop("lidar"))
+
+    new_batch: dict = nerfstudio_collate(batch)
+    new_batch["lidar"] = torch.cat(lidars, dim=0)
+    return new_batch
+
+
+@dataclass
+class ImageLidarDataManagerConfig(ParallelDataManagerConfig):
+    """A basic data manager"""
+
+    _target: Type = field(default_factory=lambda: ImageLidarDataManager)
+    """Target class to instantiate."""
+    num_processes: int = 8
+    """Number of processes to use for train data loading. More than 1 doesn't result in that much better performance"""
+    queue_size: int = 8
+    """Size of shared data queue containing generated ray bundles and batches."""
+    train_num_lidar_rays_per_batch: int = 1024
+    """Number of lidar rays per batch to use per training iteration."""
+    eval_num_lidar_rays_per_batch: int = 1024
+    """Number of lidar rays per batch to use per eval iteration."""
+    downsample_factor: float = 1
+    """Downsample factor for the lidar. If <1, downsample will be used."""
+    max_thread_workers: Optional[int] = 1
+
+
+class ImageLidarDataProcessor(mp.Process):  # type: ignore
+    """Parallel dataset batch processor.
+
+    This class is responsible for generating ray bundles from an input dataset
+    in parallel python processes.
+
+    Args:
+        out_queue: the output queue for storing the processed data
+        config: configuration object for the parallel data manager
+        dataparser_outputs: outputs from the dataparser
+        dataset: input dataset
+        pixel_sampler: The pixel sampler for sampling rays
+        ray_generator: The ray generator for generating rays
+    """
+
+    def __init__(
+        self,
+        out_queue: Any[mp.Queue],  # type: ignore
+        func_queue: Any[mp.Queue],  # type: ignore
+        config: ParallelDataManagerConfig,
+        dataparser_outputs: DataparserOutputs,
+        image_dataset: TDataset,
+        pixel_sampler: PixelSampler,
+        lidar_dataset: LidarDataset,
+        point_sampler: LidarPointSampler,
+        cached_images: Dict[str, torch.Tensor],
+        cached_points: Dict[str, torch.Tensor],
+    ):
+        super().__init__()
+        self.daemon = True
+        self.out_queue = out_queue
+        self.func_queue = func_queue
+        self.config = config
+        self.dataparser_outputs = dataparser_outputs
+        self.image_dataset = image_dataset
+        self.pixel_sampler = pixel_sampler
+        self.lidar_dataset = lidar_dataset
+        self.point_sampler = point_sampler
+        self.ray_generator = RayGenerator(self.image_dataset.cameras)
+        self.lidar_ray_generator = LidarRayGenerator(self.lidar_dataset.lidars)
+        self.cached_images = cached_images
+        self.cached_points = cached_points
+
+    def run(self):
+        """Append out queue in parallel with ray bundles and batches."""
+        while True:
+            while not self.func_queue.empty():
+                func, args, kwargs = self.func_queue.get()
+                func(self, *args, **kwargs)
+            ray_bundle, batch = self.get_batch_and_ray_bundle()
+            if torch.cuda.is_available():
+                ray_bundle = ray_bundle.pin_memory()
+            self.out_queue.put((ray_bundle, batch))
+
+    def get_batch_and_ray_bundle(self):
+        img_batch, img_ray_bundle = self.get_image_batch_and_ray_bundle()
+        lidar_batch, lidar_ray_bundle = self.get_lidar_batch_and_ray_bundle()
+        return _merge_img_lidar(img_ray_bundle, img_batch, lidar_ray_bundle, lidar_batch, len(self.image_dataset))
+
+    def get_image_batch_and_ray_bundle(self):
+        if not len(self.image_dataset.cameras):
+            return None, None
+        batch = self.pixel_sampler.sample(self.cached_images)
+        ray_indices = batch["indices"]
+        ray_bundle: RayBundle = self.ray_generator(ray_indices)
+        return batch, ray_bundle
+
+    def get_lidar_batch_and_ray_bundle(self):
+        if not len(self.lidar_dataset.lidars):
+            return None, None
+        batch = self.point_sampler.sample(self.cached_points)
+        ray_indices = batch.pop("indices")
+        ray_bundle: RayBundle = self.lidar_ray_generator(ray_indices, points=batch["lidar"])
+        return batch, ray_bundle
+
+
+class ImageLidarDataManager(ParallelDataManager):
+    """This extends the VanillaDataManager to support lidar data.
+
+    Args:
+        config: the DataManagerConfig used to instantiate class
+    """
+
+    config: ImageLidarDataManagerConfig
+    train_lidar_dataset: LidarDataset
+    eval_lidar_dataset: LidarDataset
+    train_point_sampler: Optional[LidarPointSampler] = None
+    eval_point_sampler: Optional[LidarPointSampler] = None
+
+    def __init__(
+        self,
+        config: ImageLidarDataManagerConfig,
+        device: Union[torch.device, str] = "cpu",
+        test_mode: Literal["test", "val", "inference"] = "val",
+        world_size: int = 1,
+        local_rank: int = 0,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        self.use_mp = config.num_processes > 0
+        super().__init__(config, device, test_mode, world_size, local_rank, **kwargs)
+        self.next_batch = None  # for prefetching to gpu in parallel
+
+    def create_train_dataset(self) -> InputDataset:
+        """Sets up the data loaders for training"""
+        self.train_lidar_dataset = LidarDataset(
+            dataparser_outputs=self.train_dataparser_outputs,
+            downsample_factor=self.config.downsample_factor,
+        )
+        img_dataset = super().create_train_dataset()
+        return img_dataset
+
+    def create_eval_dataset(self) -> InputDataset:
+        """Sets up the data loaders for evaluation"""
+        self.eval_lidar_dataset = LidarDataset(
+            dataparser_outputs=self.eval_dataparser_outputs,
+            downsample_factor=self.config.downsample_factor,
+        )
+        img_dataset = super().create_eval_dataset()
+        return img_dataset
+
+    def setup_train(self):
+        """Sets up the data loaders for training"""
+        assert self.train_dataset is not None
+        self.train_pixel_sampler = self._get_pixel_sampler(self.train_dataset, self.config.train_num_rays_per_batch)  # type: ignore
+        self.train_point_sampler = LidarPointSamplerConfig().setup(
+            num_rays_per_batch=self.config.train_num_lidar_rays_per_batch
+        )
+        self.train_lidar_ray_generator = LidarRayGenerator(
+            self.train_lidar_dataset.lidars,
+        )
+        # Cache jointly to allow memory sharing between processes
+        cached_images = _cache_images(self.train_dataset, self.config.max_thread_workers, self.config.collate_fn)
+        cached_points = _cache_points(self.train_lidar_dataset, self.config.max_thread_workers, lidar_packed_collate)
+        self.data_queue = mp.Queue(maxsize=self.config.queue_size) if self.use_mp else None
+        # Create an individual queue for passing functions to each process
+        self.func_queues = [mp.Queue() for _ in range(max(self.config.num_processes, 1))]
+        self.data_procs = [
+            ImageLidarDataProcessor(
+                out_queue=self.data_queue,
+                func_queue=func_queue,
+                config=self.config,
+                dataparser_outputs=self.train_dataparser_outputs,
+                image_dataset=self.train_dataset,
+                pixel_sampler=self.train_pixel_sampler,
+                lidar_dataset=self.train_lidar_dataset,
+                point_sampler=self.train_point_sampler,
+                cached_images=cached_images,
+                cached_points=cached_points,
+            )
+            for func_queue in self.func_queues
+        ]
+        if self.use_mp:
+            for proc in self.data_procs:
+                proc.start()
+            print("Started processes")
+
+    def setup_eval(self):
+        """Sets up the data loader for evaluation"""
+        if len(self.eval_dataset.cameras):
+            super().setup_eval()
+        else:
+            CONSOLE.print("Setting up lidar-only eval dataset...")
+            self.eval_camera_optimizer = None
+        if not len(self.eval_lidar_dataset.lidars):
+            CONSOLE.print("Not setting up lidar eval dataset...")
+            self.eval_lidar_optimizer = None
+            return
+        self.eval_lidar_dataloader = CacheDataloader(
+            self.eval_lidar_dataset,
+            num_images_to_sample_from=self.config.eval_num_images_to_sample_from,
+            num_times_to_repeat_images=self.config.eval_num_times_to_repeat_images,
+            device=self.device,
+            num_workers=self.world_size * 4,
+            pin_memory=True,
+            collate_fn=lidar_packed_collate,
+        )
+        self.iter_eval_lidar_dataloader = iter(self.eval_lidar_dataloader)
+        self.eval_point_sampler = LidarPointSamplerConfig().setup(
+            num_rays_per_batch=self.config.eval_num_lidar_rays_per_batch
+        )
+        self.eval_lidar_ray_generator = LidarRayGenerator(
+            self.eval_lidar_dataset.lidars.to(self.device),
+        )
+        # for loading full images
+        self.fixed_indices_eval_lidar_dataloader = FixedIndicesEvalDataloader(
+            dataset=self.eval_lidar_dataset,
+            device=self.device,
+            num_workers=self.world_size * 4,
+        )
+        self.rand_indices_eval_lidar_dataloader = RandIndicesEvalDataloader(
+            dataset=self.eval_lidar_dataset,
+            device=self.device,
+            num_workers=self.world_size * 4,
+        )
+
+    def next_train(self, step: int) -> Tuple[RayBundle, Dict]:
+        """Returns the next batch of data from the train dataloader."""
+        self.train_count += 1
+        ray_bundle, batch = self.next_batch if self.next_batch is not None else self._get_from_queue()
+        if self.use_mp:
+            self.next_batch = self._get_from_queue()  # prefetch next batch
+        return ray_bundle, batch
+
+    def _get_from_queue(self) -> Tuple[RayBundle, Dict]:
+        if self.use_mp:
+            ray_bundle, batch = self.data_queue.get()
+        else:
+            # Manually call the data processing function (not in parallel)
+            ray_bundle, batch = self.data_procs[0].get_batch_and_ray_bundle()
+        ray_bundle = ray_bundle.to(self.device, non_blocking=self.use_mp)
+        batch = {k: v.to(self.device, non_blocking=self.use_mp) for k, v in batch.items()}
+        return ray_bundle, batch
+
+    def next_eval(self, step: int) -> Tuple[RayBundle, Dict]:
+        """Returns the next batch of data from the eval dataloader."""
+        if len(self.eval_dataset.cameras):
+            img_ray_bundle, img_batch = super().next_eval(step)
+        else:
+            self.eval_count += 1
+            img_ray_bundle, img_batch = None, None
+        if len(self.eval_lidar_dataset.lidars):
+            lidar_batch = next(self.iter_eval_lidar_dataloader)
+            lidar_batch = self.eval_point_sampler.sample(lidar_batch)
+            lidar_ray_bundle = self.eval_lidar_ray_generator(lidar_batch.pop("indices"), points=lidar_batch["lidar"])
+        else:
+            lidar_ray_bundle, lidar_batch = None, None
+        return _merge_img_lidar(
+            img_ray_bundle, img_batch, lidar_ray_bundle, lidar_batch, len(self.eval_dataset.cameras)
+        )
+
+    def get_num_train_data(self) -> int:
+        """Get the number of training datapoints (images + lidar scans)."""
+        return len(self.train_dataset.cameras) + len(self.train_lidar_dataset.lidars)
+
+    def next_eval_lidar(self, step: int) -> Tuple[Lidars, Dict]:
+        for lidar, batch in self.rand_indices_eval_lidar_dataloader:
+            assert lidar.shape[0] == 1
+            assert isinstance(lidar, Lidars)
+            return lidar, batch
+        raise ValueError("No more eval images")
+
+    def clear_data_queue(self):
+        """Clear everything in the queue + any potential batch in progress."""
+        self.next_batch = None
+        if self.data_queue is None:
+            return
+        for _ in range(self.config.queue_size + 2 * self.config.num_processes):
+            self.data_queue.get()
+
+    def __del__(self):
+        """Close the data queue when the object is deleted."""
+        if self.use_mp:
+            super().__del__()
+
+
+def _cache_images(image_dataset, workers, collate_fn):
+    """Caches all input images into a NxHxWx3 tensor."""
+    indices = range(len(image_dataset))
+    batch_list = []
+    results = []
+    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:
+        for idx in indices:
+            res = executor.submit(image_dataset.__getitem__, idx)
+            results.append(res)
+        for res in track(results, description="Loading data batch", transient=False):
+            batch_list.append(res.result())
+    return collate_fn(batch_list)
+
+
+def _cache_points(lidar_dataset, workers, collate_fn):
+    """Caches all input images into a NxHxWx3 tensor."""
+    indices = range(len(lidar_dataset))
+    batch_list = []
+    results = []
+    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:
+        for idx in indices:
+            res = executor.submit(lidar_dataset.__getitem__, idx)
+            results.append(res)
+        for res in track(results, description="Loading data batch", transient=False):
+            batch_list.append(res.result())
+    return collate_fn(batch_list)
+
+
+def _merge_img_lidar(
+    img_ray_bundle: Optional[RayBundle],
+    img_batch: Optional[Dict],
+    lidar_ray_bundle: Optional[RayBundle],
+    lidar_batch: Optional[Dict],
+    img_dataset_len: int,
+) -> Tuple[RayBundle, Dict]:
+    """Helper function for merging img and lidar data."""
+    if img_ray_bundle is None and lidar_ray_bundle is None:
+        raise ValueError("Need either img or lidar data (or both)")
+
+    # process image
+    if img_ray_bundle is not None:
+        assert img_batch is not None
+        device = img_ray_bundle.origins.device
+        img_batch["is_lidar"] = torch.zeros((len(img_ray_bundle), 1), dtype=torch.bool, device=device)
+        img_batch["did_return"] = torch.ones((len(img_ray_bundle), 1), dtype=torch.bool, device=device)
+        img_ray_bundle.metadata["is_lidar"] = img_batch["is_lidar"]
+        img_batch["img_indices"] = img_batch.pop("indices")
+        img_ray_bundle.metadata["did_return"] = torch.ones((len(img_ray_bundle), 1), dtype=torch.bool, device=device)
+
+    # process lidar
+    if lidar_ray_bundle is not None:
+        assert lidar_batch is not None
+        lidar_ray_bundle.camera_indices = lidar_ray_bundle.camera_indices + img_dataset_len
+        lidar_batch["is_lidar"] = lidar_ray_bundle.metadata["is_lidar"]
+        lidar_batch["distance"] = lidar_ray_bundle.metadata["directions_norm"]
+        lidar_batch["did_return"] = lidar_ray_bundle.metadata["did_return"]
+
+    # merge
+    if img_ray_bundle is None or img_batch is None:
+        ray_bundle, batch = lidar_ray_bundle, lidar_batch
+    elif lidar_ray_bundle is None or lidar_batch is None:
+        ray_bundle, batch = img_ray_bundle, img_batch
+    else:
+        ray_bundle = img_ray_bundle.cat(lidar_ray_bundle, dim=0)
+        overlapping_keys = set(img_batch.keys()).intersection(set(lidar_batch.keys())) - {"is_lidar", "did_return"}
+        assert not overlapping_keys, f"Overlapping keys in batch: {overlapping_keys}"
+        batch = {
+            **img_batch,
+            **lidar_batch,
+            "is_lidar": torch.cat([img_batch["is_lidar"], lidar_batch["is_lidar"]]),
+            "did_return": torch.cat([img_batch["did_return"], lidar_batch["did_return"]]),
+        }
+    return ray_bundle, batch
diff --git a/nerfstudio/data/datamanagers/parallel_datamanager.py b/nerfstudio/data/datamanagers/parallel_datamanager.py
index e7521bec..9a3f6f60 100644
--- a/nerfstudio/data/datamanagers/parallel_datamanager.py
+++ b/nerfstudio/data/datamanagers/parallel_datamanager.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -212,7 +213,7 @@ def create_train_dataset(self) -> TDataset:
     def create_eval_dataset(self) -> TDataset:
         """Sets up the data loaders for evaluation."""
         return self.dataset_type(
-            dataparser_outputs=self.dataparser.get_dataparser_outputs(split=self.test_split),
+            dataparser_outputs=self.eval_dataparser_outputs,
             scale_factor=self.config.camera_res_scale_factor,
         )
 
@@ -274,12 +275,12 @@ def setup_eval(self):
         self.eval_ray_generator = RayGenerator(self.eval_dataset.cameras.to(self.device))
         # for loading full images
         self.fixed_indices_eval_dataloader = FixedIndicesEvalDataloader(
-            input_dataset=self.eval_dataset,
+            dataset=self.eval_dataset,
             device=self.device,
             num_workers=self.world_size * 4,
         )
         self.eval_dataloader = RandIndicesEvalDataloader(
-            input_dataset=self.eval_dataset,
+            dataset=self.eval_dataset,
             device=self.device,
             num_workers=self.world_size * 4,
         )
@@ -332,6 +333,10 @@ def get_param_groups(self) -> Dict[str, List[Parameter]]:
         """
         return {}
 
+    def get_num_train_data(self) -> int:
+        """Get the number of training datapoints (e.g. images)."""
+        return len(self.train_dataset.cameras)
+
     def __del__(self):
         """Clean up the parallel data processes."""
         if hasattr(self, "data_procs"):
diff --git a/nerfstudio/data/datamanagers/random_cameras_datamanager.py b/nerfstudio/data/datamanagers/random_cameras_datamanager.py
index 6c638ec0..f4bc232d 100644
--- a/nerfstudio/data/datamanagers/random_cameras_datamanager.py
+++ b/nerfstudio/data/datamanagers/random_cameras_datamanager.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 The Nerfstudio Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -235,7 +236,7 @@ def __init__(
         self.eval_dataset = TrivialDataset(cameras)
 
         self.eval_dataloader = RandIndicesEvalDataloader(
-            input_dataset=self.eval_dataset,
+            dataset=self.eval_dataset,
             device=self.device,
             num_workers=self.world_size * 4,
         )
diff --git a/nerfstudio/data/dataparsers/ad_dataparser.py b/nerfstudio/data/dataparsers/ad_dataparser.py
new file mode 100644
index 00000000..94bce83f
--- /dev/null
+++ b/nerfstudio/data/dataparsers/ad_dataparser.py
@@ -0,0 +1,699 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Base data parser for Autonomous Driving datasets."""
+
+import math
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+from typing import Dict, List, Literal, Optional, Tuple, Type, TypeVar, Union
+
+import numpy as np
+import torch
+from PIL import Image
+from torch import Tensor
+
+from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.cameras.lidars import Lidars, transform_points
+from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
+from nerfstudio.data.scene_box import SceneBox
+from nerfstudio.utils import poses as pose_utils
+from nerfstudio.utils.poses import interpolate_trajectories, inverse, multiply as pose_multiply, to4x4
+
+SensorData = TypeVar("SensorData", Cameras, Lidars)
+
+
+class SplitTypes(Enum):
+    """Split types for train/eval."""
+
+    LINSPACE = "linspace"
+    NUSCENES_SAMPLES = "nuscenes_samples"
+
+
+OPENCV_TO_NERFSTUDIO = np.array(
+    [
+        [1, 0, 0],
+        [0, -1, 0],
+        [0, 0, -1],
+    ]
+)
+DUMMY_DISTANCE_VALUE = 2e3  # meters, used for missing points
+
+
+@dataclass
+class ADDataParserConfig(DataParserConfig):
+    """AD (autonomous driving) dataset config."""
+
+    _target: Type = field(default_factory=lambda: ADDataParser)
+    """target class to instantiate"""
+    sequence: str = "001"
+    """Name of the sequence to load."""
+    train_split_fraction: float = 0.5
+    """The percent of images to use for training. The remaining images are for eval."""
+    train_eval_split_type: SplitTypes = SplitTypes.LINSPACE
+    """The type of split to use for train/eval."""
+    max_eval_frames: Optional[int] = None
+    """The maximum number of frames to use for eval. If None, use all eval frames."""
+    dataset_start_fraction: float = 0.0
+    """At what fraction of the dataset to start."""
+    dataset_end_fraction: float = 1.0
+    """At what fraction of the dataset to end."""
+    scene_box_height = (-10, 30)
+    """The upper and lower height bounds for the scene box (m)."""
+
+    cameras: Tuple[str, ...] = tuple()
+    """Which cameras to use."""
+    lidars: Tuple[str, ...] = tuple()
+    """Which lidars to use."""
+    min_lidar_dist: Tuple[float, float, float] = (1.0, 2.0, 2.0)
+    """Remove all points within this distance from lidar sensor."""
+    radars: Tuple[str, ...] = tuple()
+    """Which radars to use."""
+
+    load_cuboids: bool = True
+    """Whether to load cuboid annotations."""
+    include_deformable_actors: bool = False
+    """Whether to include deformable actors in the loaded trajectories (like pedestrians)."""
+    annotation_interval: float = 0.0
+    """The time interval at which the sequence is annotated (s)."""
+    rolling_shutter_offsets: Tuple[float, float] = (0.0, 0.0)
+    """The time offset for the first and last line, relative to the image timestamp (seconds)."""
+    allow_per_point_times: bool = True
+    """Whether to allow per-point times (for sub-frame time correction)."""
+
+    add_missing_points: bool = False
+    """Whether to add missing points (rays that did not return) to the point clouds."""
+    lidar_elevation_mapping: Optional[Dict[str, Dict[int, float]]] = None
+    """Elevation mapping for each lidar."""
+    skip_elevation_channels: Optional[Dict[str, Tuple[int, ...]]] = None
+    """Channels to skip when adding missing points."""
+    lidar_azimuth_resolution: Optional[Dict[str, float]] = None
+    """Azimuth resolution for each lidar."""
+    paint_points: bool = False
+    """Whether to paint the points in the point cloud."""
+
+    def __post_init__(self):
+        if type(self) == ADDataParserConfig:
+            return  # only make the following checks for child classes
+        # Hack to allow settings empty tuple on command line
+        if len(self.cameras) == 1 and self.cameras[0].lower() == "none":
+            self.cameras = ()
+        if len(self.lidars) == 1 and self.lidars[0].lower() == "none":
+            self.lidars = ()
+        if len(self.radars) == 1 and self.radars[0].lower() == "none":
+            self.radars = ()
+        assert self.cameras or self.lidars or self.radars, "Must specify at least one sensor to load"
+        assert self.annotation_interval > 1e-6, "Child classes must specify the annotation interval"
+        assert self.dataset_start_fraction >= 0.0, "Dataset start fraction must be >= 0.0"
+        assert self.dataset_end_fraction <= 1.0, "Dataset end fraction must be <= 1.0"
+        assert self.dataset_start_fraction < self.dataset_end_fraction, "Dataset start must be < dataset end"
+
+
+@dataclass
+class ADDataParser(DataParser):
+    """PandaSet DatasetParser"""
+
+    config: ADDataParserConfig
+    includes_time: bool = True
+
+    @property
+    def actor_transform(self) -> Tensor:
+        """The transform to convert from our actor frame (x-right, y-forward, z-up) to the original actor frame."""
+        return torch.eye(4)[:3, :]
+
+    def _get_cameras(self) -> Tuple[Cameras, List[Path]]:
+        """Returns camera info and image filenames."""
+        raise NotImplementedError()
+
+    def _get_lidars(self) -> Tuple[Lidars, List[Path]]:
+        """Returns lidar info and filenames."""
+        raise NotImplementedError()
+
+    def _read_lidars(self, lidars: Lidars, filenames: List[Path]) -> List[Tensor]:
+        """Reads the point clouds from the given filenames. Should be in x,y,z,r,t order. t is optional."""
+        raise NotImplementedError()
+
+    def _get_actor_trajectories(self) -> List[Dict]:
+        """Returns a list of actor trajectories.
+
+        Each trajectory is a dictionary with the following keys:
+            - poses: the poses of the actor (float32)
+            - timestamps: the timestamps of the actor (float64)
+            - dims: the dimensions of the actor, wlh order (float32)
+            - label: the label of the actor (str)
+            - stationary: whether the actor is stationary (bool)
+            - symmetric: whether the actor is expected to be symmetric (bool)
+            - deformable: whether the actor is expected to be deformable (e.g. pedestrian)
+        """
+        raise NotImplementedError()
+
+    def _get_radars(self):
+        """Returns a list of radars."""
+        raise NotImplementedError()
+
+    def _get_lane_shift_sign(self, sequence: str) -> Literal[-1, 1]:
+        """Get the sign of the most realistic lane shift for the given sequence.
+
+        This is to avoid shifting into unreasonable locations, that do not correspond
+        to a realistic lane shift (like shifting into a parked car, wall, etc.)
+        """
+        return 1
+
+    def _generate_dataparser_outputs(self, split="train"):
+        # Load data (this is implemented in the dataset-specific subclass)
+        cameras, img_filenames = self._get_cameras() if self.config.cameras else (_empty_cameras(), [])
+        lidars, pc_filenames = self._get_lidars() if self.config.lidars else (_empty_lidars(), [])
+        radars = self._get_radars() if self.config.radars else None
+        assert radars is None, "Radars not supported yet"
+        trajectories = self._get_actor_trajectories() if self.config.load_cuboids else []
+        # use dataset fraction to filter data
+        cameras, img_filenames, lidars, pc_filenames, trajectories = self._filter_based_on_time(
+            cameras, img_filenames, lidars, pc_filenames, trajectories
+        )
+        # read all the point clouds
+        point_clouds = self._read_lidars(lidars, pc_filenames)
+
+        # Process the data
+        self._remove_ego_points(point_clouds)
+        time_offset = self._adjust_times(cameras, lidars, point_clouds, trajectories)
+        dataparser_transform = self._adjust_poses(cameras, lidars, trajectories)
+        scene_box = self._compute_scene_box(cameras, lidars)
+        self._add_sensor_velocities(cameras, lidars)
+
+        # Adjust sensor idxs to avoid overlap (if necessary)
+        cam_sensor_idxs = cameras.metadata["sensor_idxs"].unique()
+        if set(lidars.metadata["sensor_idxs"].unique().tolist()).intersection(set(cam_sensor_idxs.tolist())):
+            assert cam_sensor_idxs.max() == cam_sensor_idxs.numel() - 1, "Sensor idxs must be contiguous"
+            lidars.metadata["sensor_idxs"] += cam_sensor_idxs.numel()
+        sensor_idx_to_name = {idx: name for idx, name in enumerate(self.config.cameras + self.config.lidars)}
+
+        # Apply splits
+        split = 0 if split == "train" else 1
+        cam_idxs = self._get_train_eval_indices(cameras)[split]
+        cameras, img_filenames = cameras[cam_idxs], [img_filenames[i] for i in cam_idxs]
+        lidar_idxs = self._get_train_eval_indices(lidars)[split]
+        lidars, point_clouds = lidars[lidar_idxs], [point_clouds[i] for i in lidar_idxs]
+
+        # sensor_times = torch.cat([cameras.times, lidars.times], dim=0).squeeze(-1).unique()
+        sensor_times = lidars.times.squeeze(-1).unique()
+        trajectories = self._interpolate_trajectories(trajectories, sensor_times, self.config.annotation_interval)
+        # remove empty trajectories
+        trajectories = [traj for traj in trajectories if len(traj["timestamps"]) > 1]
+
+        point_clouds_rgb = (
+            None if not self.config.paint_points else _paint_points(lidars, point_clouds, cameras, img_filenames)
+        )
+
+        # Massage into output format
+        return DataparserOutputs(
+            image_filenames=img_filenames,
+            cameras=cameras,
+            scene_box=scene_box,
+            mask_filenames=None,  # TODO: handle masks
+            dataparser_scale=1.0,  # no scaling
+            dataparser_transform=dataparser_transform,
+            actor_transform=self.actor_transform,
+            time_offset=time_offset,
+            metadata={
+                "lidars": lidars,
+                "point_clouds": [pc.float() for pc in point_clouds],  # Ensure they are float32
+                "point_clouds_rgb": point_clouds_rgb,
+                "trajectories": trajectories,
+                "lane_shift_sign": self._get_lane_shift_sign(self.config.sequence),
+                "sensor_idx_to_name": sensor_idx_to_name,
+                "duration": max(cameras.times.max(), lidars.times.max()) - min(cameras.times.min(), lidars.times.min()),
+            },
+        )
+
+    def _compute_scene_box(self, cameras: Cameras, lidars: Lidars) -> SceneBox:
+        if len(lidars):
+            # Pad 60 meters left/right and 80 meters forward/backward
+            og_poses = lidars.lidar_to_worlds
+            padded_poses = _get_padded_poses(og_poses, 60.0, 80.0)
+        else:
+            # Need to figure out padding here, since cameras can point in all directions
+            raise NotImplementedError("Scene box computation using cameras not implemented yet")
+            og_poses = cameras.camera_to_worlds
+            padded_poses = _get_padded_poses(og_poses, 80.0, 80.0)
+        poses = torch.concatenate([padded_poses, og_poses[:, :3, 3]])
+        aabb_scale = poses.abs().max(dim=0)[0]
+        aabb = torch.stack([-aabb_scale, aabb_scale], dim=0)
+        aabb[1, 2] = self.config.scene_box_height[1]
+        aabb[0, 2] = self.config.scene_box_height[0]
+        return SceneBox(aabb=aabb)
+
+    def _remove_ego_points(self, point_clouds: List[Tensor]):
+        dist_thresh = torch.tensor(self.config.min_lidar_dist)
+        for i, pc in enumerate(point_clouds):
+            mask = (pc[:, :3].abs() >= dist_thresh).any(-1)
+            point_clouds[i] = pc[mask]
+
+    def _filter_based_on_time(
+        self,
+        cameras: Cameras,
+        img_filenames: List[Path],
+        lidars: Lidars,
+        pc_filenames: List[Path],
+        trajectories: List[Dict],
+    ) -> Tuple[Cameras, List[Path], Lidars, List[Path], List[Dict]]:
+        # Remove the data that is outside the dataset start/end fraction
+        if self.config.dataset_start_fraction == 0.0 and self.config.dataset_end_fraction == 1.0:
+            return cameras, img_filenames, lidars, pc_filenames, trajectories
+
+        times = torch.cat([cameras.times, lidars.times], dim=0)
+        end_time = times.max().item()
+        start_time = times.min().item()
+        duration = end_time - start_time
+        start_time += duration * self.config.dataset_start_fraction
+        end_time = start_time + duration * self.config.dataset_end_fraction
+
+        cameras, img_filenames = _filter_sensordata_on_time(cameras, img_filenames, start_time, end_time)
+        lidars, pc_filenames = _filter_sensordata_on_time(lidars, pc_filenames, start_time, end_time)
+        assert len(cameras) or len(lidars), "No cameras or lidars in the dataset"
+        # filter the trajectories that are not in the sequence at all
+        trajectories = [
+            traj
+            for traj in trajectories
+            if (traj["timestamps"] >= start_time).any() and (traj["timestamps"] <= end_time).any()
+        ]
+
+        return cameras, img_filenames, lidars, pc_filenames, trajectories
+
+    def _adjust_times(
+        self, cameras: Cameras, lidars: Lidars, point_clouds: List[Tensor], trajectories: List[Dict]
+    ) -> float:
+        times = torch.cat([cameras.times, lidars.times], dim=0)
+        min_time = times.min().item()
+        # Set the times for the cameras and lidars to be relative to the min time
+        cameras.times = (cameras.times - min_time).float()
+        lidars.times = (lidars.times - min_time).float()
+        for traj in trajectories:
+            traj["timestamps"] = (traj["timestamps"] - min_time).float()
+        for pc in point_clouds:
+            # Sometimes we have only x,y,z,r sometimes x,y,z,r,t
+            if pc.shape[-1] > 4 and not self.config.allow_per_point_times:
+                pc[..., 4] = 0.0
+        return min_time
+
+    def _adjust_poses(self, cameras: Cameras, lidars: Lidars, trajectories: List[Dict]):
+        """Determines a new, centered, world coordinate system, and adjusts all poses."""
+        w2m = _get_world_to_mean_transform(cameras, lidars)
+        cameras.camera_to_worlds = pose_multiply(w2m, cameras.camera_to_worlds)
+        lidars.lidar_to_worlds = pose_multiply(w2m, lidars.lidar_to_worlds)
+        for traj in trajectories:
+            traj["poses"][:, :3] = pose_multiply(w2m, traj["poses"][:, :3])
+        return w2m
+
+    def _get_train_eval_indices(self, sensors: Union[Cameras, Lidars]) -> Tuple[Tensor, Tensor]:
+        if self.config.train_eval_split_type == SplitTypes.LINSPACE:
+            return self._get_linspaced_indices(sensors.metadata["sensor_idxs"].squeeze(-1))
+        raise NotImplementedError(
+            f"Split type {self.config.train_eval_split_type} not implemented for this dataparser, override _get_train_eval_indices."
+        )
+
+    def _get_linspaced_indices(self, sensor_idxs: Tensor) -> Tuple[Tensor, Tensor]:
+        """Returns indices of a linspaced subset of length `length` with ratio `ratio` with contiguous chunks."""
+        if sensor_idxs.numel() == 0:
+            return torch.empty(0, dtype=torch.int64), torch.empty(0, dtype=torch.int64)
+        elif self.config.train_split_fraction == 1.0:
+            # if we are using the full dataset, just return all indices
+            # we will use this for both trian and val, as we do not mean to use the validation performance for anything
+            # only for visual inspection
+            train_indices = torch.arange(sensor_idxs.numel(), dtype=torch.int64)
+            eval_indices = train_indices.clone()
+        else:
+            train_indices = []
+            for sensor_idx in sensor_idxs.unique():
+                sensor_sample_idxs = (sensor_idxs == sensor_idx).nonzero().squeeze(-1)
+                # split according to train_split_fraction
+                num_for_train_split = math.ceil(len(sensor_sample_idxs) * self.config.train_split_fraction)
+                sensor_train_split = np.linspace(0, len(sensor_sample_idxs) - 1, num_for_train_split, dtype=np.int64)
+                train_indices += sensor_sample_idxs[sensor_train_split]
+            eval_indices = np.setdiff1d(np.arange(len(sensor_idxs)), train_indices)
+            train_indices, eval_indices = torch.tensor(train_indices), torch.from_numpy(eval_indices)
+
+        if self.config.max_eval_frames is not None:
+            torch.manual_seed(123)
+            eval_indices = eval_indices[torch.randperm(len(eval_indices))[: self.config.max_eval_frames]]
+        return train_indices, eval_indices
+
+    def _add_sensor_velocities(self, cameras: Cameras, lidars: Lidars):
+        """Adds the sensor velocities to the metadata."""
+        assert cameras.metadata is not None and lidars.metadata is not None, "Must have metadata"
+        cameras.metadata["velocities"] = torch.zeros_like(cameras.camera_to_worlds[:, :3, 3])
+        for sensor_idx in cameras.metadata["sensor_idxs"].unique():
+            mask = (cameras.metadata["sensor_idxs"] == sensor_idx).squeeze(-1)
+            cam2worlds, times = cameras.camera_to_worlds[mask], cameras.times[mask]
+            velo = (cam2worlds[1:, :3, 3] - cam2worlds[:-1, :3, 3]) / (times[1:] - times[:-1])
+            cameras.metadata["velocities"][mask] = torch.cat((velo, velo[-1:]), 0)
+        cameras.metadata["rolling_shutter_offsets"] = (
+            torch.tensor(self.config.rolling_shutter_offsets).unsqueeze(0).repeat(len(cameras), 1)
+        )
+
+        lidars.metadata["velocities"] = torch.zeros_like(lidars.lidar_to_worlds[:, :3, 3])
+        for sensor_idx in lidars.metadata["sensor_idxs"].unique():
+            mask = (lidars.metadata["sensor_idxs"] == sensor_idx).squeeze(-1)
+            lidar2worlds, times = lidars.lidar_to_worlds[mask], lidars.times[mask]
+            velo = (lidar2worlds[1:, :3, 3] - lidar2worlds[:-1, :3, 3]) / (times[1:] - times[:-1])
+            lidars.metadata["velocities"][mask] = torch.cat((velo, velo[-1:]), 0)
+
+    def _interpolate_trajectories(self, trajectories: List[Dict], timestamps: Tensor, extrapolation_length: float):
+        # sort query times, just to be sure
+        timestamps = timestamps.sort().values
+        eps = 1e-3  # Because of floating point errors
+        for traj in trajectories:
+            dist = extrapolation_length - eps
+            extrap_back = (timestamps > (traj["timestamps"][0] - dist)) & (timestamps < traj["timestamps"][0])
+            if extrap_back.any():
+                # Duplicate the first pose to pad the trajectory
+                new_time = timestamps[extrap_back].min()
+                new_pose = traj["poses"][0].clone()  # TODO: could extrapolate pose instead of duplicating
+                traj["poses"] = torch.cat([new_pose.unsqueeze(0), traj["poses"]], dim=0)
+                traj["timestamps"] = torch.cat([new_time.unsqueeze(0), traj["timestamps"]], dim=0)
+
+            extrap_forward = (timestamps < (traj["timestamps"][-1] + dist)) & (timestamps > traj["timestamps"][-1])
+            if extrap_forward.any():
+                # Duplicate the last pose to pad the trajectory
+                new_time = timestamps[extrap_forward].max()
+                new_pose = traj["poses"][-1].clone()
+                traj["poses"] = torch.cat([traj["poses"], new_pose.unsqueeze(0)], dim=0)
+                traj["timestamps"] = torch.cat([traj["timestamps"], new_time.unsqueeze(0)], dim=0)
+
+            assert torch.all(traj["timestamps"][1:] >= traj["timestamps"][:-1]), "Trajectory timestamps must be sorted"
+            # Only query for times between min and max of trajectory
+            query_times = timestamps[timestamps >= traj["timestamps"][0] - eps]
+            query_times = query_times[query_times <= traj["timestamps"][-1] + eps]
+            # interpolate_poses expects num_poses x num_trajectories x 4 x 4, so we unsqueeze to get a single trajectory
+            new_poses, _, _ = interpolate_trajectories(traj["poses"].unsqueeze(1), traj["timestamps"], query_times)
+            traj["poses"] = to4x4(new_poses)
+            traj["timestamps"] = query_times
+        return trajectories
+
+    @staticmethod
+    def _remove_ego_motion_compensation(
+        point_cloud: torch.Tensor, l2ws: torch.Tensor, times: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Removes ego motion compensation from point cloud.
+
+        Args:
+            point_cloud: Point cloud to remove ego motion compensation from (in world frame). Shape: [num_points, 5+N] x,y,z,intensity,timestamp,(channel_id)
+            l2ws: Poses of the lidar. Shape: [num_poses, 4, 4]
+            times: Timestamps of the lidar poses. Shape: [num_poses]
+
+        Returns:
+            Point cloud without ego motion compensation in sensor frame. Shape: [num_points, 5+N] x,y,z,intensity,timestamp,(channel_id)
+            Lidar pose for each point in the point cloud. Shape: [num_points, 4, 4]
+        """
+
+        interpolated_l2ws, _, _ = interpolate_trajectories(
+            l2ws.unsqueeze(1), times - times.min(), point_cloud[:, 4] - times.min(), clamp_frac=False
+        )
+        interpolated_l2ws = interpolated_l2ws[:, :3, :4]
+        interpolated_w2ls = pose_utils.inverse(interpolated_l2ws)
+        homogen_points = torch.cat([point_cloud[:, :3], torch.ones_like(point_cloud[:, -1:])], dim=-1)
+        points = torch.matmul(interpolated_w2ls, homogen_points.unsqueeze(-1))[:, :, 0]
+        return torch.cat([points, point_cloud[:, 3:]], dim=-1), interpolated_l2ws
+
+    def _get_missing_points(
+        self,
+        point_cloud: torch.Tensor,
+        l2ws: torch.Tensor,
+        lidar_name: str,
+        dist_cutoff: float = 1.0,
+        ignore_regions: list = [],
+        outlier_thresh: float = 0.2,
+    ) -> torch.Tensor:
+        """Finds missing points in the point cloud according to sensor spec (self.config.lidar_elevation_mapping)
+
+        Args:
+            point_cloud: Point cloud to find missing points in (in sensor frame). Shape: [num_points, 4+x] x,y,z,channel_id(timestamp, intensity, etc.)
+            l2ws: Poses of the lidar. Shape: [num_points, 4, 4]
+            lidar_name: Name of the lidar
+            dist_cutoff: Distance cutoff for points to consider. Points closer than this will be ignored.
+            ignore_regions: List of regions to ignore. Each region is a list of [min_azimuth, max_azimuth, min_elevation, max_elevation]
+            outlier_thresh: Threshold for outlier elevation values. If the median elevation of the missing points is more than this value away from the median elevation of the points in the channel, we ignore the missing points.
+
+        Returns:
+            Missing points in the point cloud, in world_frame. Shape: [num_points, 3+x] x,y,z,(timestamp, intensity, etc.)
+        """
+        dist = torch.norm(point_cloud[:, :3], dim=-1)
+        dist_mask = dist > dist_cutoff
+        dist = dist[dist_mask]
+        point_cloud = point_cloud[dist_mask]
+        l2ws = l2ws[dist_mask]
+        elevation = torch.arcsin(point_cloud[:, 2] / dist)
+        elevation = torch.rad2deg(elevation)
+        azimuth = torch.atan2(point_cloud[:, 1], point_cloud[:, 0])
+        azimuth = torch.rad2deg(azimuth)
+
+        # find missing points
+        missing_points = []
+        missing_points_sensor_frame = []
+        assert self.config.lidar_elevation_mapping is not None, "Must specify lidar elevation mapping"
+        assert self.config.lidar_azimuth_resolution is not None, "Must specify lidar azimuth resolution"
+        assert self.config.skip_elevation_channels is not None, "Must specify skip elevation channels"
+        for channel_id, expected_elevation in self.config.lidar_elevation_mapping[lidar_name].items():
+            if channel_id in self.config.skip_elevation_channels[lidar_name]:
+                continue
+            channel_mask = (point_cloud[:, 3] - channel_id).abs() < 0.1  # handle floats
+            curr_azimuth = azimuth[channel_mask]
+            curr_l2ws = l2ws[channel_mask]
+            curr_elev = elevation[channel_mask]
+            if not channel_mask.any():
+                continue
+            curr_azimuth, sort_idx = curr_azimuth.sort()
+            curr_l2ws = curr_l2ws[sort_idx]
+            curr_elev = curr_elev[sort_idx]
+
+            # find missing azimuths, we should have 360 / lidar_azimuth_resolution azimuths
+            num_expected_azimuths = int(360 / self.config.lidar_azimuth_resolution[lidar_name]) + 1
+            expected_idx = torch.arange(num_expected_azimuths, device=curr_azimuth.device)
+            # find offset
+            offset = curr_azimuth[0] % self.config.lidar_azimuth_resolution[lidar_name]
+            current_idx = (
+                ((curr_azimuth - offset + 180) / self.config.lidar_azimuth_resolution[lidar_name]).round().int()
+            )
+            missing_idx = expected_idx[torch.isin(expected_idx, current_idx, invert=True)]
+            # interpolate missing azimuths
+            missing_azimuth = (
+                torch.from_numpy(
+                    np.interp(
+                        missing_idx,
+                        torch.cat([torch.tensor(-1).view(1), current_idx, torch.tensor(num_expected_azimuths).view(1)]),
+                        torch.cat(
+                            [
+                                (-180 + offset - self.config.lidar_azimuth_resolution[lidar_name]).view(1),
+                                curr_azimuth,
+                                (180 + offset + self.config.lidar_azimuth_resolution[lidar_name]).view(1),
+                            ]
+                        ),
+                    )
+                )
+                .float()
+                .view(-1, 1)
+            )
+            missing_elevation = (
+                torch.from_numpy(
+                    np.interp(
+                        missing_idx,
+                        torch.cat([torch.tensor(-1).view(1), current_idx, torch.tensor(num_expected_azimuths).view(1)]),
+                        torch.cat(
+                            [
+                                torch.tensor(expected_elevation).view(1),
+                                curr_elev.view(-1),
+                                torch.tensor(expected_elevation).view(1),
+                            ]
+                        ),
+                    )
+                )
+                .float()
+                .view(-1, 1)
+            )
+            elevation_outlier_mask = (missing_elevation - curr_elev.median()).abs() > outlier_thresh
+            missing_elevation[elevation_outlier_mask] = curr_elev.median()
+
+            ignore_mask = torch.zeros_like(missing_azimuth.squeeze(-1)).bool()
+            for ignore_region in ignore_regions:
+                ignore_mask = (
+                    (missing_azimuth > ignore_region[0])
+                    & (missing_azimuth < ignore_region[1])
+                    & (missing_elevation > ignore_region[2])
+                    & (missing_elevation < ignore_region[3])
+                ).squeeze(-1) | ignore_mask
+            missing_azimuth = missing_azimuth[~ignore_mask]
+            missing_elevation = missing_elevation[~ignore_mask]
+
+            # missing_elevation = torch.ones_like(missing_azimuth) * current_elevation
+            missing_distance = torch.ones_like(missing_azimuth) * DUMMY_DISTANCE_VALUE
+            x = (
+                torch.cos(torch.deg2rad(missing_azimuth))
+                * torch.cos(torch.deg2rad(missing_elevation))
+                * missing_distance
+            )
+            y = (
+                torch.sin(torch.deg2rad(missing_azimuth))
+                * torch.cos(torch.deg2rad(missing_elevation))
+                * missing_distance
+            )
+            z = torch.sin(torch.deg2rad(missing_elevation)) * missing_distance
+            points = torch.cat([x, y, z], dim=-1)
+            missing_points_sensor_frame.append(points)
+            # transform points from sensor space to world space
+            points = torch.cat([points, torch.ones_like(points[:, -1:])], dim=-1)
+            # find closest pose idx
+            closest_pose_idx = torch.searchsorted(curr_azimuth, missing_azimuth.squeeze())
+            closest_pose_idx = closest_pose_idx.clamp(max=len(curr_azimuth) - 1)
+            if closest_pose_idx.shape == ():
+                closest_pose_idx = closest_pose_idx.unsqueeze(0)
+            closest_l2w = curr_l2ws[closest_pose_idx]
+            points = torch.matmul(closest_l2w.float(), points.unsqueeze(-1))[:, :, 0]
+            closest_pc_values = point_cloud[channel_mask][sort_idx][closest_pose_idx, 3:]
+            points = torch.cat([points, closest_pc_values], dim=-1)
+            missing_points.append(points)
+
+        missing_points = torch.cat(missing_points, dim=0)
+        missing_points_sensor_frame = torch.cat(missing_points_sensor_frame, dim=0)
+        return missing_points
+
+
+def _get_mean_pose_from_trajectory(trajectory):
+    """Computes the mean pose from a trajectory of positions."""
+    trajectory = np.array(trajectory)
+    mean_position = np.mean(trajectory, axis=0)
+    # Compute the mean direction vector
+    directions = np.diff(trajectory, axis=0)  # we dont normalize as we want to weight each direction with the distance
+    mean_direction = np.mean(directions, axis=0)
+    mean_direction /= np.linalg.norm(mean_direction)
+    # Set the up and right vectors of the mean pose
+    up = np.array([0, 0, 1])
+    right = np.cross(mean_direction, up)
+    # Compute the up vector of the mean pose
+    up = np.cross(right, mean_direction)  # need to recompute up because it might not be orthogonal
+    # Normalize the right, and up vectors
+    right /= np.linalg.norm(right)
+    up /= np.linalg.norm(up)
+    # Construct the transformation matrix
+    pose = np.eye(4)
+    pose[:3, 0] = right
+    pose[:3, 1] = mean_direction
+    pose[:3, 2] = up
+    pose[:3, 3] = mean_position
+    return pose
+
+
+def _get_padded_poses(l2ws, padx: float, pady: float):
+    """Pads the poses with four locations: padx forward/backward, pady left/right."""
+    pad = torch.tensor([[-padx, 0.0, 0.0], [padx, 0.0, 0.0], [0.0, -pady, 0.0], [0.0, pady, 0.0]])
+    pad = pad.unsqueeze(0).repeat(l2ws.shape[0], 1, 1)  # [N, 4, 3]
+    pad = pad.reshape(-1, 3, 1)  # [N*4, 3, 1]
+    l2ws = torch.repeat_interleave(l2ws, 4, dim=0)  # [N*4, 4, 4]
+    pad_in_world = torch.matmul(l2ws[:, :3, :3], pad) + l2ws[:, :3, 3].unsqueeze(-1)  # [N*4, 3, 1]
+    pad_in_world = pad_in_world.reshape(-1, 3)  # [N*4, 3]
+    return pad_in_world
+
+
+def _get_world_to_mean_transform(cameras: Cameras, lidars: Lidars):
+    poses = lidars.lidar_to_worlds if len(lidars) else cameras.camera_to_worlds
+    meta = lidars.metadata if len(lidars) else cameras.metadata
+    assert meta and "sensor_idxs" in meta, "Must have sensor ids in metadata"
+    sensor_idxs = meta["sensor_idxs"].squeeze(-1)
+    selected_idx = sensor_idxs[0]
+    select_poses = poses[sensor_idxs == selected_idx]
+    select_trajectory = select_poses[:, :3, 3]
+    if torch.std(select_trajectory, dim=0).max() < 1e-1:
+        # If the trajectory is stationary, return the first pose
+        m2w = to4x4(select_poses[0:1])[0]
+    else:
+        # Otherwise
+        m2w = torch.from_numpy(_get_mean_pose_from_trajectory(select_trajectory).astype(np.float32))
+    return torch.linalg.inv(m2w)[:3]
+
+
+def _empty_cameras():
+    return Cameras(
+        camera_to_worlds=torch.zeros((0, 3, 4)),
+        cx=torch.zeros((0)),
+        cy=torch.zeros((0)),
+        fx=torch.zeros((0)),
+        fy=torch.zeros((0)),
+        times=torch.zeros((0)),
+        metadata={"sensor_idxs": torch.zeros((0))},
+    )
+
+
+def _empty_lidars():
+    return Lidars(
+        lidar_to_worlds=torch.zeros((0, 3, 4)),
+        times=torch.zeros((0)),
+        metadata={"sensor_idxs": torch.zeros((0))},
+    )
+
+
+def _filter_sensordata_on_time(
+    data: SensorData, filepaths: List[Path], start_time: float, end_time: float
+) -> Tuple[SensorData, List[Path]]:
+    """Filter the data onbased on start and end time."""
+    mask = (data.times >= start_time) & (data.times <= end_time)
+    data = data[mask.squeeze(-1)]
+    filepaths = [filepaths[i] for i in range(len(filepaths)) if mask[i]]
+    return data, filepaths
+
+
+def _paint_points(lidars: Lidars, point_clouds: List[Tensor], cameras: Cameras, image_filenames: List[Path]):
+    """Paints the points in the point cloud."""
+    point_clouds_rgb = []
+    img_cache = []
+    for img_filename in image_filenames:
+        pil_image = Image.open(img_filename)
+        image = np.array(pil_image, dtype="uint8")
+        image = torch.from_numpy(image)
+        img_cache.append(image)
+
+    for lidar, pc in zip(lidars, point_clouds):
+        pc = pc.clone().float()
+        pc_in_world = transform_points(pc[:, :3], lidar.lidar_to_worlds)
+        point_cloud_rgb = torch.rand_like(pc[:, :3]) * 255
+
+        lidar_time = lidar.times.squeeze(-1)
+        top_k_cam_idx = torch.topk(
+            (cameras.times - lidar_time).abs().squeeze(), len(cameras.metadata["sensor_idxs"].unique()), largest=False
+        ).indices
+        for cam_idx in top_k_cam_idx.flip(0):
+            camera = cameras[cam_idx]
+            pc_in_camera = transform_points(pc_in_world, inverse(camera.camera_to_worlds.squeeze(0)))
+            # Flip the y and z axis because of nerfstudio conventions
+            pc_in_camera[:, 1] = -pc_in_camera[:, 1]
+            pc_in_camera[:, 2] = -pc_in_camera[:, 2]
+            # Only paint points in front of the camera
+            valid_points = pc_in_camera[:, 2] > 0
+            # Normalize the points
+            pc_in_camera = pc_in_camera / pc_in_camera[:, 2:]
+
+            intrinsics = camera.get_intrinsics_matrices().squeeze(0)
+            pc_in_image = (torch.matmul(intrinsics, pc_in_camera[:, :3].T).T).to(torch.int64)
+
+            valid_points = (
+                valid_points
+                & (pc_in_image[:, 0] >= 0)
+                & (pc_in_image[:, 0] < camera.width)
+                & (pc_in_image[:, 1] >= 0)
+                & (pc_in_image[:, 1] < camera.height)
+            )
+
+            image = img_cache[cam_idx]
+            point_cloud_rgb[valid_points] = (image[pc_in_image[valid_points, 1], pc_in_image[valid_points, 0]]).float()
+        point_clouds_rgb.append(point_cloud_rgb)
+
+    return point_clouds_rgb
diff --git a/nerfstudio/data/dataparsers/argoverse2_dataparser.py b/nerfstudio/data/dataparsers/argoverse2_dataparser.py
new file mode 100644
index 00000000..138de0a0
--- /dev/null
+++ b/nerfstudio/data/dataparsers/argoverse2_dataparser.py
@@ -0,0 +1,521 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Dataparser for the argoverse2 dataset."""
+
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, List, Literal, Tuple, Type
+
+import av2.utils.io as io_utils
+import numpy as np
+import torch
+from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader, convert_pose_dataframe_to_SE3
+from av2.datasets.sensor.constants import AnnotationCategories
+from av2.geometry.geometry import quat_to_mat
+from av2.structures.sweep import Sweep
+from av2.utils.io import read_ego_SE3_sensor, read_feather
+
+from nerfstudio.cameras.cameras import Cameras, CameraType
+from nerfstudio.cameras.lidars import Lidars, LidarType, transform_points
+from nerfstudio.data.dataparsers.ad_dataparser import (
+    DUMMY_DISTANCE_VALUE,
+    OPENCV_TO_NERFSTUDIO,
+    ADDataParser,
+    ADDataParserConfig,
+)
+from nerfstudio.data.utils.lidar_elevation_mappings import VELODYNE_VLP32C_ELEVATION_MAPPING
+from nerfstudio.utils import poses as pose_utils
+
+MAX_REFLECTANCE_VALUE = 255.0
+HOOD_HEIGHT = 250  # number of pixels to crop from the bottom due to the hood
+
+WLH_TO_LWH = np.array([[0.0, 1.0, 0.0], [-1.0, 0.0, 0.0], [0.0, 0.0, 1.0]], dtype=np.float32)
+HORIZONTAL_BEAM_DIVERGENCE = 3e-3  # radians
+VERTICAL_BEAM_DIVERGENCE = 1.5e-3  # radians
+
+SYMMETRIC_CATEGORIES = {
+    AnnotationCategories.ARTICULATED_BUS,
+    AnnotationCategories.BUS,
+    AnnotationCategories.BOLLARD,
+    AnnotationCategories.BOX_TRUCK,
+    AnnotationCategories.CONSTRUCTION_CONE,
+    AnnotationCategories.LARGE_VEHICLE,
+    AnnotationCategories.RAILED_VEHICLE,
+    AnnotationCategories.REGULAR_VEHICLE,
+    AnnotationCategories.SCHOOL_BUS,
+    AnnotationCategories.TRUCK,
+    AnnotationCategories.TRUCK_CAB,
+    AnnotationCategories.VEHICULAR_TRAILER,
+}
+
+DEFORMABLE_CATEGORIES = {
+    AnnotationCategories.PEDESTRIAN,
+    AnnotationCategories.OFFICIAL_SIGNALER,
+}
+
+ALLOWED_CATEGORIES = {
+    AnnotationCategories.ARTICULATED_BUS,
+    AnnotationCategories.BOX_TRUCK,
+    AnnotationCategories.BUS,
+    AnnotationCategories.LARGE_VEHICLE,
+    AnnotationCategories.RAILED_VEHICLE,
+    AnnotationCategories.REGULAR_VEHICLE,
+    AnnotationCategories.SCHOOL_BUS,
+    AnnotationCategories.TRUCK,
+    AnnotationCategories.TRUCK_CAB,
+    AnnotationCategories.VEHICULAR_TRAILER,
+    # motor cycles
+    AnnotationCategories.MOTORCYCLE,
+    AnnotationCategories.MOTORCYCLIST,
+    # bicycles
+    AnnotationCategories.BICYCLE,
+    AnnotationCategories.BICYCLIST,
+    # wheel devices
+    AnnotationCategories.WHEELED_DEVICE,
+    AnnotationCategories.WHEELED_RIDER,
+}
+
+CAMERA_TO_BOTTOM_RIGHT_CROP = {
+    "ring_front_center": (250, 0),
+    "ring_front_left": (0, 0),
+    "ring_front_right": (0, 0),
+    "ring_rear_left": (250, 0),
+    "ring_rear_right": (250, 0),
+    "ring_side_left": (0, 0),
+    "ring_side_right": (0, 0),
+}
+
+AVAILABLE_CAMERAS = (
+    "ring_front_center",
+    "ring_front_left",
+    "ring_front_right",
+    "ring_rear_left",
+    "ring_rear_right",
+    "ring_side_left",
+    "ring_side_right",
+)
+
+
+# lidar_up is the upper lidar, lidar_down is the lower lidar, and lidar is short for any of them
+AV2_VELO_VLP32C_ELEVATION_MAPPING = {
+    i: j for i, j in enumerate(sorted(VELODYNE_VLP32C_ELEVATION_MAPPING.values(), reverse=True))
+}
+AV2_ELEVATION_MAPPING = {
+    "lidar_down": AV2_VELO_VLP32C_ELEVATION_MAPPING,
+    "lidar_up": AV2_VELO_VLP32C_ELEVATION_MAPPING,
+    "lidar": AV2_VELO_VLP32C_ELEVATION_MAPPING,
+}
+AV2_AZIMUTH_RESOLUTION = {
+    "lidar_down": 0.2,
+    "lidar_up": 0.2,
+    "lidar": 0.2,
+}  # degrees
+AV2_SKIP_ELEVATION_CHANNELS = {
+    "lidar": (0, 1, 2),
+    "lidar_down": (0, 1, 2),  # corresponding to 15,10.333,7.0 degrees as lidar_down is upside down
+    "lidar_up": (29, 30, 31),  # corresponding to -25,-15.639,-11.31 degrees
+}
+AV2_IGNORE_REGIONS = {
+    "lidar_down": [[140, 200, -30.0, 20.0], [-200.0, -170.0, -30.0, 20.0]],
+    "lidar_up": [],
+    "lidar": [],
+}
+
+
+@dataclass
+class Argoverse2DataParserConfig(ADDataParserConfig):
+    """Argoverse 2 dataset config."""
+
+    _target: Type = field(default_factory=lambda: Argoverse2)
+    """target class to instantiate"""
+    sequence: str = "2b044433-ddc1-3580-b560-d46474934089"
+    """name of the sequence to use"""
+    data: Path = Path("data/argoverse2")
+    """path to the dataset"""
+    cameras: Tuple[
+        Literal[
+            "ring_front_center",
+            "ring_front_left",
+            "ring_front_right",
+            "ring_rear_left",
+            "ring_rear_right",
+            "ring_side_left",
+            "ring_side_right",
+            "none",
+            "all",
+        ],
+        ...,
+    ] = ("all",)
+    """what cameras to use"""
+    lidars: Tuple[Literal["lidar", "none"], ...] = ("lidar",)
+    """what lidars to use. only one lidar is available."""
+    annotation_interval: float = 0.1
+    """interval between annotations in seconds"""
+    split: str = "val"
+    """what split to use. options are: train, val, test, mini, mini_val, mini_test"""
+    add_missing_points: bool = True
+    """whether to add missing points to the point clouds"""
+    lidar_elevation_mapping: Dict[str, Dict[int, float]] = field(default_factory=lambda: AV2_ELEVATION_MAPPING)
+    """Elevation mapping for each lidar."""
+    lidar_azimuth_resolution: Dict[str, float] = field(default_factory=lambda: AV2_AZIMUTH_RESOLUTION)
+    """Azimuth resolution for each lidar."""
+    skip_elevation_channels: Dict[str, Tuple] = field(default_factory=lambda: AV2_SKIP_ELEVATION_CHANNELS)
+    """Channels to skip when adding missing points."""
+    max_eval_frames: int = 100
+    """maximum number of frames to use for evaluation"""
+
+
+@dataclass
+class Argoverse2(ADDataParser):
+    """Argoverse2 DatasetParser."""
+
+    config: Argoverse2DataParserConfig
+
+    @property
+    def actor_transform(self) -> torch.Tensor:
+        """Argo uses x-forward, so we need to rotate to x-right."""
+        wlh_to_lwh = np.eye(4)
+        wlh_to_lwh[:3, :3] = WLH_TO_LWH
+        return torch.from_numpy(wlh_to_lwh)
+
+    def _get_cameras(self) -> Tuple[Cameras, List[Path]]:
+        """Returns camera info and image filenames."""
+        if "all" in self.config.cameras:
+            self.config.cameras = AVAILABLE_CAMERAS
+        filenames, times, intrinsics, poses, idxs = [], [], [], [], []
+        heights, widths = [], []
+
+        for camera_idx, cam_name in enumerate(self.config.cameras):
+            pinhole_camera = self.av2.get_log_pinhole_camera(self.config.sequence, cam_name)
+
+            # get all camera paths
+            camera_paths = self.av2.get_ordered_log_cam_fpaths(self.config.sequence, cam_name)
+            # timestamps is the filename without the extension
+            timestamps = [int(p.stem) for p in camera_paths]
+
+            # compute the poses of each camera at each timestamp
+            for t, fp in zip(timestamps, camera_paths):
+                # get the ego-pose
+                ego2world = self.av2.get_city_SE3_ego(self.config.sequence, t)
+                cam_extrinsics = pinhole_camera.ego_SE3_cam.transform_matrix.copy()
+                cam_extrinsics[:3, :3] = cam_extrinsics[:3, :3] @ OPENCV_TO_NERFSTUDIO
+                poses.append(ego2world.transform_matrix @ cam_extrinsics)
+                # add the camera intrinsics
+                intrinsics.append(pinhole_camera.intrinsics.K)
+                # add the camera height and width, which differ between cameras
+                # the ring_front_center is in portrait mode and the others are in landscape mode
+                heights.append(pinhole_camera.intrinsics.height_px - CAMERA_TO_BOTTOM_RIGHT_CROP[cam_name][0])
+                widths.append(pinhole_camera.intrinsics.width_px - CAMERA_TO_BOTTOM_RIGHT_CROP[cam_name][1])
+                # append the times and filenames to the lists
+                times.append(t / 1e9)  # convert to seconds
+                filenames.append(fp)
+                # add the camera index
+                idxs.append(camera_idx)
+
+        # To tensors
+        intrinsics = torch.from_numpy(np.array(intrinsics)).float()
+        poses = torch.tensor(np.array(poses), dtype=torch.float32)
+        times = torch.tensor(times, dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+        heights = torch.tensor(heights).int()
+        widths = torch.tensor(widths).int()
+
+        cameras = Cameras(
+            fx=intrinsics[:, 0, 0],
+            fy=intrinsics[:, 1, 1],
+            cx=intrinsics[:, 0, 2],
+            cy=intrinsics[:, 1, 2],
+            height=heights,
+            width=widths,
+            camera_to_worlds=poses[:, :3, :4],
+            camera_type=CameraType.PERSPECTIVE,
+            times=times,
+            metadata={"sensor_idxs": idxs},
+        )
+
+        return cameras, filenames
+
+    def _get_lidars(self) -> Tuple[Lidars, List[Path]]:
+        """Returns lidar info and loaded point clouds."""
+
+        (
+            lidar_filenames,
+            times,
+            poses_up,
+            poses_down,
+            idxs,
+        ) = ([], [], [], [], [])
+
+        lidar_times = self.av2.get_ordered_log_lidar_timestamps(self.config.sequence)
+
+        for t in lidar_times:
+            # get the path to the lidar point cloud
+            pc_path = self.av2.get_lidar_fpath(self.config.sequence, t)
+            lidar_filenames.append(pc_path)
+
+            log_dir = pc_path.parent.parent.parent
+            sensor_name_to_pose = read_ego_SE3_sensor(log_dir=log_dir)
+            ego_SE3_up_lidar = sensor_name_to_pose["up_lidar"]
+            ego_SE3_down_lidar = sensor_name_to_pose["down_lidar"]
+
+            # get the ego-vehicle pose at the lidar timestamp
+            ego_pose = self.av2.get_city_SE3_ego(self.config.sequence, t)
+            # move to the actual lidar position
+            ego_pose_up = ego_pose.compose(ego_SE3_up_lidar)
+            ego_pose_down = ego_pose.compose(ego_SE3_down_lidar)
+            # add the pose
+            poses_up.append(ego_pose_up.transform_matrix)
+            poses_down.append(ego_pose_down.transform_matrix)
+            idxs.append(0)  # add the lidar index (there is only one so lets use 0)
+            times.append(t / 1e9)  # convert to seconds
+
+        poses_up = torch.tensor(np.array(poses_up), dtype=torch.float32)
+        poses_down = torch.tensor(np.array(poses_down), dtype=torch.float32)
+        down2up = pose_utils.inverse(poses_up[0, ...]) @ poses_down[0, ...]
+        times = torch.tensor(np.array(times, dtype=np.float64), dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(np.array(idxs)).int().unsqueeze(-1)
+        # we will store the down2up transformation in the metadata, as we will need it later
+        down2up = down2up.repeat(len(poses_up), 1, 1).view(len(poses_up), -1)
+        poses_down = poses_down.view(len(poses_up), -1)
+
+        lidars = Lidars(
+            lidar_to_worlds=poses_up[:, :3, :4],
+            lidar_type=LidarType.VELODYNE_VLP32C,
+            times=times,
+            metadata={
+                "sensor_idxs": idxs,
+                "down2up": down2up,  # will be removed later
+                "poses_down": poses_down,  # will be removed later
+            },
+            horizontal_beam_divergence=HORIZONTAL_BEAM_DIVERGENCE,
+            vertical_beam_divergence=VERTICAL_BEAM_DIVERGENCE,
+            valid_lidar_distance_threshold=DUMMY_DISTANCE_VALUE / 2,
+        )
+
+        return lidars, lidar_filenames
+
+    def _read_lidars(self, lidars: Lidars, filepaths: List[Path]) -> List[torch.Tensor]:
+        point_clouds_up, point_clouds_down = [], []
+
+        for pc_path in filepaths:
+            sweep = Sweep.from_feather(pc_path)
+            # get laser_number, 0-31 is up_lidar, 32-63 is down_lidar according to
+            laser_number = sweep.laser_number
+            # transform the points back to the sensor position
+            xyz_up = sweep.ego_SE3_up_lidar.inverse().transform_point_cloud(sweep.xyz[laser_number < 32])
+            xyz_down = sweep.ego_SE3_down_lidar.inverse().transform_point_cloud(sweep.xyz[laser_number >= 32])
+            # normalize the intensity values to be in [0, 1]
+            intensity = sweep.intensity / MAX_REFLECTANCE_VALUE
+            # Add relative time
+            time = sweep.offset_ns / 1e9
+            # add the intensity as the last channel
+            xyz_intensity_up = np.concatenate(
+                [
+                    xyz_up,
+                    intensity[laser_number < 32, None],
+                    time[laser_number < 32, None],
+                    laser_number[laser_number < 32, None],
+                ],
+                axis=1,
+            )
+            xyz_intensity_down = np.concatenate(
+                [
+                    xyz_down,
+                    intensity[laser_number >= 32, None],
+                    time[laser_number >= 32, None],
+                    laser_number[laser_number >= 32, None],
+                ],
+                axis=1,
+            )
+            # add the point cloud
+            point_clouds_up.append(torch.from_numpy(xyz_intensity_up))
+            point_clouds_down.append(torch.from_numpy(xyz_intensity_down))
+
+        missing_points = []
+
+        if self.config.add_missing_points:
+            assert lidars.metadata is not None  # typehints
+            assert lidars.times is not None  # typehints
+            down2up = lidars.metadata["down2up"].view(len(lidars.times), 3, 4)[0]
+            poses_down = lidars.metadata["poses_down"].view(len(lidars.times), 4, 4)
+            poses_up = lidars.lidar_to_worlds
+            times = lidars.times
+
+            log_pose_df = io_utils.read_feather(
+                self.av2._data_dir / self.config.sequence / "city_SE3_egovehicle.feather"
+            )
+            all_ego2w = [convert_pose_dataframe_to_SE3(pose_df) for _, pose_df in log_pose_df.iterrows()]
+            # calibration is constant, just use the last one
+            assert sweep is not None
+            uplidar2ego = sweep.ego_SE3_up_lidar
+            all_lup2w = torch.tensor(
+                np.array([e2w.compose(uplidar2ego).transform_matrix for e2w in all_ego2w]), dtype=torch.float32
+            )
+            downlidar2ego = sweep.ego_SE3_down_lidar
+            all_ldown2w = torch.tensor(
+                np.array([e2w.compose(downlidar2ego).transform_matrix for e2w in all_ego2w]), dtype=torch.float32
+            )
+            all_times = torch.from_numpy(log_pose_df["timestamp_ns"].to_numpy() / 1e9)
+
+            for i, (pc_up, pc_down, lup2w, ldown2w, time) in enumerate(
+                zip(point_clouds_up, point_clouds_down, poses_up, poses_down, times)
+            ):
+                mp = []
+                for j, (pc_, l2w) in enumerate(zip((pc_up, pc_down), (lup2w, ldown2w))):
+                    pc = pc_.clone().to(torch.float64)
+                    # relative -> absolute time
+                    pc[:, -2] += time
+                    # project to world frame
+                    pc[..., :3] = transform_points(pc[..., :3], l2w.unsqueeze(0).to(pc))
+                    # remove ego motion compensation and move to sensor frame
+                    if j == 0:  # up_lidar
+                        l2ws = all_lup2w
+                        lidar_name = "lidar_up"
+                    else:  # down_lidar
+                        l2ws = all_ldown2w
+                        lidar_name = "lidar_down"
+                    pc, interpolated_poses = self._remove_ego_motion_compensation(pc, l2ws, all_times)
+                    # reset time to relative
+                    pc[:, 4] = pc_[:, 4].clone()
+                    # transform to common lidar frame again, interpolated_poses is l2w for each point at its timestamp
+                    interpolated_poses = torch.matmul(
+                        pose_utils.inverse(l2w.unsqueeze(0)).float(), pose_utils.to4x4(interpolated_poses).float()
+                    )
+                    # move channel from index 5 to 3
+                    pc = pc[..., [0, 1, 2, 5, 3, 4]]
+                    if j == 1:  # down_lidar
+                        pc[..., 3] -= 32
+                    # add missing points
+                    miss_points = self._get_missing_points(
+                        pc,
+                        interpolated_poses,
+                        lidar_name,
+                        dist_cutoff=0.0,
+                        outlier_thresh=10.0,
+                        ignore_regions=AV2_IGNORE_REGIONS[lidar_name],
+                    )
+                    if j == 1:  # down_lidar
+                        miss_points[..., 3] += 32
+                        miss_points[..., :3] = transform_points(
+                            miss_points[..., :3], down2up.unsqueeze(0).to(miss_points)
+                        )
+                    # move channel back from index 3 to 5
+                    miss_points = miss_points[..., [0, 1, 2, 4, 5, 3]]
+                    mp.append(miss_points.float())
+                missing_points.append(torch.cat(mp, dim=0))
+
+        # transform all points to common lidar frame (up_lidar)
+        point_clouds = [
+            torch.cat([pc_up, transform_points(pc_down, down2up.unsqueeze(0).to(pc_down))], dim=0)
+            for pc_up, pc_down in zip(point_clouds_up, point_clouds_down)
+        ]
+        if self.config.add_missing_points:
+            point_clouds = [torch.cat([pc, mp], dim=0) for pc, mp in zip(point_clouds, missing_points)]
+        point_clouds = [pc.float() for pc in point_clouds]
+
+        lidars.lidar_to_worlds = lidars.lidar_to_worlds.float()
+        if lidars.metadata:
+            del lidars.metadata["down2up"]  # we dont need this anymore
+            del lidars.metadata["poses_down"]  # we dont need this anymore
+        return point_clouds
+
+    def _get_actor_trajectories(self) -> List[Dict]:
+        """Returns a list of actor trajectories.
+
+        Each trajectory is a dictionary with the following keys:
+            - poses: the poses of the actor (float32)
+            - timestamps: the timestamps of the actor (float64)
+            - dims: the dimensions of the actor, wlh order (float32)
+            - label: the label of the actor (str)
+            - stationary: whether the actor is stationary (bool)
+            - symmetric: whether the actor is expected to be symmetric (bool)
+            - deformable: whether the actor is expected to be deformable (e.g. pedestrian)
+        """
+        trajs = []
+
+        # frames are annotated at the lidar timestamp
+        anno_times = self.av2.get_ordered_log_lidar_timestamps(self.config.sequence)
+        annos = read_feather(self.av2._data_dir / self.config.sequence / "annotations.feather")
+        unique_tracks = annos["track_uuid"].unique()
+        all_ego_poses = {t: self.av2.get_city_SE3_ego(self.config.sequence, t).transform_matrix for t in anno_times}
+
+        for track_uuid in unique_tracks:
+            rows = annos[annos["track_uuid"] == track_uuid]
+            # TODO: how to handle this?
+            if len(rows) < 2:
+                # only keep tracks that have at least 2 annotations
+                continue
+
+            first_row = rows.iloc[0]
+            w, l, h = first_row["width_m"], first_row["length_m"], first_row["height_m"]  # noqa: E741
+            wlh = np.array([w, l, h], dtype=np.float32)
+            label = first_row["category"]
+            # remove all objects that are not in the allowed categories
+            allowed_cats = ALLOWED_CATEGORIES
+            if self.config.include_deformable_actors:
+                allowed_cats = allowed_cats.union(DEFORMABLE_CATEGORIES)
+            if label not in ALLOWED_CATEGORIES:
+                continue
+            # check if the actor is symmetric
+            symmetric = label in SYMMETRIC_CATEGORIES
+            # check if the actor is deformable
+            deformable = label in DEFORMABLE_CATEGORIES
+
+            rotations = quat_to_mat(rows.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy())
+            # rotate 90 deg around z counter clockwise to get the correct orientation
+            rotations = np.matmul(rotations, WLH_TO_LWH)
+            translation_xyz_m = rows.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy()
+            # get the ego-vehicle poses at the annotation times as the annotations are relative to the ego-vehicle
+            ego_poses = np.array([all_ego_poses[t] for t in rows["timestamp_ns"]])
+            # get the poses in the ego-vehicle frame
+            poses = np.zeros((len(rows), 4, 4), dtype=np.float32)
+            poses[:, :3, :3] = rotations
+            poses[:, :3, 3] = translation_xyz_m
+            poses[:, 3, 3] = 1
+
+            # transform the actor poses to the ego-vehicle frame
+            actor_world_poses = np.matmul(ego_poses, poses)
+
+            # check if the actor is stationary, if it has moved more than 0.5m in any direction
+            dynamic = np.any(np.std(actor_world_poses[:, :3, 3], axis=0) > 0.5)
+
+            # remove all static objects
+            if not dynamic:
+                continue
+
+            trajs.append(
+                {
+                    "poses": torch.tensor(actor_world_poses, dtype=torch.float32),
+                    "timestamps": torch.tensor(rows["timestamp_ns"].to_numpy(), dtype=torch.float64) / 1e9,
+                    "dims": torch.tensor(wlh, dtype=torch.float32),
+                    "label": label,
+                    "stationary": not dynamic,
+                    "symmetric": symmetric,
+                    "deformable": deformable,
+                }
+            )
+
+        return trajs
+
+    def _generate_dataparser_outputs(self, split="train"):
+        datapath = self.config.data / "sensor" / self.config.split
+        self.av2 = AV2SensorDataLoader(datapath, datapath)
+
+        assert self.config.sequence in self.av2.get_log_ids(), f"Sequence {self.config.sequence} not found in dataset."
+        out = super()._generate_dataparser_outputs(split=split)
+
+        del self.av2
+        return out
diff --git a/nerfstudio/data/dataparsers/arkitscenes_dataparser.py b/nerfstudio/data/dataparsers/arkitscenes_dataparser.py
deleted file mode 100644
index 8dbd7d8b..00000000
--- a/nerfstudio/data/dataparsers/arkitscenes_dataparser.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for ARKitScenes dataset"""
-import math
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Literal, Type
-
-import cv2
-import numpy as np
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-
-
-# Taken from https://github.com/apple/ARKitScenes/blob/main/threedod/benchmark_scripts/utils/tenFpsDataLoader.py
-def traj_string_to_matrix(traj_string: str):
-    """convert traj_string into translation and rotation matrices
-    Args:
-        traj_string: A space-delimited file where each line represents a camera position at a particular timestamp.
-        The file has seven columns:
-        * Column 1: timestamp
-        * Columns 2-4: rotation (axis-angle representation in radians)
-        * Columns 5-7: translation (usually in meters)
-    Returns:
-        ts: translation matrix
-        Rt: rotation matrix
-    """
-    tokens = traj_string.split()
-    assert len(tokens) == 7
-    ts = tokens[0]
-    # Rotation in angle axis
-    angle_axis = [float(tokens[1]), float(tokens[2]), float(tokens[3])]
-    r_w_to_p, _ = cv2.Rodrigues(np.asarray(angle_axis))  # type: ignore
-    # Translation
-    t_w_to_p = np.asarray([float(tokens[4]), float(tokens[5]), float(tokens[6])])
-    extrinsics = np.eye(4, 4)
-    extrinsics[:3, :3] = r_w_to_p
-    extrinsics[:3, -1] = t_w_to_p
-    Rt = np.linalg.inv(extrinsics)
-    return (ts, Rt)
-
-
-@dataclass
-class ARKitScenesDataParserConfig(DataParserConfig):
-    """ARKitScenes dataset config.
-    ARKitScenes dataset (http://github.com/apple/ARKitScenes) is a large-scale 3D dataset of indoor scenes.
-    This dataparser uses 3D detection subset of the ARKitScenes dataset.
-    """
-
-    _target: Type = field(default_factory=lambda: ARKitScenes)
-    """target class to instantiate"""
-    data: Path = Path("data/ARKitScenes/3dod/Validation/41069021")
-    """Path to ARKitScenes folder with densely extracted scenes."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    scene_scale: float = 1.0
-    """How much to scale the region of interest by."""
-    center_method: Literal["poses", "focus", "none"] = "poses"
-    """The method to use to center the poses."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-    train_split_fraction: float = 0.9
-    """The fraction of images to use for training. The remaining images are for eval."""
-    depth_unit_scale_factor: float = 1e-3
-    """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion."""
-
-
-@dataclass
-class ARKitScenes(DataParser):
-    """ARKitScenes DatasetParser"""
-
-    config: ARKitScenesDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        video_id = self.config.data.name
-
-        image_dir = self.config.data / f"{video_id}_frames" / "lowres_wide"
-        depth_dir = self.config.data / f"{video_id}_frames" / "lowres_depth"
-        intrinsics_dir = self.config.data / f"{video_id}_frames" / "lowres_wide_intrinsics"
-        pose_file = self.config.data / f"{video_id}_frames" / "lowres_wide.traj"
-
-        frame_ids = [x.name for x in sorted(depth_dir.iterdir())]
-        frame_ids = [x.split(".png")[0].split("_")[1] for x in frame_ids]
-        frame_ids.sort()
-
-        poses_from_traj = {}
-        with open(pose_file, "r", encoding="utf-8") as f:
-            traj = f.readlines()
-
-        for line in traj:
-            poses_from_traj[f"{round(float(line.split(' ')[0]), 3):.3f}"] = np.array(
-                traj_string_to_matrix(line)[1].tolist()
-            )
-
-        image_filenames, depth_filenames, intrinsics, poses = [], [], [], []
-        w, h, _, _, _, _ = np.loadtxt(list(sorted(intrinsics_dir.iterdir()))[0])  # Get image size from first intrinsic
-
-        for frame_id in frame_ids:
-            intrinsic = self._get_intrinsic(intrinsics_dir, frame_id, video_id)
-            frame_pose = self._get_pose(frame_id, poses_from_traj)
-
-            intrinsics.append(intrinsic)
-            image_filenames.append(image_dir / f"{video_id}_{frame_id}.png")
-            depth_filenames.append(depth_dir / f"{video_id}_{frame_id}.png")
-            poses.append(frame_pose)
-
-        # filter image_filenames and poses based on train/eval split percentage
-        num_images = len(image_filenames)
-        num_train_images = math.ceil(num_images * self.config.train_split_fraction)
-        num_eval_images = num_images - num_train_images
-        i_all = np.arange(num_images)
-        i_train = np.linspace(
-            0, num_images - 1, num_train_images, dtype=int
-        )  # equally spaced training images starting and ending at 0 and num_images-1
-        i_eval = np.setdiff1d(i_all, i_train)  # eval images are the remaining images
-        assert len(i_eval) == num_eval_images
-        if split == "train":
-            indices = i_train
-        elif split in ["val", "test"]:
-            indices = i_eval
-        else:
-            raise ValueError(f"Unknown dataparser split {split}")
-
-        poses = torch.from_numpy(np.stack(poses).astype(np.float32))
-        intrinsics = torch.from_numpy(np.stack(intrinsics).astype(np.float32))
-
-        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
-            poses,
-            method="none",
-            center_method=self.config.center_method,
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
-        scale_factor *= self.config.scale_factor
-
-        poses[:, :3, 3] *= scale_factor
-
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        image_filenames = [image_filenames[i] for i in indices]
-        depth_filenames = [depth_filenames[i] for i in indices] if len(depth_filenames) > 0 else []
-        intrinsics = intrinsics[indices.tolist()]
-        poses = poses[indices.tolist()]
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        cameras = Cameras(
-            fx=intrinsics[:, 0, 0],
-            fy=intrinsics[:, 1, 1],
-            cx=intrinsics[:, 0, 2],
-            cy=intrinsics[:, 1, 2],
-            height=int(h),
-            width=int(w),
-            camera_to_worlds=poses[:, :3, :4],
-            camera_type=CameraType.PERSPECTIVE,
-        )
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            dataparser_scale=scale_factor,
-            dataparser_transform=transform_matrix,
-            metadata={
-                "depth_filenames": depth_filenames if len(depth_filenames) > 0 else None,
-                "depth_unit_scale_factor": self.config.depth_unit_scale_factor,
-            },
-        )
-        return dataparser_outputs
-
-    @staticmethod
-    def _get_intrinsic(intrinsics_dir: Path, frame_id: str, video_id: str):
-        intrinsic_fn = intrinsics_dir / f"{video_id}_{frame_id}.pincam"
-
-        if not intrinsic_fn.exists():
-            intrinsic_fn = intrinsics_dir / f"{video_id}_{float(frame_id) - 0.001:.3f}.pincam"
-
-        if not intrinsic_fn.exists():
-            intrinsic_fn = intrinsics_dir / f"{video_id}_{float(frame_id) + 0.001:.3f}.pincam"
-
-        _, _, fx, fy, hw, hh = np.loadtxt(intrinsic_fn)
-        intrinsic = np.asarray([[fx, 0, hw], [0, fy, hh], [0, 0, 1]])
-        return intrinsic
-
-    @staticmethod
-    def _get_pose(frame_id: str, poses_from_traj: dict):
-        frame_pose = None
-        if str(frame_id) in poses_from_traj:
-            frame_pose = np.array(poses_from_traj[str(frame_id)])
-        else:
-            for my_key in poses_from_traj:
-                if abs(float(frame_id) - float(my_key)) < 0.005:
-                    frame_pose = np.array(poses_from_traj[str(my_key)])
-
-        assert frame_pose is not None
-        frame_pose[0:3, 1:3] *= -1
-        frame_pose = frame_pose[np.array([1, 0, 2, 3]), :]
-        frame_pose[2, :] *= -1
-        return frame_pose
diff --git a/nerfstudio/data/dataparsers/base_dataparser.py b/nerfstudio/data/dataparsers/base_dataparser.py
index 5cf1e6bd..9ec52eea 100644
--- a/nerfstudio/data/dataparsers/base_dataparser.py
+++ b/nerfstudio/data/dataparsers/base_dataparser.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -66,9 +67,13 @@ class DataparserOutputs:
     Will be processed by the InputDataset to create any additional tensors that may be required.
     """
     dataparser_transform: Float[Tensor, "3 4"] = torch.eye(4)[:3, :]
-    """Transform applied by the dataparser."""
+    """Transform applied by the dataparser to the entire scene."""
+    actor_transform: Float[Tensor, "3 4"] = torch.eye(4)[:3, :]
+    """Transform applied by the dataparser to each actor's local frame."""
     dataparser_scale: float = 1.0
     """Scale applied by the dataparser."""
+    time_offset: float = 0.0
+    """Time offset (min time)."""
 
     def as_dict(self) -> dict:
         """Returns the dataclass as a dictionary."""
@@ -84,6 +89,7 @@ def save_dataparser_transform(self, path: Path):
         data = {
             "transform": self.dataparser_transform.tolist(),
             "scale": float(self.dataparser_scale),
+            "time-offset": self.time_offset,
         }
         if not path.parent.exists():
             path.parent.mkdir(parents=True)
diff --git a/nerfstudio/data/dataparsers/blender_dataparser.py b/nerfstudio/data/dataparsers/blender_dataparser.py
deleted file mode 100644
index 64ccf13d..00000000
--- a/nerfstudio/data/dataparsers/blender_dataparser.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for blender dataset"""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Type
-
-import imageio
-import numpy as np
-import torch
-
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.colors import get_color
-from nerfstudio.utils.io import load_from_json
-
-
-@dataclass
-class BlenderDataParserConfig(DataParserConfig):
-    """Blender dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: Blender)
-    """target class to instantiate"""
-    data: Path = Path("data/blender/lego")
-    """Directory specifying location of data."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    alpha_color: str = "white"
-    """alpha color of background"""
-
-
-@dataclass
-class Blender(DataParser):
-    """Blender Dataset
-    Some of this code comes from https://github.com/yenchenlin/nerf-pytorch/blob/master/load_blender.py#L37.
-    """
-
-    config: BlenderDataParserConfig
-
-    def __init__(self, config: BlenderDataParserConfig):
-        super().__init__(config=config)
-        self.data: Path = config.data
-        self.scale_factor: float = config.scale_factor
-        self.alpha_color = config.alpha_color
-        if self.alpha_color is not None:
-            self.alpha_color_tensor = get_color(self.alpha_color)
-        else:
-            self.alpha_color_tensor = None
-
-    def _generate_dataparser_outputs(self, split="train"):
-        meta = load_from_json(self.data / f"transforms_{split}.json")
-        image_filenames = []
-        poses = []
-        for frame in meta["frames"]:
-            fname = self.data / Path(frame["file_path"].replace("./", "") + ".png")
-            image_filenames.append(fname)
-            poses.append(np.array(frame["transform_matrix"]))
-        poses = np.array(poses).astype(np.float32)
-
-        img_0 = imageio.v2.imread(image_filenames[0])
-        image_height, image_width = img_0.shape[:2]
-        camera_angle_x = float(meta["camera_angle_x"])
-        focal_length = 0.5 * image_width / np.tan(0.5 * camera_angle_x)
-
-        cx = image_width / 2.0
-        cy = image_height / 2.0
-        camera_to_world = torch.from_numpy(poses[:, :3])  # camera to world transform
-
-        # in x,y,z order
-        camera_to_world[..., 3] *= self.scale_factor
-        scene_box = SceneBox(aabb=torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]], dtype=torch.float32))
-
-        cameras = Cameras(
-            camera_to_worlds=camera_to_world,
-            fx=focal_length,
-            fy=focal_length,
-            cx=cx,
-            cy=cy,
-            camera_type=CameraType.PERSPECTIVE,
-        )
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            alpha_color=self.alpha_color_tensor,
-            scene_box=scene_box,
-            dataparser_scale=self.scale_factor,
-        )
-
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/colmap_dataparser.py b/nerfstudio/data/dataparsers/colmap_dataparser.py
deleted file mode 100644
index 5f5917e7..00000000
--- a/nerfstudio/data/dataparsers/colmap_dataparser.py
+++ /dev/null
@@ -1,548 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Data parser for nerfstudio datasets. """
-
-from __future__ import annotations
-
-import sys
-from dataclasses import dataclass, field
-from functools import partial
-from pathlib import Path
-from typing import List, Literal, Optional, Type
-
-import numpy as np
-import torch
-from PIL import Image
-from rich.prompt import Confirm
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import CAMERA_MODEL_TO_TYPE, Cameras
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.data.utils import colmap_parsing_utils as colmap_utils
-from nerfstudio.data.utils.dataparsers_utils import (
-    get_train_eval_split_all,
-    get_train_eval_split_filename,
-    get_train_eval_split_fraction,
-    get_train_eval_split_interval,
-)
-from nerfstudio.process_data.colmap_utils import parse_colmap_camera_params
-from nerfstudio.utils.rich_utils import CONSOLE, status
-from nerfstudio.utils.scripts import run_command
-
-MAX_AUTO_RESOLUTION = 1600
-
-
-@dataclass
-class ColmapDataParserConfig(DataParserConfig):
-    """Nerfstudio dataset config"""
-
-    _target: Type = field(default_factory=lambda: ColmapDataParser)
-    """target class to instantiate"""
-    data: Path = Path()
-    """Directory or explicit json file path specifying location of data."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    downscale_factor: Optional[int] = None
-    """How much to downscale images. If not set, images are chosen such that the max dimension is <1600px."""
-    scene_scale: float = 1.0
-    """How much to scale the region of interest by."""
-    orientation_method: Literal["pca", "up", "vertical", "none"] = "up"
-    """The method to use for orientation."""
-    center_method: Literal["poses", "focus", "none"] = "poses"
-    """The method to use to center the poses."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-    assume_colmap_world_coordinate_convention: bool = True
-    """Colmap optimized world often have y direction of the first camera pointing towards down direction,
-    while nerfstudio world set z direction to be up direction for viewer. Therefore, we usually need to apply an extra
-    transform when orientation_method=none. This parameter has no effects if orientation_method is set other than none.
-    When this parameter is set to False, no extra transform is applied when reading data from colmap.
-    """
-    eval_mode: Literal["fraction", "filename", "interval", "all"] = "interval"
-    """
-    The method to use for splitting the dataset into train and eval.
-    Fraction splits based on a percentage for train and the remaining for eval.
-    Filename splits based on filenames containing train/eval.
-    Interval uses every nth frame for eval (used by most academic papers, e.g. MipNerf360, GSplat).
-    All uses all the images for any split.
-    """
-    train_split_fraction: float = 0.9
-    """The fraction of images to use for training. The remaining images are for eval."""
-    eval_interval: int = 8
-    """The interval between frames to use for eval. Only used when eval_mode is eval-interval."""
-    depth_unit_scale_factor: float = 1e-3
-    """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion."""
-
-    images_path: Path = Path("images")
-    """Path to images directory relative to the data path."""
-    masks_path: Optional[Path] = None
-    """Path to masks directory. If not set, masks are not loaded."""
-    depths_path: Optional[Path] = None
-    """Path to depth maps directory. If not set, depths are not loaded."""
-    colmap_path: Path = Path("colmap/sparse/0")
-    """Path to the colmap reconstruction directory relative to the data path."""
-    load_3D_points: bool = True
-    """Whether to load the 3D points from the colmap reconstruction. This is helpful for Gaussian splatting and
-    generally unused otherwise, but it's typically harmless so we default to True."""
-    max_2D_matches_per_3D_point: int = 0
-    """Maximum number of 2D matches per 3D point. If set to -1, all 2D matches are loaded. If set to 0, no 2D matches are loaded."""
-
-
-class ColmapDataParser(DataParser):
-    """COLMAP DatasetParser.
-    Expects a folder with the following structure:
-        images/ # folder containing images used to create the COLMAP model
-        sparse/0 # folder containing the COLMAP reconstruction (either TEXT or BINARY format)
-        masks/ # (OPTIONAL) folder containing masks for each image
-        depths/ # (OPTIONAL) folder containing depth maps for each image
-    The paths can be different and can be specified in the config. (e.g., sparse/0 -> sparse)
-    Currently, most COLMAP camera models are supported except for the FULL_OPENCV and THIN_PRISM_FISHEYE models.
-
-    The dataparser loads the downscaled images from folders with `_{downscale_factor}` suffix.
-    If these folders do not exist, the user can choose to automatically downscale the images and
-    create these folders.
-
-    The loader is compatible with the datasets processed using the ns-process-data script and
-    can be used as a drop-in replacement. It further supports datasets like Mip-NeRF 360 (although
-    in the case of Mip-NeRF 360 the downsampled images may have a different resolution because they
-    use different rounding when computing the image resolution).
-    """
-
-    config: ColmapDataParserConfig
-
-    def __init__(self, config: ColmapDataParserConfig):
-        super().__init__(config)
-        self.config = config
-        self._downscale_factor = None
-
-    def _get_all_images_and_cameras(self, recon_dir: Path):
-        if (recon_dir / "cameras.txt").exists():
-            cam_id_to_camera = colmap_utils.read_cameras_text(recon_dir / "cameras.txt")
-            im_id_to_image = colmap_utils.read_images_text(recon_dir / "images.txt")
-        elif (recon_dir / "cameras.bin").exists():
-            cam_id_to_camera = colmap_utils.read_cameras_binary(recon_dir / "cameras.bin")
-            im_id_to_image = colmap_utils.read_images_binary(recon_dir / "images.bin")
-        else:
-            raise ValueError(f"Could not find cameras.txt or cameras.bin in {recon_dir}")
-
-        cameras = {}
-        frames = []
-        camera_model = None
-
-        # Parse cameras
-        for cam_id, cam_data in cam_id_to_camera.items():
-            cameras[cam_id] = parse_colmap_camera_params(cam_data)
-
-        # Parse frames
-        # we want to sort all images based on im_id
-        ordered_im_id = sorted(im_id_to_image.keys())
-        for im_id in ordered_im_id:
-            im_data = im_id_to_image[im_id]
-            # NB: COLMAP uses Eigen / scalar-first quaternions
-            # * https://colmap.github.io/format.html
-            # * https://github.com/colmap/colmap/blob/bf3e19140f491c3042bfd85b7192ef7d249808ec/src/base/pose.cc#L75
-            # the `rotation_matrix()` handles that format for us.
-            rotation = colmap_utils.qvec2rotmat(im_data.qvec)
-            translation = im_data.tvec.reshape(3, 1)
-            w2c = np.concatenate([rotation, translation], 1)
-            w2c = np.concatenate([w2c, np.array([[0, 0, 0, 1]])], 0)
-            c2w = np.linalg.inv(w2c)
-            # Convert from COLMAP's camera coordinate system (OpenCV) to ours (OpenGL)
-            c2w[0:3, 1:3] *= -1
-            if self.config.assume_colmap_world_coordinate_convention:
-                # world coordinate transform: map colmap gravity guess (-y) to nerfstudio convention (+z)
-                c2w = c2w[np.array([0, 2, 1, 3]), :]
-                c2w[2, :] *= -1
-
-            frame = {
-                "file_path": (self.config.data / self.config.images_path / im_data.name).as_posix(),
-                "transform_matrix": c2w,
-                "colmap_im_id": im_id,
-            }
-            frame.update(cameras[im_data.camera_id])
-            if self.config.masks_path is not None:
-                frame["mask_path"] = (
-                    (self.config.data / self.config.masks_path / im_data.name).with_suffix(".png").as_posix()
-                )
-            if self.config.depths_path is not None:
-                frame["depth_path"] = (
-                    (self.config.data / self.config.depths_path / im_data.name).with_suffix(".png").as_posix()
-                )
-            frames.append(frame)
-            if camera_model is not None:
-                assert camera_model == frame["camera_model"], "Multiple camera models are not supported"
-            else:
-                camera_model = frame["camera_model"]
-
-        out = {}
-        out["frames"] = frames
-        if self.config.assume_colmap_world_coordinate_convention:
-            # world coordinate transform: map colmap gravity guess (-y) to nerfstudio convention (+z)
-            applied_transform = np.eye(4)[:3, :]
-            applied_transform = applied_transform[np.array([0, 2, 1]), :]
-            applied_transform[2, :] *= -1
-            out["applied_transform"] = applied_transform.tolist()
-        out["camera_model"] = camera_model
-        assert len(frames) > 0, "No images found in the colmap model"
-        return out
-
-    def _get_image_indices(self, image_filenames, split):
-        has_split_files_spec = (
-            (self.config.data / "train_list.txt").exists()
-            or (self.config.data / "test_list.txt").exists()
-            or (self.config.data / "validation_list.txt").exists()
-        )
-        if (self.config.data / f"{split}_list.txt").exists():
-            CONSOLE.log(f"Using {split}_list.txt to get indices for split {split}.")
-            with (self.config.data / f"{split}_list.txt").open("r", encoding="utf8") as f:
-                filenames = f.read().splitlines()
-            # Validate split first
-            split_filenames = set(self.config.data / self.config.images_path / x for x in filenames)
-            unmatched_filenames = split_filenames.difference(image_filenames)
-            if unmatched_filenames:
-                raise RuntimeError(
-                    f"Some filenames for split {split} were not found: {set(map(str, unmatched_filenames))}."
-                )
-
-            indices = [i for i, path in enumerate(image_filenames) if path in split_filenames]
-            CONSOLE.log(f"[yellow] Dataset is overriding {split}_indices to {indices}")
-            indices = np.array(indices, dtype=np.int32)
-        elif has_split_files_spec:
-            raise RuntimeError(f"The dataset's list of filenames for split {split} is missing.")
-        else:
-            # find train and eval indices based on the eval_mode specified
-            if self.config.eval_mode == "fraction":
-                i_train, i_eval = get_train_eval_split_fraction(image_filenames, self.config.train_split_fraction)
-            elif self.config.eval_mode == "filename":
-                i_train, i_eval = get_train_eval_split_filename(image_filenames)
-            elif self.config.eval_mode == "interval":
-                i_train, i_eval = get_train_eval_split_interval(image_filenames, self.config.eval_interval)
-            elif self.config.eval_mode == "all":
-                CONSOLE.log(
-                    "[yellow] Be careful with '--eval-mode=all'. If using camera optimization, the cameras may diverge in the current implementation, giving unpredictable results."
-                )
-                i_train, i_eval = get_train_eval_split_all(image_filenames)
-            else:
-                raise ValueError(f"Unknown eval mode {self.config.eval_mode}")
-
-            if split == "train":
-                indices = i_train
-            elif split in ["val", "test"]:
-                indices = i_eval
-            else:
-                raise ValueError(f"Unknown dataparser split {split}")
-        return indices
-
-    def _generate_dataparser_outputs(self, split: str = "train", **kwargs):
-        assert self.config.data.exists(), f"Data directory {self.config.data} does not exist."
-        colmap_path = self.config.data / self.config.colmap_path
-        assert colmap_path.exists(), f"Colmap path {colmap_path} does not exist."
-
-        meta = self._get_all_images_and_cameras(colmap_path)
-        camera_type = CAMERA_MODEL_TO_TYPE[meta["camera_model"]]
-
-        image_filenames = []
-        mask_filenames = []
-        depth_filenames = []
-        poses = []
-
-        fx = []
-        fy = []
-        cx = []
-        cy = []
-        height = []
-        width = []
-        distort = []
-
-        for frame in meta["frames"]:
-            fx.append(float(frame["fl_x"]))
-            fy.append(float(frame["fl_y"]))
-            cx.append(float(frame["cx"]))
-            cy.append(float(frame["cy"]))
-            height.append(int(frame["h"]))
-            width.append(int(frame["w"]))
-            distort.append(
-                camera_utils.get_distortion_params(
-                    k1=float(frame["k1"]) if "k1" in frame else 0.0,
-                    k2=float(frame["k2"]) if "k2" in frame else 0.0,
-                    k3=float(frame["k3"]) if "k3" in frame else 0.0,
-                    k4=float(frame["k4"]) if "k4" in frame else 0.0,
-                    p1=float(frame["p1"]) if "p1" in frame else 0.0,
-                    p2=float(frame["p2"]) if "p2" in frame else 0.0,
-                )
-            )
-
-            image_filenames.append(Path(frame["file_path"]))
-            poses.append(frame["transform_matrix"])
-            if "mask_path" in frame:
-                mask_filenames.append(Path(frame["mask_path"]))
-            if "depth_path" in frame:
-                depth_filenames.append(Path(frame["depth_path"]))
-
-        assert len(mask_filenames) == 0 or (len(mask_filenames) == len(image_filenames)), """
-        Different number of image and mask filenames.
-        You should check that mask_path is specified for every frame (or zero frames) in transforms.json.
-        """
-        assert len(depth_filenames) == 0 or (len(depth_filenames) == len(image_filenames)), """
-        Different number of image and depth filenames.
-        You should check that depth_file_path is specified for every frame (or zero frames) in transforms.json.
-        """
-        poses = torch.from_numpy(np.array(poses).astype(np.float32))
-        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
-            poses,
-            method=self.config.orientation_method,
-            center_method=self.config.center_method,
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
-        scale_factor *= self.config.scale_factor
-        poses[:, :3, 3] *= scale_factor
-
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        indices = self._get_image_indices(image_filenames, split)
-        image_filenames, mask_filenames, depth_filenames, downscale_factor = self._setup_downscale_factor(
-            image_filenames, mask_filenames, depth_filenames
-        )
-
-        image_filenames = [image_filenames[i] for i in indices]
-        mask_filenames = [mask_filenames[i] for i in indices] if len(mask_filenames) > 0 else []
-        depth_filenames = [depth_filenames[i] for i in indices] if len(depth_filenames) > 0 else []
-
-        idx_tensor = torch.tensor(indices, dtype=torch.long)
-        poses = poses[idx_tensor]
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        fx = torch.tensor(fx, dtype=torch.float32)[idx_tensor]
-        fy = torch.tensor(fy, dtype=torch.float32)[idx_tensor]
-        cx = torch.tensor(cx, dtype=torch.float32)[idx_tensor]
-        cy = torch.tensor(cy, dtype=torch.float32)[idx_tensor]
-        height = torch.tensor(height, dtype=torch.int32)[idx_tensor]
-        width = torch.tensor(width, dtype=torch.int32)[idx_tensor]
-        distortion_params = torch.stack(distort, dim=0)[idx_tensor]
-
-        cameras = Cameras(
-            fx=fx,
-            fy=fy,
-            cx=cx,
-            cy=cy,
-            distortion_params=distortion_params,
-            height=height,
-            width=width,
-            camera_to_worlds=poses[:, :3, :4],
-            camera_type=camera_type,
-        )
-
-        cameras.rescale_output_resolution(scaling_factor=1.0 / downscale_factor)
-
-        if "applied_transform" in meta:
-            applied_transform = torch.tensor(meta["applied_transform"], dtype=transform_matrix.dtype)
-            transform_matrix = transform_matrix @ torch.cat(
-                [applied_transform, torch.tensor([[0, 0, 0, 1]], dtype=transform_matrix.dtype)], 0
-            )
-        if "applied_scale" in meta:
-            applied_scale = float(meta["applied_scale"])
-            scale_factor *= applied_scale
-
-        metadata = {}
-        if self.config.load_3D_points:
-            # Load 3D points
-            metadata.update(self._load_3D_points(colmap_path, transform_matrix, scale_factor))
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
-            dataparser_scale=scale_factor,
-            dataparser_transform=transform_matrix,
-            metadata={
-                "depth_filenames": depth_filenames if len(depth_filenames) > 0 else None,
-                "depth_unit_scale_factor": self.config.depth_unit_scale_factor,
-                **metadata,
-            },
-        )
-        return dataparser_outputs
-
-    def _load_3D_points(self, colmap_path: Path, transform_matrix: torch.Tensor, scale_factor: float):
-        if (colmap_path / "points3D.bin").exists():
-            colmap_points = colmap_utils.read_points3D_binary(colmap_path / "points3D.bin")
-        elif (colmap_path / "points3D.txt").exists():
-            colmap_points = colmap_utils.read_points3D_text(colmap_path / "points3D.txt")
-        else:
-            raise ValueError(f"Could not find points3D.txt or points3D.bin in {colmap_path}")
-        points3D = torch.from_numpy(np.array([p.xyz for p in colmap_points.values()], dtype=np.float32))
-        points3D = (
-            torch.cat(
-                (
-                    points3D,
-                    torch.ones_like(points3D[..., :1]),
-                ),
-                -1,
-            )
-            @ transform_matrix.T
-        )
-        points3D *= scale_factor
-
-        # Load point colours
-        points3D_rgb = torch.from_numpy(np.array([p.rgb for p in colmap_points.values()], dtype=np.uint8))
-        points3D_num_points = torch.tensor([len(p.image_ids) for p in colmap_points.values()], dtype=torch.int64)
-        out = {
-            "points3D_xyz": points3D,
-            "points3D_rgb": points3D_rgb,
-            "points3D_error": torch.from_numpy(np.array([p.error for p in colmap_points.values()], dtype=np.float32)),
-            "points3D_num_points2D": points3D_num_points,
-        }
-        if self.config.max_2D_matches_per_3D_point != 0:
-            if (colmap_path / "images.txt").exists():
-                im_id_to_image = colmap_utils.read_images_text(colmap_path / "images.txt")
-            elif (colmap_path / "images.bin").exists():
-                im_id_to_image = colmap_utils.read_images_binary(colmap_path / "images.bin")
-            else:
-                raise ValueError(f"Could not find images.txt or images.bin in {colmap_path}")
-            downscale_factor = self._downscale_factor
-            max_num_points = int(torch.max(points3D_num_points).item())
-            if self.config.max_2D_matches_per_3D_point > 0:
-                max_num_points = min(max_num_points, self.config.max_2D_matches_per_3D_point)
-            points3D_image_ids = []
-            points3D_image_xy = []
-            for p in colmap_points.values():
-                nids = np.array(p.image_ids, dtype=np.int64)
-                nxy_ids = np.array(p.point2D_idxs, dtype=np.int32)
-                if self.config.max_2D_matches_per_3D_point != -1:
-                    # Randomly sample 2D matches
-                    idxs = np.argsort(p.error)[: self.config.max_2D_matches_per_3D_point]
-                    nids = nids[idxs]
-                    nxy_ids = nxy_ids[idxs]
-                nxy = [im_id_to_image[im_id].xys[pt_idx] for im_id, pt_idx in zip(nids, nxy_ids)]
-                nxy = torch.from_numpy(np.stack(nxy).astype(np.float32))
-                nids = torch.from_numpy(nids)
-                assert len(nids.shape) == 1
-                assert len(nxy.shape) == 2
-                points3D_image_ids.append(
-                    torch.cat((nids, torch.full((max_num_points - len(nids),), -1, dtype=torch.int64)))
-                )
-                points3D_image_xy.append(
-                    torch.cat((nxy, torch.full((max_num_points - len(nxy), nxy.shape[-1]), 0, dtype=torch.float32)))
-                    / downscale_factor
-                )
-            out["points3D_image_ids"] = torch.stack(points3D_image_ids, dim=0)
-            out["points3D_points2D_xy"] = torch.stack(points3D_image_xy, dim=0)
-        return out
-
-    def _downscale_images(self, paths, get_fname, downscale_factor: int, nearest_neighbor: bool = False):
-        with status(msg="[bold yellow]Downscaling images...", spinner="growVertical"):
-            assert downscale_factor > 1
-            assert isinstance(downscale_factor, int)
-            # Using %05d ffmpeg commands appears to be unreliable (skips images).
-            for path in paths:
-                nn_flag = "" if not nearest_neighbor else ":flags=neighbor"
-                path_out = get_fname(path)
-                path_out.parent.mkdir(parents=True, exist_ok=True)
-                ffmpeg_cmd = [
-                    f'ffmpeg -y -noautorotate -i "{path}" ',
-                    f"-q:v 2 -vf scale=iw/{downscale_factor}:ih/{downscale_factor}{nn_flag} ",
-                    f'"{path_out}"',
-                ]
-                ffmpeg_cmd = " ".join(ffmpeg_cmd)
-                run_command(ffmpeg_cmd)
-
-        CONSOLE.log("[bold green]:tada: Done downscaling images.")
-
-    def _setup_downscale_factor(
-        self, image_filenames: List[Path], mask_filenames: List[Path], depth_filenames: List[Path]
-    ):
-        """
-        Setup the downscale factor for the dataset. This is used to downscale the images and cameras.
-        """
-
-        def get_fname(parent: Path, filepath: Path) -> Path:
-            """Returns transformed file name when downscale factor is applied"""
-            rel_part = filepath.relative_to(parent)
-            base_part = parent.parent / (str(parent.name) + f"_{self._downscale_factor}")
-            return base_part / rel_part
-
-        filepath = next(iter(image_filenames))
-        if self._downscale_factor is None:
-            if self.config.downscale_factor is None:
-                test_img = Image.open(filepath)
-                h, w = test_img.size
-                max_res = max(h, w)
-                df = 0
-                while True:
-                    if (max_res / 2 ** (df)) <= MAX_AUTO_RESOLUTION:
-                        break
-                    df += 1
-
-                self._downscale_factor = 2**df
-                CONSOLE.log(f"Using image downscale factor of {self._downscale_factor}")
-            else:
-                self._downscale_factor = self.config.downscale_factor
-            if self._downscale_factor > 1 and not all(
-                get_fname(self.config.data / self.config.images_path, fp).parent.exists() for fp in image_filenames
-            ):
-                # Downscaled images not found
-                # Ask if user wants to downscale the images automatically here
-                CONSOLE.print(
-                    f"[bold red]Downscaled images do not exist for factor of {self._downscale_factor}.[/bold red]"
-                )
-                if Confirm.ask("\nWould you like to downscale the images now?", default=False, console=CONSOLE):
-                    # Install the method
-                    self._downscale_images(
-                        image_filenames,
-                        partial(get_fname, self.config.data / self.config.images_path),
-                        self._downscale_factor,
-                        nearest_neighbor=False,
-                    )
-                    if len(mask_filenames) > 0:
-                        assert self.config.masks_path is not None
-                        self._downscale_images(
-                            mask_filenames,
-                            partial(get_fname, self.config.data / self.config.masks_path),
-                            self._downscale_factor,
-                            nearest_neighbor=True,
-                        )
-                    if len(depth_filenames) > 0:
-                        assert self.config.depths_path is not None
-                        self._downscale_images(
-                            depth_filenames,
-                            partial(get_fname, self.config.data / self.config.depths_path),
-                            self._downscale_factor,
-                            nearest_neighbor=True,
-                        )
-                else:
-                    sys.exit(1)
-
-        # Return transformed filenames
-        if self._downscale_factor > 1:
-            image_filenames = [get_fname(self.config.data / self.config.images_path, fp) for fp in image_filenames]
-            if len(mask_filenames) > 0:
-                assert self.config.masks_path is not None
-                mask_filenames = [get_fname(self.config.data / self.config.masks_path, fp) for fp in mask_filenames]
-            if len(depth_filenames) > 0:
-                assert self.config.depths_path is not None
-                depth_filenames = [get_fname(self.config.data / self.config.depths_path, fp) for fp in depth_filenames]
-        assert isinstance(self._downscale_factor, int)
-        return image_filenames, mask_filenames, depth_filenames, self._downscale_factor
diff --git a/nerfstudio/data/dataparsers/dnerf_dataparser.py b/nerfstudio/data/dataparsers/dnerf_dataparser.py
deleted file mode 100644
index 478b1fc3..00000000
--- a/nerfstudio/data/dataparsers/dnerf_dataparser.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for blender dataset"""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Type
-
-import imageio
-import numpy as np
-import torch
-
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.colors import get_color
-from nerfstudio.utils.io import load_from_json
-
-
-@dataclass
-class DNeRFDataParserConfig(DataParserConfig):
-    """D-NeRF dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: DNeRF)
-    """target class to instantiate"""
-    data: Path = Path("data/dnerf/lego")
-    """Directory specifying location of data."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    alpha_color: str = "white"
-    """alpha color of background"""
-
-
-@dataclass
-class DNeRF(DataParser):
-    """DNeRF Dataset"""
-
-    config: DNeRFDataParserConfig
-    includes_time: bool = True
-
-    def __init__(self, config: DNeRFDataParserConfig):
-        super().__init__(config=config)
-        self.data: Path = config.data
-        self.scale_factor: float = config.scale_factor
-        self.alpha_color = config.alpha_color
-
-    def _generate_dataparser_outputs(self, split="train"):
-        if self.alpha_color is not None:
-            alpha_color_tensor = get_color(self.alpha_color)
-        else:
-            alpha_color_tensor = None
-
-        meta = load_from_json(self.data / f"transforms_{split}.json")
-        image_filenames = []
-        poses = []
-        times = []
-        for frame in meta["frames"]:
-            fname = self.data / Path(frame["file_path"].replace("./", "") + ".png")
-            image_filenames.append(fname)
-            poses.append(np.array(frame["transform_matrix"]))
-            times.append(frame["time"])
-        poses = np.array(poses).astype(np.float32)
-        times = torch.tensor(times, dtype=torch.float32)
-
-        img_0 = imageio.imread(image_filenames[0])
-        image_height, image_width = img_0.shape[:2]
-        camera_angle_x = float(meta["camera_angle_x"])
-        focal_length = 0.5 * image_width / np.tan(0.5 * camera_angle_x)
-
-        cx = image_width / 2.0
-        cy = image_height / 2.0
-        camera_to_world = torch.from_numpy(poses[:, :3])  # camera to world transform
-
-        # in x,y,z order
-        camera_to_world[..., 3] *= self.scale_factor
-        scene_box = SceneBox(aabb=torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]], dtype=torch.float32))
-
-        cameras = Cameras(
-            camera_to_worlds=camera_to_world,
-            fx=focal_length,
-            fy=focal_length,
-            cx=cx,
-            cy=cy,
-            camera_type=CameraType.PERSPECTIVE,
-            times=times,
-        )
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            alpha_color=alpha_color_tensor,
-            scene_box=scene_box,
-            dataparser_scale=self.scale_factor,
-        )
-
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/dycheck_dataparser.py b/nerfstudio/data/dataparsers/dycheck_dataparser.py
deleted file mode 100644
index 90f5e9e9..00000000
--- a/nerfstudio/data/dataparsers/dycheck_dataparser.py
+++ /dev/null
@@ -1,340 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for DyCheck (https://arxiv.org/abs/2210.13445) dataset of `iphone` subset"""
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Dict, List, Tuple, Type
-
-import cv2
-import numpy as np
-import torch
-
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.colors import get_color
-from nerfstudio.utils.io import load_from_json
-from nerfstudio.utils.rich_utils import CONSOLE
-
-
-def downscale(img, scale: int) -> np.ndarray:
-    """Function from DyCheck's repo. Downscale an image.
-
-    Args:
-        img: Input image
-        scale: Factor of the scale
-
-    Returns:
-        New image
-    """
-    if scale == 1:
-        return img
-    height, width = img.shape[:2]
-    if height % scale > 0 or width % scale > 0:
-        raise ValueError(f"Image shape ({height},{width}) must be divisible by the" f" scale ({scale}).")
-    out_height, out_width = height // scale, width // scale
-    resized = cv2.resize(img, (out_width, out_height), cv2.INTER_AREA)  # type: ignore
-    return resized
-
-
-def upscale(img, scale: int) -> np.ndarray:
-    """Function from DyCheck's repo. Upscale an image.
-
-    Args:
-        img: Input image
-        scale: Factor of the scale
-
-    Returns:
-        New image
-    """
-    if scale == 1:
-        return img
-    height, width = img.shape[:2]
-    out_height, out_width = height * scale, width * scale
-    resized = cv2.resize(img, (out_width, out_height), cv2.INTER_AREA)  # type: ignore
-    return resized
-
-
-def rescale(img, scale_factor: float, interpolation: int = cv2.INTER_AREA) -> np.ndarray:
-    """Function from DyCheck's repo. Rescale an image.
-
-    Args:
-        img: Input image
-        scale: Factor of the scale
-        interpolation: Interpolation method in opencv
-
-    Returns:
-        New image
-    """
-    scale_factor = float(scale_factor)
-    if scale_factor <= 0.0:
-        raise ValueError("scale_factor must be a non-negative number.")
-    if scale_factor == 1.0:
-        return img
-
-    height, width = img.shape[:2]
-    if scale_factor.is_integer():
-        return upscale(img, int(scale_factor))
-
-    inv_scale = 1.0 / scale_factor
-    if inv_scale.is_integer() and (scale_factor * height).is_integer() and (scale_factor * width).is_integer():
-        return downscale(img, int(inv_scale))
-
-    print(f"Resizing image by non-integer factor {scale_factor}, this may lead to artifacts.")
-    height, width = img.shape[:2]
-    out_height = math.ceil(height * scale_factor)
-    out_height -= out_height % 2
-    out_width = math.ceil(width * scale_factor)
-    out_width -= out_width % 2
-
-    return cv2.resize(img, (out_width, out_height), interpolation)  # type: ignore
-
-
-def _load_scene_info(data_dir: Path) -> Tuple[np.ndarray, float, float, float]:
-    """Function from DyCheck's repo. Load scene info from json.
-
-    Args:
-        data_dir: data path
-
-    Returns:
-        A tuple of scene info: center, scale, near, far
-    """
-    scene_dict = load_from_json(data_dir / "scene.json")
-    center = np.array(scene_dict["center"], dtype=np.float32)
-    scale = scene_dict["scale"]
-    near = scene_dict["near"]
-    far = scene_dict["far"]
-    return center, scale, near, far
-
-
-def _load_metadata_info(data_dir: Path) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Function from DyCheck's repo. Load scene metadata from json.
-
-    Args:
-        data_dir: data path
-
-    Returns:
-        A tuple of scene info: frame_names_map, time_ids, camera_ids
-    """
-    dataset_dict = load_from_json(data_dir / "dataset.json")
-    _frame_names = np.array(dataset_dict["ids"])
-
-    metadata_dict = load_from_json(data_dir / "metadata.json")
-    time_ids = np.array([metadata_dict[k]["warp_id"] for k in _frame_names], dtype=np.uint32)
-    camera_ids = np.array([metadata_dict[k]["camera_id"] for k in _frame_names], dtype=np.uint32)
-
-    frame_names_map = np.zeros((time_ids.max() + 1, camera_ids.max() + 1), _frame_names.dtype)
-    for i, (t, c) in enumerate(zip(time_ids, camera_ids)):
-        frame_names_map[t, c] = _frame_names[i]
-
-    return frame_names_map, time_ids, camera_ids
-
-
-def _rescale_depth(depth_raw: np.ndarray, cam: Dict) -> np.ndarray:
-    """Depth rescale function from DyCheck.
-
-    Args:
-        depth: A numpy ndarray of the raw depth
-        cam: Dict of the camera
-
-    Returns:
-        A numpy ndarray of the processed depth
-    """
-    xx, yy = np.meshgrid(np.arange(cam["width"], dtype=np.float32), np.arange(cam["height"], dtype=np.float32))
-    pixels = np.stack([xx, yy], axis=-1)
-    batch_shape = pixels.shape[:-1]
-    pixels = np.reshape(pixels, (-1, 2))
-    y = (pixels[..., 1] - cam["cy"]) / cam["fy"]
-    x = (pixels[..., 0] - cam["cx"]) / cam["fx"]
-    # x = (pixels[..., 0] - self.principal_point_x - y * self.skew) / self.scale_factor_x
-    # assume skew = 0
-    viewdirs = np.stack([x, y, np.ones_like(x)], axis=-1)
-    local_viewdirs = viewdirs / np.linalg.norm(viewdirs, axis=-1, keepdims=True)
-    viewdirs = (cam["camera_to_worlds"][:3, :3] @ local_viewdirs[..., None])[..., 0]
-    viewdirs /= np.linalg.norm(viewdirs, axis=-1, keepdims=True)
-    viewdirs = viewdirs.reshape((*batch_shape, 3))
-    cosa = viewdirs @ (cam["camera_to_worlds"][:, 2])
-    if depth_raw.ndim == cosa.ndim:
-        depth = depth_raw[..., None] / cosa[..., None]
-    else:
-        depth = depth_raw / cosa[..., None]
-    return depth
-
-
-@dataclass
-class DycheckDataParserConfig(DataParserConfig):
-    """Dycheck (https://arxiv.org/abs/2210.13445) dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: Dycheck)
-    """target class to instantiate"""
-    data: Path = Path("data/iphone/mochi-high-five")
-    """Directory specifying location of data."""
-    scale_factor: float = 5.0
-    """How much to scale the camera origins by."""
-    alpha_color: str = "white"
-    """alpha color of background"""
-    downscale_factor: int = 1
-    """How much to downscale images."""
-    scene_box_bound: float = 1.5
-    """Boundary of scene box."""
-
-
-@dataclass
-class Dycheck(DataParser):
-    """Dycheck (https://arxiv.org/abs/2210.13445) Dataset `iphone` subset"""
-
-    config: DycheckDataParserConfig
-    includes_time: bool = True
-
-    def __init__(self, config: DycheckDataParserConfig):
-        super().__init__(config=config)
-        self.data: Path = config.data
-        self.scale_factor: float = config.scale_factor
-        self.alpha_color = config.alpha_color
-        # load extra info from "extra.json"
-        extra_path = self.data / "extra.json"
-        extra_dict = load_from_json(extra_path)
-        self._factor = extra_dict["factor"]
-        self._fps = extra_dict["fps"]
-        self._bbox = np.array(extra_dict["bbox"], dtype=np.float32)
-        self._lookat = np.array(extra_dict["lookat"], dtype=np.float32)
-        self._up = np.array(extra_dict["up"], dtype=np.float32)
-        self._center, self._scale, self._near, self._far = _load_scene_info(self.data)
-        self._frame_names_map, self._time_ids, self._camera_ids = _load_metadata_info(self.data)
-
-    def _generate_dataparser_outputs(self, split="train"):
-        if self.alpha_color is not None:
-            alpha_color_tensor = get_color(self.alpha_color)
-        else:
-            alpha_color_tensor = None
-        splits_dir = self.data / "splits"
-
-        # scale the scene to fill the aabb bbox
-        sf = self.config.scene_box_bound / 4 / (self._scale * self._far)
-        # CONSOLE.print(f"scale factor changed from {self.config.scale_factor} to {sf}")
-        self.config.scale_factor = sf
-
-        if not (splits_dir / f"{split}.json").exists():
-            CONSOLE.print(f"split {split} not found, using split train")
-            split = "train"
-        split_dict = load_from_json(splits_dir / f"{split}.json")
-        frame_names = np.array(split_dict["frame_names"])
-        time_ids = np.array(split_dict["time_ids"])
-        if split != "train":
-            CONSOLE.print(f"split {split} is empty, using the 1st training image")
-            split_dict = load_from_json(splits_dir / "train.json")
-            frame_names = np.array(split_dict["frame_names"])[[0]]
-            time_ids = np.array(split_dict["time_ids"])[[0]]
-
-        image_filenames, depth_filenames, cams = self.process_frames(frame_names.tolist(), time_ids)
-
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-self.config.scene_box_bound] * 3, [self.config.scene_box_bound] * 3], dtype=torch.float32
-            )
-        )
-        cam_dict = {}
-        for k in cams[0].keys():
-            cam_dict[k] = torch.stack([torch.as_tensor(c[k]) for c in cams], dim=0)
-        cameras = Cameras(camera_type=CameraType.PERSPECTIVE, **cam_dict)
-
-        scale = self._scale * self.config.scale_factor
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            alpha_color=alpha_color_tensor,
-            scene_box=scene_box,
-            metadata={
-                "depth_filenames": depth_filenames,
-                "depth_unit_scale_factor": scale,
-                "scale": scale,
-                "near": self._near * scale,
-                "far": self._far * scale,
-            },
-        )
-
-        return dataparser_outputs
-
-    def process_frames(self, frame_names: List[str], time_ids: np.ndarray) -> Tuple[List, List, List]:
-        """Read cameras and filenames from the name list.
-
-        Args:
-            frame_names: list of file names.
-            time_ids: time id of each frame.
-
-        Returns:
-            A list of camera, each entry is a dict of the camera.
-        """
-        image_filenames, depth_filenames = [], []
-        cams = []
-        for idx, frame in enumerate(frame_names):
-            image_filenames.append(self.data / f"rgb/{self.config.downscale_factor}x/{frame}.png")
-            depth_filenames.append(self.data / f"processed_depth/{self.config.downscale_factor}x/{frame}.npy")
-            cam_json = load_from_json(self.data / f"camera/{frame}.json")
-            c2w = torch.as_tensor(cam_json["orientation"]).T
-            position = torch.as_tensor(cam_json["position"])
-            position -= self._center  # some scenes look weird (wheel)
-            position *= self._scale * self.config.scale_factor
-            pose = torch.zeros([3, 4])
-            pose[:3, :3] = c2w
-            pose[:3, 3] = position
-            # from opencv coord to opengl coord (used by nerfstudio)
-            pose[0:3, 1:3] *= -1  # switch cam coord x,y
-            pose = pose[[1, 0, 2], :]  # switch world x,y
-            pose[2, :] *= -1  # invert world z
-            # for aabb bbox usage
-            pose = pose[[1, 2, 0], :]  # switch world xyz to zxy
-            cams.append(
-                {
-                    "camera_to_worlds": pose,
-                    "fx": cam_json["focal_length"] / self.config.downscale_factor,
-                    "fy": cam_json["focal_length"] * cam_json["pixel_aspect_ratio"] / self.config.downscale_factor,
-                    "cx": cam_json["principal_point"][0] / self.config.downscale_factor,
-                    "cy": cam_json["principal_point"][1] / self.config.downscale_factor,
-                    "height": cam_json["image_size"][1] // self.config.downscale_factor,
-                    "width": cam_json["image_size"][0] // self.config.downscale_factor,
-                    "times": torch.as_tensor(time_ids[idx] / self._time_ids.max()).float(),
-                }
-            )
-
-        d = self.config.downscale_factor
-        if not image_filenames[0].exists():
-            CONSOLE.print(f"downscale factor {d}x not exist, converting")
-            ori_h, ori_w = cv2.imread(str(self.data / f"rgb/1x/{frame_names[0]}.png")).shape[:2]
-            (self.data / f"rgb/{d}x").mkdir(exist_ok=True)
-            h, w = ori_h // d, ori_w // d
-            for frame in frame_names:
-                cv2.imwrite(
-                    str(self.data / f"rgb/{d}x/{frame}.png"),
-                    cv2.resize(cv2.imread(str(self.data / f"rgb/1x/{frame}.png")), (w, h)),
-                )
-            CONSOLE.print("finished")
-
-        if not depth_filenames[0].exists():
-            CONSOLE.print(f"processed depth downscale factor {d}x not exist, converting")
-            (self.data / f"processed_depth/{d}x").mkdir(exist_ok=True, parents=True)
-            for idx, frame in enumerate(frame_names):
-                depth = np.load(self.data / f"depth/1x/{frame}.npy")
-                mask = rescale((depth != 0).astype(np.uint8) * 255, 1 / d, cv2.INTER_AREA)
-                depth = rescale(depth, 1 / d, cv2.INTER_AREA)
-                depth[mask != 255] = 0
-                depth = _rescale_depth(depth, cams[idx])
-                np.save(str(self.data / f"processed_depth/{d}x/{frame}.npy"), depth)
-            CONSOLE.print("finished")
-
-        return image_filenames, depth_filenames, cams
diff --git a/nerfstudio/data/dataparsers/instant_ngp_dataparser.py b/nerfstudio/data/dataparsers/instant_ngp_dataparser.py
deleted file mode 100644
index 86780dcc..00000000
--- a/nerfstudio/data/dataparsers/instant_ngp_dataparser.py
+++ /dev/null
@@ -1,228 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for instant ngp data"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Dict, Literal, Tuple, Type
-
-import imageio
-import numpy as np
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.data.utils.dataparsers_utils import (
-    get_train_eval_split_all,
-    get_train_eval_split_filename,
-    get_train_eval_split_fraction,
-    get_train_eval_split_interval,
-)
-from nerfstudio.utils.io import load_from_json
-from nerfstudio.utils.rich_utils import CONSOLE
-
-
-@dataclass
-class InstantNGPDataParserConfig(DataParserConfig):
-    """Instant-NGP dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: InstantNGP)
-    """target class to instantiate"""
-    data: Path = Path("data/ours/posterv2")
-    """Directory or explicit json file path specifying location of data."""
-    scene_scale: float = 0.3333
-    """How much to scale the scene."""
-    eval_mode: Literal["fraction", "filename", "interval", "all"] = "fraction"
-    """
-    The method to use for splitting the dataset into train and eval.
-    Fraction splits based on a percentage for train and the remaining for eval.
-    Filename splits based on filenames containing train/eval.
-    Interval uses every nth frame for eval.
-    All uses all the images for any split.
-    """
-    train_split_fraction: float = 0.9
-    """The percentage of the dataset to use for training. Only used when eval_mode is train-split-fraction."""
-    eval_interval: int = 8
-    """The interval between frames to use for eval. Only used when eval_mode is eval-interval."""
-
-
-@dataclass
-class InstantNGP(DataParser):
-    """Instant NGP Dataset"""
-
-    config: InstantNGPDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        if self.config.data.suffix == ".json":
-            meta = load_from_json(self.config.data)
-            data_dir = self.config.data.parent
-        else:
-            meta = load_from_json(self.config.data / "transforms.json")
-            data_dir = self.config.data
-
-        image_filenames = []
-        mask_filenames = []
-        poses = []
-        num_skipped_image_filenames = 0
-        for frame in meta["frames"]:
-            fname = data_dir / Path(frame["file_path"])
-            # search for png file
-            if not fname.exists():
-                fname = data_dir / Path(frame["file_path"] + ".png")
-            if not fname.exists():
-                CONSOLE.log(f"couldn't find {fname} image")
-                num_skipped_image_filenames += 1
-            else:
-                if "w" not in meta:
-                    img_0 = imageio.imread(fname)
-                    h, w = img_0.shape[:2]
-                    meta["w"] = w
-                    if "h" in meta:
-                        meta_h = meta["h"]
-                        assert meta_h == h, f"height of image dont not correspond metadata {h} != {meta_h}"
-                    else:
-                        meta["h"] = h
-                image_filenames.append(fname)
-                poses.append(np.array(frame["transform_matrix"]))
-                if "mask_path" in frame:
-                    mask_fname = data_dir / Path(frame["mask_path"])
-                    mask_filenames.append(mask_fname)
-        if num_skipped_image_filenames >= 0:
-            CONSOLE.print(f"Skipping {num_skipped_image_filenames} files in dataset split {split}.")
-        assert len(image_filenames) != 0, """
-        No image files found.
-        You should check the file_paths in the transforms.json file to make sure they are correct.
-        """
-        poses = np.array(poses).astype(np.float32)
-        poses[:, :3, 3] *= self.config.scene_scale
-
-        # find train and eval indices based on the eval_mode specified
-        if self.config.eval_mode == "fraction":
-            i_train, i_eval = get_train_eval_split_fraction(image_filenames, self.config.train_split_fraction)
-        elif self.config.eval_mode == "filename":
-            i_train, i_eval = get_train_eval_split_filename(image_filenames)
-        elif self.config.eval_mode == "interval":
-            i_train, i_eval = get_train_eval_split_interval(image_filenames, self.config.eval_interval)
-        elif self.config.eval_mode == "all":
-            CONSOLE.log(
-                "[yellow] Be careful with '--eval-mode=all'. If using camera optimization, the cameras may diverge in the current implementation, giving unpredictable results."
-            )
-            i_train, i_eval = get_train_eval_split_all(image_filenames)
-        else:
-            raise ValueError(f"Unknown eval mode {self.config.eval_mode}")
-
-        if split == "train":
-            indices = i_train
-        elif split in ["val", "test"]:
-            indices = i_eval
-        else:
-            raise ValueError(f"Unknown dataparser split {split}")
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        image_filenames = [image_filenames[i] for i in indices]
-        mask_filenames = [mask_filenames[i] for i in indices] if len(mask_filenames) > 0 else []
-
-        idx_tensor = torch.tensor(indices, dtype=torch.long)
-        poses = poses[idx_tensor]
-
-        camera_to_world = torch.from_numpy(poses[:, :3])  # camera to world transform
-
-        distortion_params = camera_utils.get_distortion_params(
-            k1=float(meta.get("k1", 0)),
-            k2=float(meta.get("k2", 0)),
-            k3=float(meta.get("k3", 0)),
-            k4=float(meta.get("k4", 0)),
-            p1=float(meta.get("p1", 0)),
-            p2=float(meta.get("p2", 0)),
-        )
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = 0.5 * meta.get("aabb_scale", 1)
-
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        fl_x, fl_y = InstantNGP.get_focal_lengths(meta)
-
-        w, h = meta["w"], meta["h"]
-
-        camera_type = CameraType.PERSPECTIVE
-        if meta.get("is_fisheye", False):
-            camera_type = CameraType.FISHEYE
-
-        cameras = Cameras(
-            fx=float(fl_x),
-            fy=float(fl_y),
-            cx=float(meta.get("cx", 0.5 * w)),
-            cy=float(meta.get("cy", 0.5 * h)),
-            distortion_params=distortion_params,
-            height=int(h),
-            width=int(w),
-            camera_to_worlds=camera_to_world,
-            camera_type=camera_type,
-        )
-
-        # TODO(ethan): add alpha background color
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
-            dataparser_scale=self.config.scene_scale,
-        )
-
-        return dataparser_outputs
-
-    @classmethod
-    def get_focal_lengths(cls, meta: Dict) -> Tuple[float, float]:
-        """Reads or computes the focal length from transforms dict.
-        Args:
-            meta: metadata from transforms.json file.
-        Returns:
-            Focal lengths in the x and y directions. Error is raised if these cannot be calculated.
-        """
-        fl_x, fl_y = 0, 0
-
-        def fov_to_focal_length(rad, res):
-            return 0.5 * res / np.tan(0.5 * rad)
-
-        if "fl_x" in meta:
-            fl_x = meta["fl_x"]
-        elif "x_fov" in meta:
-            fl_x = fov_to_focal_length(np.deg2rad(meta["x_fov"]), meta["w"])
-        elif "camera_angle_x" in meta:
-            fl_x = fov_to_focal_length(meta["camera_angle_x"], meta["w"])
-
-        if "camera_angle_y" not in meta or "y_fov" not in meta:
-            fl_y = fl_x
-        else:
-            if "fl_y" in meta:
-                fl_y = meta["fl_y"]
-            elif "y_fov" in meta:
-                fl_y = fov_to_focal_length(np.deg2rad(meta["y_fov"]), meta["h"])
-            elif "camera_angle_y" in meta:
-                fl_y = fov_to_focal_length(meta["camera_angle_y"], meta["h"])
-
-        if fl_x == 0 or fl_y == 0:
-            raise AttributeError("Focal length cannot be calculated from transforms.json (missing fields).")
-
-        return (fl_x, fl_y)
diff --git a/nerfstudio/data/dataparsers/kittimot_dataparser.py b/nerfstudio/data/dataparsers/kittimot_dataparser.py
new file mode 100644
index 00000000..ac7520af
--- /dev/null
+++ b/nerfstudio/data/dataparsers/kittimot_dataparser.py
@@ -0,0 +1,588 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Data parser for the KITTI MOT dataset"""
+from collections import defaultdict
+from dataclasses import dataclass, field
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Tuple, Type
+
+import numpy as np
+import numpy.typing as npt
+import torch
+from pyquaternion import Quaternion
+from torch import Tensor
+from typing_extensions import Literal
+
+from nerfstudio.cameras.camera_utils import rotmat_to_unitquat, unitquat_slerp, unitquat_to_rotmat
+from nerfstudio.cameras.cameras import Cameras, CameraType
+from nerfstudio.cameras.lidars import Lidars, LidarType
+from nerfstudio.data.dataparsers.ad_dataparser import OPENCV_TO_NERFSTUDIO, ADDataParser, ADDataParserConfig
+from nerfstudio.utils.poses import to4x4
+
+HORIZONTAL_BEAM_DIVERGENCE = 3.0e-3  # radians, or meters at a distance of 1m
+VERTICAL_BEAM_DIVERGENCE = 1.5e-3  # radians, or meters at a distance of 1m
+MAX_INTENSITY_VALUE = 1.0
+KITTI_IMAGE_WIDTH = 1242
+KITTI_IMAGE_HEIGHT = 375
+
+ALLOWED_CATEGORIES = {
+    "Car",
+    "Van",
+    "Truck",
+    "Cyclist",
+    "Tram",
+}  # skip Misc and DontCare
+SYMMETRIC_CATEGORIES = ALLOWED_CATEGORIES
+
+DEFORMABLE_CATEGORIES = {
+    "Pedestrian",
+    "Person_sitting",
+}
+
+AVAILABLE_CAMERAS = ("image_02", "image_03")
+
+DATA_FREQUENCY = 10.0  # 10 Hz
+LIDAR_ROTATION_TIME = 1.0 / DATA_FREQUENCY  # 10 Hz
+
+MARS_SEQ_TO_START_FRAME = {str(i).zfill(4): 0 for i in range(1, 21)}
+MARS_SEQ_TO_END_FRAME = {str(i).zfill(4): -1 for i in range(1, 21)}
+# MARS only evaluate on the 0006 sequence. Note that the latest number on their github uses
+# frames from 65 -> 120, but the results in their paper uses from frame 5 -> 260, which
+# is what we will use to compare with.
+MARS_SEQ_TO_START_FRAME["0006"] = 5  # taken from the paper
+MARS_SEQ_TO_END_FRAME["0006"] = 260  # taken from the paper
+
+RIGHT_FRONT_UP2RIGHT_DOWN_FRONT = np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
+
+
+@dataclass
+class KittiMotDataParserConfig(ADDataParserConfig):
+    """KITTI MOT DatasetParser config."""
+
+    _target: Type = field(default_factory=lambda: KittiMot)
+    """target class to instantiate"""
+    sequence: str = "0006"
+    """Name of the scene."""
+    data: Path = Path("data/kittimot")
+    """Path to ZOD dataset."""
+    split: Literal["training"] = "training"  # we do not have labels for testing set...
+    """Which split to use."""
+    cameras: Tuple[Literal["image_02", "image_03", "none", "all"], ...] = ("image_02", "image_03")
+    """Which cameras to use."""
+    lidars: Tuple[Literal["velodyne", "none"], ...] = ("velodyne",)
+    """Which lidars to use."""
+    annotation_interval: float = 0.1
+    """Interval between annotations in seconds."""
+    allow_per_point_times: bool = False
+    """Whether to allow per-point timestamps."""
+    compute_sensor_velocities: bool = False
+    """Whether to compute sensor velocities."""
+    min_lidar_dist: Tuple[float, ...] = (2.0, 1.6, 2.0)
+    """Minimum distance of lidar points."""
+    use_mars_nvs_50_split: bool = False
+    """Whether to use the MARS NVS 50 proposed split."""
+    use_sensor_timestamps: bool = True
+    """Whether to use sensor timestamps."""
+
+
+@dataclass
+class KittiMot(ADDataParser):
+    """Zenseact Open Dataset DatasetParser"""
+
+    config: KittiMotDataParserConfig
+
+    @property
+    def actor_transform(self) -> Tensor:
+        """The transform needed to convert the actor poses to our desired format (x-right, y-forward, z-up)."""
+        return torch.from_numpy(RIGHT_FRONT_UP2RIGHT_DOWN_FRONT)
+
+    def _get_cameras(self) -> Tuple[Cameras, List[Path]]:
+        """Returns camera info and image filenames."""
+
+        if "all" in self.config.cameras:
+            self.config.cameras = AVAILABLE_CAMERAS
+
+        filenames, times, poses, idxs = [], [], [], []
+        # we will convert everything to the common camera, hence we use the camera parameters of the common camera
+        fx = self.calibs["P0"][0, 0].copy()
+        fy = self.calibs["P0"][1, 1].copy()
+        cx = self.calibs["P0"][0, 2].copy()
+        cy = self.calibs["P0"][1, 2].copy()
+
+        for camera_idx, cam_name in enumerate(self.config.cameras):
+            camera_folder = self.config.data / self.config.split / cam_name / self.config.sequence
+            camera_files = sorted(camera_folder.glob("*.png"))
+
+            for camera_file in camera_files:
+                image_id = int(camera_file.stem)
+                filenames.append(camera_file)
+                if self.config.use_sensor_timestamps:
+                    times.append(self.timestamp_per_sensor[cam_name][image_id])
+                else:
+                    times.append(image_id / DATA_FREQUENCY)
+                ego_pose = self._get_ego_pose(image_id, sensor=cam_name).double()
+                idxs.append(camera_idx)
+                # get the ego pose
+                velo2cam = torch.from_numpy(self.calibs["Tr_velo_to_cam"].copy()).double()  # 3x4
+                cam2velo = to4x4(velo2cam).inverse()
+                imu2velo = torch.from_numpy(self.calibs["Tr_imu_to_velo"].copy()).double()  # 3x4
+                velo2imu = to4x4(imu2velo).inverse()
+                # cam to imu
+                cam2imu = velo2imu @ cam2velo
+                # consider both the rectification similar to here
+                # https://github.com/autonomousvision/kitti360Scripts/blob/7ecc14eab6fa30e5d2ac71ad37fed2bb4b0b8073/kitti360scripts/helpers/project.py#L35-L41
+                rect_inv = torch.eye(4, dtype=torch.double)
+                rect_inv[:3, :3] = torch.from_numpy(self.calibs["R_rect"].copy()).inverse().double()
+
+                # get the calib for the current camera
+                camera_int = int(cam_name.split("_")[1])
+                calib = self.calibs[f"P{camera_int}"].copy()
+                # consider that the cameras are rectified wrt to the common camera
+
+                cam2commoncam = (
+                    to4x4(torch.from_numpy(calib).double()).inverse()
+                    @ to4x4(torch.from_numpy(self.calibs["P0"].copy())).double()
+                )
+                cam_pose = ego_pose @ cam2imu @ rect_inv @ cam2commoncam
+                # consider the nerfstudio to opencv conversion
+                cam_pose[:3, :3] = cam_pose[:3, :3] @ torch.from_numpy(OPENCV_TO_NERFSTUDIO).double()
+                poses.append(cam_pose.float()[:3, :4])
+
+        # To tensors
+        poses = torch.stack(poses)
+        times = torch.tensor(times, dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+
+        cameras = Cameras(
+            fx=torch.tensor(fx, dtype=torch.float32),
+            fy=torch.tensor(fy, dtype=torch.float32),
+            cx=torch.tensor(cx, dtype=torch.float32),
+            cy=torch.tensor(cy, dtype=torch.float32),
+            height=KITTI_IMAGE_HEIGHT,
+            width=KITTI_IMAGE_WIDTH,
+            camera_to_worlds=poses[:, :3, :4],
+            camera_type=CameraType.PERSPECTIVE,
+            times=times,
+            metadata={"sensor_idxs": idxs},
+        )
+        return cameras, filenames
+
+    def _get_lidars(self) -> Tuple[Lidars, List[Path]]:
+        """Returns lidar info and loaded point clouds."""
+        times, poses, idxs, lidar_filenames = [], [], [], []
+
+        for lidar_idx, lidar_name in enumerate(self.config.lidars):
+            lidar_folder = self.config.data / self.config.split / lidar_name / self.config.sequence
+
+            lidar_files = sorted(lidar_folder.glob("*.bin"))
+
+            # get the calibration of the lidar
+            imu2velo = torch.from_numpy(self.calibs["Tr_imu_to_velo"].copy())  # 3x4
+            velo2imu = to4x4(imu2velo).inverse()
+
+            for lidar_file in lidar_files:
+                frame_idx = int(lidar_file.stem)
+                if self.config.use_sensor_timestamps:
+                    times.append(self.timestamp_per_sensor[lidar_name][frame_idx])
+                else:
+                    times.append(frame_idx / DATA_FREQUENCY)
+
+                ego_pose = self._get_ego_pose(frame_idx, sensor=lidar_name).float()
+                pose = ego_pose @ velo2imu.clone()
+                poses.append(pose)
+                idxs.append(lidar_idx)
+                lidar_filenames.append(lidar_file)
+
+        # To tensors
+        poses = torch.stack(poses)  # will change to float later, see _read_lidars
+        times = torch.tensor(times, dtype=torch.float64)
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+
+        lidars = Lidars(
+            lidar_to_worlds=poses[:, :3, :4],
+            lidar_type=LidarType.VELODYNE64E,
+            times=times,
+            assume_ego_compensated=False,
+            metadata={"sensor_idxs": idxs},
+            horizontal_beam_divergence=HORIZONTAL_BEAM_DIVERGENCE,
+            vertical_beam_divergence=VERTICAL_BEAM_DIVERGENCE,
+        )
+
+        return lidars, lidar_filenames
+
+    def _read_lidars(self, lidars: Lidars, filepaths: List[Path]) -> List[torch.Tensor]:
+        point_clouds = []
+        for filepath in filepaths:
+            pc = np.fromfile(filepath, dtype=np.float32).reshape(-1, 4)
+            xyz = pc[:, :3]  # N x 3
+            intensity = pc[:, 3] / MAX_INTENSITY_VALUE  # N,
+            t = get_mock_timestamps(xyz)  # N, relative timestamps
+            pc = np.hstack((xyz, intensity[:, None], t[:, None]))
+            point_clouds.append(torch.from_numpy(pc).float())
+
+        lidars.lidar_to_worlds = lidars.lidar_to_worlds.float()
+        return point_clouds
+
+    def _get_actor_trajectories(self) -> List[Dict]:
+        """Returns a list of actor trajectories.
+
+        Each trajectory is a dictionary with the following keys:
+            - poses: the poses of the actor (float32)
+            - timestamps: the timestamps of the actor (float64)
+            - dims: the dimensions of the actor, wlh order (float32)
+            - label: the label of the actor (str)
+            - stationary: whether the actor is stationary (bool)
+            - symmetric: whether the actor is expected to be symmetric (bool)
+            - deformable: whether the actor is expected to be deformable (e.g. pedestrian)
+        """
+
+        if self.config.include_deformable_actors:
+            allowed_cats = ALLOWED_CATEGORIES.union(DEFORMABLE_CATEGORIES)
+        else:
+            allowed_cats = ALLOWED_CATEGORIES
+
+        anno_file = self.config.data / self.config.split / "label_02" / f"{self.config.sequence}.txt"
+
+        # read annotations
+        with open(anno_file, "r") as f:
+            lines = f.readlines()
+
+        traj_list = defaultdict(list)
+
+        # loop over all annotations to create per agent trajectories
+        for line in lines:
+            line = line.strip()
+            frame, track_id, label, _, _, _, _, _, _, _, height, width, length, x, y, z, rotation_y = line.split(" ")
+
+            # remove all actors that are not in the allowed categories
+            if label not in allowed_cats:
+                continue
+
+            frame = int(frame)
+            track_id = int(track_id)
+
+            height = float(height)
+            width = float(width)
+            length = float(length)
+
+            # defined in the camera coordinate system of the ego-vehicle
+            x = float(x)
+            y = float(y) - height / 2.0  # center of the object is at the bottom
+            z = float(z)
+
+            #
+            rotation_y = float(rotation_y)
+
+            traj_list[track_id].append(
+                {
+                    "frame": frame,
+                    "label": label,
+                    "height": height,
+                    "width": width,
+                    "length": length,
+                    "x": x,
+                    "y": y,
+                    "z": z,
+                    "rotation_y": rotation_y,
+                }
+            )
+
+        trajs = []
+        for track_id, track in traj_list.items():
+            poses, timestamps, wlh = [], [], []
+            if len(track) < 2:
+                continue  # skip if there is only one frame
+
+            label = track[0]["label"]
+            deformable = label in DEFORMABLE_CATEGORIES
+            symmetric = label in SYMMETRIC_CATEGORIES
+
+            # cam to velo
+            velo2cam = self.calibs["Tr_velo_to_cam"].copy()  # 3x4
+            velo2cam = np.vstack((velo2cam, np.array([0, 0, 0, 1])))
+            cam2velo = np.linalg.inv(velo2cam)
+            # imu to velo
+            imu2velo = self.calibs["Tr_imu_to_velo"].copy()  # 3x4
+            imu2velo = np.vstack((imu2velo, np.array([0, 0, 0, 1])))
+            velo2imu = np.linalg.inv(imu2velo)
+            # cam to imu
+            cam2imu = velo2imu @ cam2velo
+
+            # note that the boxes are in the camera coordinate system of the ego-vehicle
+            for box in sorted(track, key=lambda x: x["frame"]):
+                if self.config.use_sensor_timestamps:
+                    timestamps.append(self.timestamp_per_sensor["velodyne"][box["frame"]])
+                else:
+                    timestamps.append(box["frame"] / DATA_FREQUENCY)
+                # The actors are annotated in the LIDAR point cloud, so we use those timestamps.
+                ego_pose = self._get_ego_pose(box["frame"], "velodyne").numpy()
+                # cam to world
+                cam2world = ego_pose @ cam2imu
+                obj_pose_cam = np.eye(4)
+                obj_pose_cam[:3, 3] = np.array([box["x"], box["y"], box["z"]])
+                obj_pose_cam[:3, :3] = Quaternion(axis=[0, 1, 0], angle=np.pi / 2 + box["rotation_y"]).rotation_matrix
+                obj_pose_world = cam2world @ obj_pose_cam @ RIGHT_FRONT_UP2RIGHT_DOWN_FRONT
+                poses.append(obj_pose_world)
+                wlh.append(np.array([box["width"], box["length"], box["height"]]))
+
+            poses = np.array(poses)
+            # dynamic if we move more that 1m in any direction
+            dynamic = np.any(np.std(poses[:, :3, 3], axis=0) > 0.5)
+            # we skip all stationary objects
+            if not dynamic:
+                continue
+
+            trajs.append(
+                {
+                    "poses": torch.tensor(poses, dtype=torch.float32),
+                    "timestamps": torch.tensor(timestamps, dtype=torch.float64),
+                    "dims": torch.tensor(np.median(wlh, axis=0), dtype=torch.float32),
+                    "label": label,
+                    "stationary": not dynamic,
+                    "symmetric": symmetric,
+                    "deformable": deformable,
+                }
+            )
+
+        return trajs
+
+    def _generate_dataparser_outputs(self, split="train"):
+        # load the ego_poses
+        root = self.config.data / self.config.split
+        ego_pose_path = root / "oxts" / f"{self.config.sequence}.txt"
+        self.ego_poses = get_poses_from_oxts(ego_pose_path)
+        self.calibs = get_calib(root / "calib" / f"{self.config.sequence}.txt")
+
+        # we only use timestamps if all sensors have them.
+        if self.config.use_sensor_timestamps:
+            self._setup_sensor_timestamps()
+
+        out = super()._generate_dataparser_outputs(split=split)
+        del self.ego_poses
+        del self.calibs
+        return out
+
+    def _setup_sensor_timestamps(self) -> None:
+        self.timestamp_per_sensor = {}
+        for sensor in self.config.cameras + self.config.lidars + ("oxts",):
+            timestamp_file = (
+                self.config.data
+                / self.config.split
+                / sensor
+                / "timestamps"
+                / f"{self.config.sequence}"
+                / "timestamps.txt"
+            )
+            assert timestamp_file.exists(), f"Trying to use sensor timestamps but file {timestamp_file} does not exist."
+            with open(timestamp_file, "r") as f:
+                lines = f.readlines()
+            # parse the timestamps: 2011-09-26 13:13:32.322364840
+            timestamps = []
+            for line in lines:
+                line = line.strip()[:-3]  # remove nanosecond precision
+                dt = datetime.strptime(line, "%Y-%m-%d %H:%M:%S.%f")
+                timestamp = dt.timestamp()
+                timestamps.append(timestamp)
+
+            self.timestamp_per_sensor[sensor] = np.array(timestamps)
+
+    def _get_ego_pose(self, frame_idx: int, sensor: str):
+        if self.config.use_sensor_timestamps:
+            ego_pose_timestamps = self.timestamp_per_sensor["oxts"]
+            sensor_timestamp = self.timestamp_per_sensor[sensor][frame_idx]
+            # get closest ego-pose timestamp
+            next_ego_pose_idx = np.searchsorted(ego_pose_timestamps, sensor_timestamp)
+            prev_ego_pose_idx = next_ego_pose_idx - 1
+            # lets not extrapolate
+            if prev_ego_pose_idx < 0:
+                return torch.from_numpy(self.ego_poses[0])
+            if next_ego_pose_idx >= len(ego_pose_timestamps):
+                return torch.from_numpy(self.ego_poses[-1])
+
+            prev_ego_pose = torch.from_numpy(self.ego_poses[prev_ego_pose_idx].copy())  # 4x4
+            next_ego_pose = torch.from_numpy(self.ego_poses[next_ego_pose_idx].copy())  # 4x4
+            fraction = (sensor_timestamp - ego_pose_timestamps[prev_ego_pose_idx]) / (
+                ego_pose_timestamps[next_ego_pose_idx] - ego_pose_timestamps[prev_ego_pose_idx]
+            )
+
+            # position
+            pos_prev = prev_ego_pose[:3, 3]
+            pos_next = next_ego_pose[:3, 3]
+            interp_pos = pos_prev + (pos_next - pos_prev) * fraction
+
+            # rotation
+            quat_prev = rotmat_to_unitquat(prev_ego_pose[:3, :3])
+            quat_next = rotmat_to_unitquat(next_ego_pose[:3, :3])
+            interp_quat = unitquat_slerp(quat_prev.float(), quat_next.float(), torch.tensor(fraction))
+            interp_rot = unitquat_to_rotmat(interp_quat)
+
+            # concat
+            interp_pose = torch.eye(4)
+            interp_pose[:3, 3] = interp_pos.float()
+            interp_pose[:3, :3] = interp_rot
+            return interp_pose
+        else:
+            return torch.from_numpy(self.ego_poses[frame_idx].copy())
+
+    def _get_linspaced_indices(self, sensor_idxs: Tensor) -> Tuple[Tensor, Tensor]:
+        # if we are using all the samples, i.e., optimizing poses, we can use the same for eval
+        if self.config.train_split_fraction == 1.0:
+            return torch.arange(sensor_idxs.numel(), dtype=torch.int64), torch.arange(
+                sensor_idxs.numel(), dtype=torch.int64
+            )
+
+        if self.config.use_mars_nvs_50_split:
+            # based on the implementation found here:
+            # https://github.com/OPEN-AIR-SUN/mars/blob/master/mars/data/mars_kitti_dataparser.py#L1090-L1114
+            start_frame = MARS_SEQ_TO_START_FRAME[self.config.sequence]
+            end_frame = MARS_SEQ_TO_END_FRAME[self.config.sequence]
+            if end_frame != -1:
+                end_frame += 1  # they are inclusive
+
+            # for each camera the logic is:
+            # [train, train, skip, eval] and repeat this
+            first_sensor_idxes = sensor_idxs[sensor_idxs == sensor_idxs.unique()[0]]
+            train_mask = torch.zeros_like(first_sensor_idxes)
+            train_mask[start_frame:end_frame:4] = 1
+            train_mask[start_frame + 1 : end_frame : 4] = 1
+
+            eval_mask = torch.zeros_like(first_sensor_idxes)
+            eval_mask[start_frame + 3 : end_frame : 4] = 1
+
+            # repeat this for the number of unique sensors
+            train_mask = torch.concat([train_mask] * len(sensor_idxs.unique()))
+            eval_mask = torch.concat([eval_mask] * len(sensor_idxs.unique()))
+            assert (train_mask + eval_mask).max() == 1, "Something went wrong with the indices."
+
+            idxes = torch.arange(len(sensor_idxs))
+            train_indices = idxes[train_mask.bool()]
+            eval_indices = idxes[eval_mask.bool()]
+            return train_indices, eval_indices
+        else:
+            return super()._get_linspaced_indices(sensor_idxs)
+
+
+def lat_lon_to_mercator(
+    lat: npt.NDArray[np.float64], lon: npt.NDArray[np.float64], scale: float
+) -> Tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]:
+    """Converts lat/lon coordinates to mercator coordinates.
+
+    Reimplemented from the kitti dataset devkit.
+    """
+
+    earth_radius = 6378137.0
+    mx = lon * scale * earth_radius * np.pi / 180.0
+    my = np.log(np.tan((90 + lat) * np.pi / 360.0)) * scale * earth_radius
+    return mx, my
+
+
+def get_poses_from_oxts(ego_pose_path: Path) -> npt.NDArray[np.float64]:
+    """Takes the path to the KITTI oxts file and returns the poses for each timestamp.
+
+    Reimplemented based on the KITTI dataset devkit.
+    """
+    oxts = np.loadtxt(ego_pose_path, dtype=np.float64)
+    lat, lon, alt = oxts[:, 0], oxts[:, 1], oxts[:, 2]
+    roll, pitch, yaw = oxts[:, 3], oxts[:, 4], oxts[:, 5]
+
+    scale = np.cos(lat[0] * np.pi / 180.0)
+    x, y = lat_lon_to_mercator(lat, lon, scale)
+    z = alt
+
+    Rx = np.array(
+        [
+            [np.ones_like(roll), np.zeros_like(roll), np.zeros_like(roll)],
+            [np.zeros_like(roll), np.cos(roll), -np.sin(roll)],
+            [np.zeros_like(roll), np.sin(roll), np.cos(roll)],
+        ]
+    )  # 3x3xN
+    Ry = np.array(
+        [
+            [np.cos(pitch), np.zeros_like(roll), np.sin(pitch)],
+            [np.zeros_like(roll), np.ones_like(roll), np.zeros_like(roll)],
+            [-np.sin(pitch), np.zeros_like(roll), np.cos(pitch)],
+        ]
+    )  # 3x3xN
+    Rz = np.array(
+        [
+            [np.cos(yaw), -np.sin(yaw), np.zeros_like(roll)],
+            [np.sin(yaw), np.cos(yaw), np.zeros_like(roll)],
+            [np.zeros_like(roll), np.zeros_like(roll), np.ones_like(roll)],
+        ]
+    )  # 3x3xN
+    # make into N x 3 x 3
+    Rx = np.transpose(Rx, (2, 0, 1))
+    Ry = np.transpose(Ry, (2, 0, 1))
+    Rz = np.transpose(Rz, (2, 0, 1))
+
+    R = Rz @ Ry @ Rx
+
+    poses = np.zeros((len(oxts), 4, 4))
+    poses[:, :3, :3] = R
+    poses[:, :3, 3] = np.array([x, y, z]).T
+    poses[:, 3, 3] = 1
+
+    # normalize to the first pose
+    poses = np.linalg.inv(poses[0]) @ poses
+
+    return poses
+
+
+def get_calib(calib_path: Path) -> Dict[str, npt.NDArray[np.float32]]:
+    with open(str(calib_path), "r") as f:
+        lines = f.readlines()
+
+    for line in lines:
+        line = line.strip()
+        if line.startswith("P0:"):
+            P0 = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 4)
+        elif line.startswith("P1:"):
+            P1 = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 4)
+        elif line.startswith("P2:"):
+            P2 = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 4)
+        elif line.startswith("P3:"):
+            P3 = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 4)
+        elif line.startswith("R_rect"):
+            R_rect = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 3)
+        elif line.startswith("Tr_velo_cam"):
+            Tr_velo_to_cam = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 4)
+        elif line.startswith("Tr_imu_velo"):
+            Tr_imu_to_velo = np.array(line.split(" ")[1:], dtype=np.float32).reshape(3, 4)
+        else:
+            raise ValueError(f"Unknown calibration line: {line}")
+    return {
+        "P0": P0,  # type: ignore
+        "P1": P1,  # type: ignore
+        "P2": P2,  # type: ignore
+        "P3": P3,  # type: ignore
+        "R_rect": R_rect,  # type: ignore
+        "Tr_velo_to_cam": Tr_velo_to_cam,  # type: ignore
+        "Tr_imu_to_velo": Tr_imu_to_velo,  # type: ignore
+    }
+
+
+def get_mock_timestamps(points: npt.NDArray[np.float32]) -> npt.NDArray[np.float32]:
+    """Get mock relative timestamps for the velodyne points."""
+    # the velodyne has x forward, y left, z up and the sweep is split behind the car.
+    # it is also rotating counter-clockwise, meaning that the angles close to -pi are the
+    # first ones in the sweep and the ones close to pi are the last ones in the sweep.
+    angles = np.arctan2(points[:, 1], points[:, 0])  # N, [-pi, pi]
+    angles += np.pi  # N, [0, 2pi]
+    # see how much of the rotation have finished
+    fraction_of_rotation = angles / (2 * np.pi)  # N, [0, 1]
+    # get the pseudo timestamps based on the total rotation time
+    timestamps = fraction_of_rotation * LIDAR_ROTATION_TIME
+    return timestamps
diff --git a/nerfstudio/data/dataparsers/nerfosr_dataparser.py b/nerfstudio/data/dataparsers/nerfosr_dataparser.py
deleted file mode 100644
index db884cb7..00000000
--- a/nerfstudio/data/dataparsers/nerfosr_dataparser.py
+++ /dev/null
@@ -1,228 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Data parser for NeRF-OSR datasets
-
-    Presented in the paper: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/
-
-"""
-
-from __future__ import annotations
-
-import glob
-import os
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import List, Literal, Tuple, Type
-
-import numpy as np
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-
-
-def _find_files(directory: str, exts: List[str]):
-    """Find all files in a directory that have a certain file extension.
-
-    Args:
-        directory : The directory to search for files.
-        exts :  A list of file extensions to search for. Each file extension should be in the form '*.ext'.
-
-    Returns:
-        A list of file paths for all the files that were found. The list is sorted alphabetically.
-    """
-    if os.path.isdir(directory):
-        # types should be ['*.png', '*.jpg', '*.JPG', '*.PNG']
-        files_grabbed = []
-        for ext in exts:
-            files_grabbed.extend(glob.glob(os.path.join(directory, ext)))
-        if len(files_grabbed) > 0:
-            files_grabbed = sorted(files_grabbed)
-        return files_grabbed
-    return []
-
-
-def _parse_osm_txt(filename: str):
-    """Parse a text file containing numbers and return a 4x4 numpy array of float32 values.
-
-    Args:
-        filename : a file containing numbers in a 4x4 matrix.
-
-    Returns:
-        A numpy array of shape [4, 4] containing the numbers from the file.
-    """
-    assert os.path.isfile(filename)
-    with open(filename, encoding="UTF-8") as f:
-        nums = f.read().split()
-    return np.array([float(x) for x in nums]).reshape([4, 4]).astype(np.float32)
-
-
-def get_camera_params(
-    scene_dir: str, split: Literal["train", "validation", "test"]
-) -> Tuple[torch.Tensor, torch.Tensor, int]:
-    """Load camera intrinsic and extrinsic parameters for a given scene split.
-
-    Args"
-      scene_dir : The directory containing the scene data.
-      split : The split for which to load the camera parameters.
-
-    Returns
-        A tuple containing the intrinsic parameters (as a torch.Tensor of shape [N, 4, 4]),
-        the camera-to-world matrices (as a torch.Tensor of shape [N, 4, 4]), and the number of cameras (N).
-    """
-    split_dir = f"{scene_dir}/{split}"
-
-    # camera parameters files
-    intrinsics_files = _find_files(f"{split_dir}/intrinsics", exts=["*.txt"])
-    pose_files = _find_files(f"{split_dir}/pose", exts=["*.txt"])
-
-    num_cams = len(pose_files)
-
-    intrinsics = []
-    camera_to_worlds = []
-    for i in range(num_cams):
-        intrinsics.append(_parse_osm_txt(intrinsics_files[i]))
-
-        pose = _parse_osm_txt(pose_files[i])
-
-        # convert from COLMAP/OpenCV to nerfstudio camera (OpenGL/Blender)
-        pose[0:3, 1:3] *= -1
-
-        camera_to_worlds.append(pose)
-
-    intrinsics = torch.from_numpy(np.stack(intrinsics).astype(np.float32))  # [N, 4, 4]
-    camera_to_worlds = torch.from_numpy(np.stack(camera_to_worlds).astype(np.float32))  # [N, 4, 4]
-
-    return intrinsics, camera_to_worlds, num_cams
-
-
-@dataclass
-class NeRFOSRDataParserConfig(DataParserConfig):
-    """Nerfstudio dataset config"""
-
-    _target: Type = field(default_factory=lambda: NeRFOSR)
-    """target class to instantiate"""
-    data: Path = Path("data/NeRF-OSR/Data/")
-    """Directory specifying location of data."""
-    scene: str = "stjacob"
-    """Which scene to load"""
-    scene_scale: float = 1.0
-    """How much to scale the region of interest by."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    use_masks: bool = False
-    """Whether to use masks."""
-    orientation_method: Literal["pca", "up", "vertical", "none"] = "vertical"
-    """The method to use for orientation."""
-    center_method: Literal["poses", "focus", "none"] = "focus"
-    """The method to use for centering."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-
-
-@dataclass
-class NeRFOSR(DataParser):
-    """NeRFOSR Dataparser
-    Presented in the paper: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/
-
-    Some of this code comes from https://github.com/r00tman/NeRF-OSR/blob/main/data_loader_split.py
-
-    Source data convention is:
-      camera coordinate system: x-->right, y-->down, z-->scene (opencv/colmap convention)
-      poses is camera-to-world
-      masks are 0 for dynamic content, 255 for static content
-    """
-
-    config: NeRFOSRDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        data = self.config.data
-        scene = self.config.scene
-        split = "validation" if split == "val" else split
-
-        if scene == "trevi":
-            scene_dir = f"{data}/{scene}/final_clean"
-            split_dir = f"{data}/{scene}/final_clean/{split}"
-        else:
-            scene_dir = f"{data}/{scene}/final"
-            split_dir = f"{data}/{scene}/final/{split}"
-
-        # get all split cam params
-        intrinsics_train, camera_to_worlds_train, n_train = get_camera_params(scene_dir, "train")
-        intrinsics_val, camera_to_worlds_val, n_val = get_camera_params(scene_dir, "validation")
-        intrinsics_test, camera_to_worlds_test, _ = get_camera_params(scene_dir, "test")
-
-        # combine all cam params
-        intrinsics = torch.cat([intrinsics_train, intrinsics_val, intrinsics_test], dim=0)
-        camera_to_worlds = torch.cat([camera_to_worlds_train, camera_to_worlds_val, camera_to_worlds_test], dim=0)
-
-        camera_to_worlds, _ = camera_utils.auto_orient_and_center_poses(
-            camera_to_worlds,
-            method=self.config.orientation_method,
-            center_method=self.config.center_method,
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= torch.max(torch.abs(camera_to_worlds[:, :3, 3]))
-
-        camera_to_worlds[:, :3, 3] *= scale_factor * self.config.scale_factor
-
-        if split == "train":
-            camera_to_worlds = camera_to_worlds[:n_train]
-            intrinsics = intrinsics[:n_train]
-        elif split == "validation":
-            camera_to_worlds = camera_to_worlds[n_train : n_train + n_val]
-            intrinsics = intrinsics[n_train : n_train + n_val]
-        elif split == "test":
-            camera_to_worlds = camera_to_worlds[n_train + n_val :]
-            intrinsics = intrinsics[n_train + n_val :]
-
-        cameras = Cameras(
-            camera_to_worlds=camera_to_worlds[:, :3, :4],
-            fx=intrinsics[:, 0, 0],
-            fy=intrinsics[:, 1, 1],
-            cx=intrinsics[:, 0, 2],
-            cy=intrinsics[:, 1, 2],
-            camera_type=CameraType.PERSPECTIVE,
-        )
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        # --- images ---
-        image_filenames = _find_files(f"{split_dir}/rgb", exts=["*.png", "*.jpg", "*.JPG", "*.PNG"])
-
-        # --- masks ---
-        mask_filenames = []
-        if self.config.use_masks:
-            mask_filenames = _find_files(f"{split_dir}/mask", exts=["*.png", "*.jpg", "*.JPG", "*.PNG"])
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
-            dataparser_scale=self.config.scale_factor,
-        )
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/nerfstudio_dataparser.py b/nerfstudio/data/dataparsers/nerfstudio_dataparser.py
deleted file mode 100644
index 910e0bc1..00000000
--- a/nerfstudio/data/dataparsers/nerfstudio_dataparser.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Data parser for nerfstudio datasets. """
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Literal, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from PIL import Image
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import CAMERA_MODEL_TO_TYPE, Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.data.utils.dataparsers_utils import (
-    get_train_eval_split_all,
-    get_train_eval_split_filename,
-    get_train_eval_split_fraction,
-    get_train_eval_split_interval,
-)
-from nerfstudio.utils.io import load_from_json
-from nerfstudio.utils.rich_utils import CONSOLE
-
-MAX_AUTO_RESOLUTION = 1600
-
-
-@dataclass
-class NerfstudioDataParserConfig(DataParserConfig):
-    """Nerfstudio dataset config"""
-
-    _target: Type = field(default_factory=lambda: Nerfstudio)
-    """target class to instantiate"""
-    data: Path = Path()
-    """Directory or explicit json file path specifying location of data."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    downscale_factor: Optional[int] = None
-    """How much to downscale images. If not set, images are chosen such that the max dimension is <1600px."""
-    scene_scale: float = 1.0
-    """How much to scale the region of interest by."""
-    orientation_method: Literal["pca", "up", "vertical", "none"] = "up"
-    """The method to use for orientation."""
-    center_method: Literal["poses", "focus", "none"] = "poses"
-    """The method to use to center the poses."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-    eval_mode: Literal["fraction", "filename", "interval", "all"] = "fraction"
-    """
-    The method to use for splitting the dataset into train and eval.
-    Fraction splits based on a percentage for train and the remaining for eval.
-    Filename splits based on filenames containing train/eval.
-    Interval uses every nth frame for eval.
-    All uses all the images for any split.
-    """
-    train_split_fraction: float = 0.9
-    """The percentage of the dataset to use for training. Only used when eval_mode is train-split-fraction."""
-    eval_interval: int = 8
-    """The interval between frames to use for eval. Only used when eval_mode is eval-interval."""
-    depth_unit_scale_factor: float = 1e-3
-    """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion."""
-    mask_color: Optional[Tuple[float, float, float]] = None
-    """Replace the unknown pixels with this color. Relevant if you have a mask but still sample everywhere."""
-    load_3D_points: bool = False
-    """Whether to load the 3D points from the colmap reconstruction."""
-
-
-@dataclass
-class Nerfstudio(DataParser):
-    """Nerfstudio DatasetParser"""
-
-    config: NerfstudioDataParserConfig
-    downscale_factor: Optional[int] = None
-
-    def _generate_dataparser_outputs(self, split="train"):
-        assert self.config.data.exists(), f"Data directory {self.config.data} does not exist."
-
-        if self.config.data.suffix == ".json":
-            meta = load_from_json(self.config.data)
-            data_dir = self.config.data.parent
-        else:
-            meta = load_from_json(self.config.data / "transforms.json")
-            data_dir = self.config.data
-
-        image_filenames = []
-        mask_filenames = []
-        depth_filenames = []
-        poses = []
-
-        fx_fixed = "fl_x" in meta
-        fy_fixed = "fl_y" in meta
-        cx_fixed = "cx" in meta
-        cy_fixed = "cy" in meta
-        height_fixed = "h" in meta
-        width_fixed = "w" in meta
-        distort_fixed = False
-        for distort_key in ["k1", "k2", "k3", "p1", "p2", "distortion_params"]:
-            if distort_key in meta:
-                distort_fixed = True
-                break
-        fisheye_crop_radius = meta.get("fisheye_crop_radius", None)
-        fx = []
-        fy = []
-        cx = []
-        cy = []
-        height = []
-        width = []
-        distort = []
-
-        # sort the frames by fname
-        fnames = []
-        for frame in meta["frames"]:
-            filepath = Path(frame["file_path"])
-            fname = self._get_fname(filepath, data_dir)
-            fnames.append(fname)
-        inds = np.argsort(fnames)
-        frames = [meta["frames"][ind] for ind in inds]
-
-        for frame in frames:
-            filepath = Path(frame["file_path"])
-            fname = self._get_fname(filepath, data_dir)
-
-            if not fx_fixed:
-                assert "fl_x" in frame, "fx not specified in frame"
-                fx.append(float(frame["fl_x"]))
-            if not fy_fixed:
-                assert "fl_y" in frame, "fy not specified in frame"
-                fy.append(float(frame["fl_y"]))
-            if not cx_fixed:
-                assert "cx" in frame, "cx not specified in frame"
-                cx.append(float(frame["cx"]))
-            if not cy_fixed:
-                assert "cy" in frame, "cy not specified in frame"
-                cy.append(float(frame["cy"]))
-            if not height_fixed:
-                assert "h" in frame, "height not specified in frame"
-                height.append(int(frame["h"]))
-            if not width_fixed:
-                assert "w" in frame, "width not specified in frame"
-                width.append(int(frame["w"]))
-            if not distort_fixed:
-                distort.append(
-                    torch.tensor(frame["distortion_params"], dtype=torch.float32)
-                    if "distortion_params" in frame
-                    else camera_utils.get_distortion_params(
-                        k1=float(frame["k1"]) if "k1" in frame else 0.0,
-                        k2=float(frame["k2"]) if "k2" in frame else 0.0,
-                        k3=float(frame["k3"]) if "k3" in frame else 0.0,
-                        k4=float(frame["k4"]) if "k4" in frame else 0.0,
-                        p1=float(frame["p1"]) if "p1" in frame else 0.0,
-                        p2=float(frame["p2"]) if "p2" in frame else 0.0,
-                    )
-                )
-
-            image_filenames.append(fname)
-            poses.append(np.array(frame["transform_matrix"]))
-            if "mask_path" in frame:
-                mask_filepath = Path(frame["mask_path"])
-                mask_fname = self._get_fname(
-                    mask_filepath,
-                    data_dir,
-                    downsample_folder_prefix="masks_",
-                )
-                mask_filenames.append(mask_fname)
-
-            if "depth_file_path" in frame:
-                depth_filepath = Path(frame["depth_file_path"])
-                depth_fname = self._get_fname(depth_filepath, data_dir, downsample_folder_prefix="depths_")
-                depth_filenames.append(depth_fname)
-
-        assert len(mask_filenames) == 0 or (len(mask_filenames) == len(image_filenames)), """
-        Different number of image and mask filenames.
-        You should check that mask_path is specified for every frame (or zero frames) in transforms.json.
-        """
-        assert len(depth_filenames) == 0 or (len(depth_filenames) == len(image_filenames)), """
-        Different number of image and depth filenames.
-        You should check that depth_file_path is specified for every frame (or zero frames) in transforms.json.
-        """
-
-        has_split_files_spec = any(f"{split}_filenames" in meta for split in ("train", "val", "test"))
-        if f"{split}_filenames" in meta:
-            # Validate split first
-            split_filenames = set(self._get_fname(Path(x), data_dir) for x in meta[f"{split}_filenames"])
-            unmatched_filenames = split_filenames.difference(image_filenames)
-            if unmatched_filenames:
-                raise RuntimeError(f"Some filenames for split {split} were not found: {unmatched_filenames}.")
-
-            indices = [i for i, path in enumerate(image_filenames) if path in split_filenames]
-            CONSOLE.log(f"[yellow] Dataset is overriding {split}_indices to {indices}")
-            indices = np.array(indices, dtype=np.int32)
-        elif has_split_files_spec:
-            raise RuntimeError(f"The dataset's list of filenames for split {split} is missing.")
-        else:
-            # find train and eval indices based on the eval_mode specified
-            if self.config.eval_mode == "fraction":
-                i_train, i_eval = get_train_eval_split_fraction(image_filenames, self.config.train_split_fraction)
-            elif self.config.eval_mode == "filename":
-                i_train, i_eval = get_train_eval_split_filename(image_filenames)
-            elif self.config.eval_mode == "interval":
-                i_train, i_eval = get_train_eval_split_interval(image_filenames, self.config.eval_interval)
-            elif self.config.eval_mode == "all":
-                CONSOLE.log(
-                    "[yellow] Be careful with '--eval-mode=all'. If using camera optimization, the cameras may diverge in the current implementation, giving unpredictable results."
-                )
-                i_train, i_eval = get_train_eval_split_all(image_filenames)
-            else:
-                raise ValueError(f"Unknown eval mode {self.config.eval_mode}")
-
-            if split == "train":
-                indices = i_train
-            elif split in ["val", "test"]:
-                indices = i_eval
-            else:
-                raise ValueError(f"Unknown dataparser split {split}")
-
-        if "orientation_override" in meta:
-            orientation_method = meta["orientation_override"]
-            CONSOLE.log(f"[yellow] Dataset is overriding orientation method to {orientation_method}")
-        else:
-            orientation_method = self.config.orientation_method
-
-        poses = torch.from_numpy(np.array(poses).astype(np.float32))
-        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
-            poses,
-            method=orientation_method,
-            center_method=self.config.center_method,
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
-        scale_factor *= self.config.scale_factor
-
-        poses[:, :3, 3] *= scale_factor
-
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        image_filenames = [image_filenames[i] for i in indices]
-        mask_filenames = [mask_filenames[i] for i in indices] if len(mask_filenames) > 0 else []
-        depth_filenames = [depth_filenames[i] for i in indices] if len(depth_filenames) > 0 else []
-
-        idx_tensor = torch.tensor(indices, dtype=torch.long)
-        poses = poses[idx_tensor]
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        if "camera_model" in meta:
-            camera_type = CAMERA_MODEL_TO_TYPE[meta["camera_model"]]
-        else:
-            camera_type = CameraType.PERSPECTIVE
-
-        fx = float(meta["fl_x"]) if fx_fixed else torch.tensor(fx, dtype=torch.float32)[idx_tensor]
-        fy = float(meta["fl_y"]) if fy_fixed else torch.tensor(fy, dtype=torch.float32)[idx_tensor]
-        cx = float(meta["cx"]) if cx_fixed else torch.tensor(cx, dtype=torch.float32)[idx_tensor]
-        cy = float(meta["cy"]) if cy_fixed else torch.tensor(cy, dtype=torch.float32)[idx_tensor]
-        height = int(meta["h"]) if height_fixed else torch.tensor(height, dtype=torch.int32)[idx_tensor]
-        width = int(meta["w"]) if width_fixed else torch.tensor(width, dtype=torch.int32)[idx_tensor]
-        if distort_fixed:
-            distortion_params = (
-                torch.tensor(meta["distortion_params"], dtype=torch.float32)
-                if "distortion_params" in meta
-                else camera_utils.get_distortion_params(
-                    k1=float(meta["k1"]) if "k1" in meta else 0.0,
-                    k2=float(meta["k2"]) if "k2" in meta else 0.0,
-                    k3=float(meta["k3"]) if "k3" in meta else 0.0,
-                    k4=float(meta["k4"]) if "k4" in meta else 0.0,
-                    p1=float(meta["p1"]) if "p1" in meta else 0.0,
-                    p2=float(meta["p2"]) if "p2" in meta else 0.0,
-                )
-            )
-        else:
-            distortion_params = torch.stack(distort, dim=0)[idx_tensor]
-
-        # Only add fisheye crop radius parameter if the images are actually fisheye, to allow the same config to be used
-        # for both fisheye and non-fisheye datasets.
-        metadata = {}
-        if (camera_type in [CameraType.FISHEYE, CameraType.FISHEYE624]) and (fisheye_crop_radius is not None):
-            metadata["fisheye_crop_radius"] = fisheye_crop_radius
-
-        cameras = Cameras(
-            fx=fx,
-            fy=fy,
-            cx=cx,
-            cy=cy,
-            distortion_params=distortion_params,
-            height=height,
-            width=width,
-            camera_to_worlds=poses[:, :3, :4],
-            camera_type=camera_type,
-            metadata=metadata,
-        )
-
-        assert self.downscale_factor is not None
-        cameras.rescale_output_resolution(scaling_factor=1.0 / self.downscale_factor)
-
-        # The naming is somewhat confusing, but:
-        # - transform_matrix contains the transformation to dataparser output coordinates from saved coordinates.
-        # - dataparser_transform_matrix contains the transformation to dataparser output coordinates from original data coordinates.
-        # - applied_transform contains the transformation to saved coordinates from original data coordinates.
-        applied_transform = None
-        colmap_path = self.config.data / "colmap/sparse/0"
-        if "applied_transform" in meta:
-            applied_transform = torch.tensor(meta["applied_transform"], dtype=transform_matrix.dtype)
-        elif colmap_path.exists():
-            # For converting from colmap, this was the effective value of applied_transform that was being
-            # used before we added the applied_transform field to the output dataformat.
-            meta["applied_transform"] = [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, -1, 0]]
-            applied_transform = torch.tensor(meta["applied_transform"], dtype=transform_matrix.dtype)
-
-        if applied_transform is not None:
-            dataparser_transform_matrix = transform_matrix @ torch.cat(
-                [applied_transform, torch.tensor([[0, 0, 0, 1]], dtype=transform_matrix.dtype)], 0
-            )
-        else:
-            dataparser_transform_matrix = transform_matrix
-
-        if "applied_scale" in meta:
-            applied_scale = float(meta["applied_scale"])
-            scale_factor *= applied_scale
-
-        # reinitialize metadata for dataparser_outputs
-        metadata = {}
-
-        # _generate_dataparser_outputs might be called more than once so we check if we already loaded the point cloud
-        try:
-            self.prompted_user
-        except AttributeError:
-            self.prompted_user = False
-
-        # Load 3D points
-        if self.config.load_3D_points:
-            if "ply_file_path" in meta:
-                ply_file_path = data_dir / meta["ply_file_path"]
-
-            elif colmap_path.exists():
-                from rich.prompt import Confirm
-
-                # check if user wants to make a point cloud from colmap points
-                if not self.prompted_user:
-                    self.create_pc = Confirm.ask(
-                        "load_3D_points is true, but the dataset was processed with an outdated ns-process-data that didn't convert colmap points to .ply! Update the colmap dataset automatically?"
-                    )
-
-                if self.create_pc:
-                    import json
-
-                    from nerfstudio.process_data.colmap_utils import create_ply_from_colmap
-
-                    with open(self.config.data / "transforms.json") as f:
-                        transforms = json.load(f)
-
-                    # Update dataset if missing the applied_transform field.
-                    if "applied_transform" not in transforms:
-                        transforms["applied_transform"] = meta["applied_transform"]
-
-                    ply_filename = "sparse_pc.ply"
-                    create_ply_from_colmap(
-                        filename=ply_filename,
-                        recon_dir=colmap_path,
-                        output_dir=self.config.data,
-                        applied_transform=applied_transform,
-                    )
-                    ply_file_path = data_dir / ply_filename
-                    transforms["ply_file_path"] = ply_filename
-
-                    # This was the applied_transform value
-
-                    with open(self.config.data / "transforms.json", "w", encoding="utf-8") as f:
-                        json.dump(transforms, f, indent=4)
-                else:
-                    ply_file_path = None
-            else:
-                if not self.prompted_user:
-                    CONSOLE.print(
-                        "[bold yellow]Warning: load_3D_points set to true but no point cloud found. splatfacto will use random point cloud initialization."
-                    )
-                ply_file_path = None
-
-            if ply_file_path:
-                sparse_points = self._load_3D_points(ply_file_path, transform_matrix, scale_factor)
-                if sparse_points is not None:
-                    metadata.update(sparse_points)
-            self.prompted_user = True
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
-            dataparser_scale=scale_factor,
-            dataparser_transform=dataparser_transform_matrix,
-            metadata={
-                "depth_filenames": depth_filenames if len(depth_filenames) > 0 else None,
-                "depth_unit_scale_factor": self.config.depth_unit_scale_factor,
-                "mask_color": self.config.mask_color,
-                **metadata,
-            },
-        )
-        return dataparser_outputs
-
-    def _load_3D_points(self, ply_file_path: Path, transform_matrix: torch.Tensor, scale_factor: float):
-        """Loads point clouds positions and colors from .ply
-
-        Args:
-            ply_file_path: Path to .ply file
-            transform_matrix: Matrix to transform world coordinates
-            scale_factor: How much to scale the camera origins by.
-
-        Returns:
-            A dictionary of points: points3D_xyz and colors: points3D_rgb
-        """
-        import open3d as o3d  # Importing open3d is slow, so we only do it if we need it.
-
-        pcd = o3d.io.read_point_cloud(str(ply_file_path))
-
-        # if no points found don't read in an initial point cloud
-        if len(pcd.points) == 0:
-            return None
-
-        points3D = torch.from_numpy(np.asarray(pcd.points, dtype=np.float32))
-        points3D = (
-            torch.cat(
-                (
-                    points3D,
-                    torch.ones_like(points3D[..., :1]),
-                ),
-                -1,
-            )
-            @ transform_matrix.T
-        )
-        points3D *= scale_factor
-        points3D_rgb = torch.from_numpy((np.asarray(pcd.colors) * 255).astype(np.uint8))
-
-        out = {
-            "points3D_xyz": points3D,
-            "points3D_rgb": points3D_rgb,
-        }
-        return out
-
-    def _get_fname(self, filepath: Path, data_dir: Path, downsample_folder_prefix="images_") -> Path:
-        """Get the filename of the image file.
-        downsample_folder_prefix can be used to point to auxiliary image data, e.g. masks
-
-        filepath: the base file name of the transformations.
-        data_dir: the directory of the data that contains the transform file
-        downsample_folder_prefix: prefix of the newly generated downsampled images
-        """
-
-        if self.downscale_factor is None:
-            if self.config.downscale_factor is None:
-                test_img = Image.open(data_dir / filepath)
-                h, w = test_img.size
-                max_res = max(h, w)
-                df = 0
-                while True:
-                    if (max_res / 2 ** (df)) <= MAX_AUTO_RESOLUTION:
-                        break
-                    if not (data_dir / f"{downsample_folder_prefix}{2**(df+1)}" / filepath.name).exists():
-                        break
-                    df += 1
-
-                self.downscale_factor = 2**df
-                CONSOLE.log(f"Auto image downscale factor of {self.downscale_factor}")
-            else:
-                self.downscale_factor = self.config.downscale_factor
-
-        if self.downscale_factor > 1:
-            return data_dir / f"{downsample_folder_prefix}{self.downscale_factor}" / filepath.name
-        return data_dir / filepath
diff --git a/nerfstudio/data/dataparsers/nuscenes_dataparser.py b/nerfstudio/data/dataparsers/nuscenes_dataparser.py
index f1fecc88..53eb2351 100644
--- a/nerfstudio/data/dataparsers/nuscenes_dataparser.py
+++ b/nerfstudio/data/dataparsers/nuscenes_dataparser.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -11,40 +12,111 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Data parser for NuScenes dataset"""
-import math
-import os
+from collections import defaultdict
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Literal, Optional, Tuple, Type
+from typing import Dict, List, Literal, Set, Tuple, Type, Union
 
 import numpy as np
 import pyquaternion
 import torch
+from nuscenes.nuscenes import NuScenes as NuScenesDatabase
 
 from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-
-
-def rotation_translation_to_pose(r_quat, t_vec):
-    """Convert quaternion rotation and translation vectors to 4x4 matrix"""
-
-    pose = np.eye(4)
-
-    # NB: Nuscenes recommends pyquaternion, which uses scalar-first format (w x y z)
-    # https://github.com/nutonomy/nuscenes-devkit/issues/545#issuecomment-766509242
-    # https://github.com/KieranWynn/pyquaternion/blob/99025c17bab1c55265d61add13375433b35251af/pyquaternion/quaternion.py#L299
-    # https://fzheng.me/2017/11/12/quaternion_conventions_en/
-    pose[:3, :3] = pyquaternion.Quaternion(r_quat).rotation_matrix
-
-    pose[:3, 3] = t_vec
-    return pose
+from nerfstudio.cameras.lidars import Lidars, LidarType, transform_points
+from nerfstudio.data.dataparsers.ad_dataparser import (
+    DUMMY_DISTANCE_VALUE,
+    OPENCV_TO_NERFSTUDIO,
+    ADDataParser,
+    ADDataParserConfig,
+    SplitTypes,
+)
+from nerfstudio.data.utils.lidar_elevation_mappings import VELODYNE_HDL32E_ELEVATION_MAPPING
+from nerfstudio.utils import poses as pose_utils
+
+MAX_RELECTANCE_VALUE = 255.0
+LIDAR_FREQUENCY = 20.0  # Hz
+LIDAR_CHANNELS = 32  # number of vertical channels
+ALLOWED_RIGID_CLASSES = (
+    "vehicle.car",
+    "vehicle.bicycle",
+    "vehicle.motorcycle",
+    "vehicle.bus",
+    "vehicle.bus",
+    "vehicle.truck",
+    "vehicle.trailer",
+    "movable_object.pushable_pullable",
+)
+ALLOWED_DEFORMABLE_CLASSES = ("human.pedestrian",)
+
+TRACKING_TO_GT_CLASSNAME_MAPPING = {
+    "pedestrian": "human.pedestrian",
+    "bicycle": "vehicle.bicycle",
+    "motorcycle": "vehicle.motorcycle",
+    "car": "vehicle.car",
+    "bus": "vehicle.bus",
+    "truck": "vehicle.truck",
+    "trailer": "vehicle.truck",
+}
+# Nuscenes defines actor coordinate system as x-forward, y-left, z-up
+# But we want to use x-right, y-forward, z-up
+# So we need to rotate the actor coordinate system by 90 degrees around z-axis
+WLH_TO_LWH = np.array(
+    [
+        [0, 1, 0, 0],
+        [-1, 0, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1],
+    ]
+)
+HORIZONTAL_BEAM_DIVERGENCE = 0.00333333333  # radians, given as 4 inches at 100 feet
+VERTICAL_BEAM_DIVERGENCE = 0.00166666666  # radians, given as 2 inches at 100 feet
+
+NUSCENES_ELEVATION_MAPPING = {
+    "LIDAR_TOP": VELODYNE_HDL32E_ELEVATION_MAPPING,
+}
+NUSCENES_AZIMUTH_RESOLUTION = {
+    "LIDAR_TOP": 1 / 3.0,
+}
+NUSCENES_SKIP_ELEVATION_CHANNELS = {
+    "LIDAR_TOP": (
+        0,
+        1,
+    )
+}
+AVAILABLE_CAMERAS = (
+    "FRONT",
+    "FRONT_LEFT",
+    "FRONT_RIGHT",
+    "BACK",
+    "BACK_LEFT",
+    "BACK_RIGHT",
+)
+CAMERA_TO_BOTTOM_RIGHT_CROP = {
+    "FRONT": (0, 0),
+    "FRONT_LEFT": (0, 0),
+    "FRONT_RIGHT": (0, 0),
+    "BACK": (80, 0),
+    "BACK_LEFT": (0, 0),
+    "BACK_RIGHT": (0, 0),
+}
+SEQ_CAMERA_TO_BOTTOM_RIGHT_CROP = defaultdict(lambda: CAMERA_TO_BOTTOM_RIGHT_CROP)
+SEQ_CAMERA_TO_BOTTOM_RIGHT_CROP["scene-0164"] = {
+    "FRONT": (0, 0),
+    "FRONT_LEFT": (0, 0),
+    "FRONT_RIGHT": (0, 0),
+    "BACK": (80 + 66, 0),
+    "BACK_LEFT": (0, 0),
+    "BACK_RIGHT": (0, 0),
+}
+
+DEFAULT_IMAGE_HEIGHT = 900
+DEFAULT_IMAGE_WIDTH = 1600
 
 
 @dataclass
-class NuScenesDataParserConfig(DataParserConfig):
+class NuScenesDataParserConfig(ADDataParserConfig):
     """NuScenes dataset config.
     NuScenes (https://www.nuscenes.org/nuscenes) is an autonomous driving dataset containing 1000 20s clips.
     Each clip was recorded with a suite of sensors including 6 surround cameras.
@@ -55,163 +127,318 @@ class NuScenesDataParserConfig(DataParserConfig):
 
     _target: Type = field(default_factory=lambda: NuScenes)
     """target class to instantiate"""
-    data: Path = Path("scene-0103")  # TODO: rename to scene but keep checkpoint saving name?
+    sequence: str = "0103"
     """Name of the scene."""
-    data_dir: Path = Path("/mnt/local/NuScenes")
+    data: Path = Path("/mnt/local/NuScenes")
     """Path to NuScenes dataset."""
-    version: Literal["v1.0-mini", "v1.0-trainval"] = "v1.0-mini"
+    version: Literal["v1.0-mini", "v1.0-trainval"] = "v1.0-trainval"
     """Dataset version."""
-    cameras: Tuple[Literal["FRONT", "FRONT_LEFT", "FRONT_RIGHT", "BACK", "BACK_LEFT", "BACK_RIGHT"], ...] = ("FRONT",)
+    cameras: Tuple[
+        Literal[
+            "FRONT",
+            "FRONT_LEFT",
+            "FRONT_RIGHT",
+            "BACK",
+            "BACK_LEFT",
+            "BACK_RIGHT",
+            "none",
+            "all",
+        ],
+        ...,
+    ] = ("all",)
     """Which cameras to use."""
-    mask_dir: Optional[Path] = None
-    """Path to masks of dynamic objects."""
-
-    train_split_fraction: float = 0.9
-    """The percent of images to use for training. The remaining images are for eval."""
-
+    lidars: Tuple[Literal["LIDAR_TOP", "none"], ...] = ("LIDAR_TOP",)
+    """Which lidars to use. Currently only supports LIDAR_TOP"""
     verbose: bool = False
     """Load dataset with verbose messaging"""
+    annotation_interval: float = 0.5
+    """Length of time interval used to sample annotations from the dataset"""
+    include_deformable_actors: bool = True
+    """Include deformable actors in the dataset (NuScenes has many pedestrians so we default this to true)."""
+    scene_box_height = (-10, 20)
+    """The upper and lower height bounds for the scene box (m)."""
+    train_eval_split_type: SplitTypes = SplitTypes.LINSPACE
+    """Type of split to use for train/eval split."""
+    lidar_elevation_mapping: Dict[str, Dict] = field(default_factory=lambda: NUSCENES_ELEVATION_MAPPING)
+    """Elevation mapping for each lidar."""
+    skip_elevation_channels: Dict[str, Tuple] = field(default_factory=lambda: NUSCENES_SKIP_ELEVATION_CHANNELS)
+    """Channels to skip when adding missing points."""
+    lidar_azimuth_resolution: Dict[str, float] = field(default_factory=lambda: NUSCENES_AZIMUTH_RESOLUTION)
+    """Azimuth resolution for each lidar."""
+    add_missing_points: bool = True
+    """Add missing points to lidar point clouds."""
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if "scene" not in self.sequence:
+            self.sequence = "scene-" + self.sequence
 
 
 @dataclass
-class NuScenes(DataParser):
+class NuScenes(ADDataParser):
     """NuScenes DatasetParser"""
 
     config: NuScenesDataParserConfig
 
-    def _generate_dataparser_outputs(self, split="train"):
-        # nuscenes is slow to import, so we only do it if we need it.
-        from nuscenes.nuscenes import NuScenes as NuScenesDatabase
+    @property
+    def actor_transform(self) -> torch.Tensor:
+        """Nuscenes uses x-forward, so we need to rotate to x-right."""
+        return torch.from_numpy(WLH_TO_LWH)
+
+    def _get_cameras(self) -> Tuple[Cameras, List[Path]]:
+        if "all" in self.config.cameras:
+            self.config.cameras = AVAILABLE_CAMERAS
+        filenames, times, intrinsics, poses, cam2egos, idxs = [], [], [], [], [], []
+        heights, widths = [], []
+        first_sample = self.nusc.get("sample", self.scene["first_sample_token"])
+        is_key_frame = []
+        for cam_idx, camera in enumerate(["CAM_" + camera for camera in self.config.cameras]):
+            for sample_data in self._find_all_sample_data(first_sample["data"][camera]):
+                calibrated_sensor_data = self.nusc.get("calibrated_sensor", sample_data["calibrated_sensor_token"])
+                ego_pose_data = self.nusc.get("ego_pose", sample_data["ego_pose_token"])
+                ego_pose = _rotation_translation_to_pose(ego_pose_data["rotation"], ego_pose_data["translation"])
+                cam_pose = _rotation_translation_to_pose(
+                    calibrated_sensor_data["rotation"], calibrated_sensor_data["translation"]
+                )
+                cam_pose[:3, :3] = cam_pose[:3, :3] @ OPENCV_TO_NERFSTUDIO
+                pose = ego_pose @ cam_pose
+                cam2egos.append(cam_pose)
+                poses.append(pose)
+                filenames.append(self.config.data / sample_data["filename"])
+                intrinsics.append(calibrated_sensor_data["camera_intrinsic"])
+                times.append(sample_data["timestamp"] / 1e6)
+                idxs.append(cam_idx)
+                heights.append(
+                    DEFAULT_IMAGE_HEIGHT - SEQ_CAMERA_TO_BOTTOM_RIGHT_CROP[self.config.sequence][camera[4:]][0]
+                )  # :4 to remove CAM_
+                widths.append(
+                    DEFAULT_IMAGE_WIDTH - SEQ_CAMERA_TO_BOTTOM_RIGHT_CROP[self.config.sequence][camera[4:]][1]
+                )  # :4 to remove CAM_
+                is_key_frame.append(sample_data["is_key_frame"])
+
+        # To tensors
+        intrinsics = torch.tensor(np.array(intrinsics), dtype=torch.float32)
+        poses = torch.tensor(np.array(poses), dtype=torch.float32)
+        cam2egos = torch.tensor(np.array(cam2egos), dtype=torch.float32)
+        times = torch.tensor(times, dtype=torch.float64)
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+        heights = torch.tensor(heights).int()
+        widths = torch.tensor(widths).int()
+        is_key_frame = torch.tensor(is_key_frame).reshape(-1, 1).bool()
+        cameras = Cameras(
+            fx=intrinsics[:, 0, 0],
+            fy=intrinsics[:, 1, 1],
+            cx=intrinsics[:, 0, 2],
+            cy=intrinsics[:, 1, 2],
+            height=heights,
+            width=widths,
+            camera_to_worlds=poses[:, :3, :4],
+            camera_type=CameraType.PERSPECTIVE,
+            times=times,
+            metadata={"sensor_idxs": idxs, "is_key_frame": is_key_frame},
+        )
+        return cameras, filenames
+
+    def _get_lidars(self) -> Tuple[Lidars, List[Path]]:
+        lidar_filenames, times, poses, lid2egos, idxs = [], [], [], [], []
+        first_sample = self.nusc.get("sample", self.scene["first_sample_token"])
+        is_key_frame = []
+        for lidar_idx, lidar in enumerate(self.config.lidars):
+            for lidar_data in self._find_all_sample_data(first_sample["data"][lidar]):
+                calibrated_sensor_data = self.nusc.get("calibrated_sensor", lidar_data["calibrated_sensor_token"])
+                ego_pose_data = self.nusc.get("ego_pose", lidar_data["ego_pose_token"])
+                ego_pose = _rotation_translation_to_pose(ego_pose_data["rotation"], ego_pose_data["translation"])
+                lidar_pose = _rotation_translation_to_pose(
+                    calibrated_sensor_data["rotation"], calibrated_sensor_data["translation"]
+                )
+                pose = ego_pose @ lidar_pose
+
+                lidar_filenames.append(self.config.data / lidar_data["filename"])
 
-        nusc = NuScenesDatabase(
+                poses.append(pose)
+                lid2egos.append(lidar_pose)
+                times.append(lidar_data["timestamp"] / 1e6)
+                idxs.append(lidar_idx)
+                is_key_frame.append(lidar_data["is_key_frame"])
+
+        poses = torch.tensor(np.array(poses), dtype=torch.float64)  # will be changed to float32 later
+        lid2egos = torch.tensor(np.array(lid2egos), dtype=torch.float32)
+        times = torch.tensor(times, dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+
+        is_key_frame = torch.tensor(is_key_frame).reshape(-1, 1).bool()
+        lidars = Lidars(
+            lidar_to_worlds=poses[:, :3, :4],
+            lidar_type=LidarType.VELODYNE_HDL32E,
+            assume_ego_compensated=True,
+            times=times,
+            metadata={"sensor_idxs": idxs, "is_key_frame": is_key_frame},
+            horizontal_beam_divergence=HORIZONTAL_BEAM_DIVERGENCE,
+            vertical_beam_divergence=VERTICAL_BEAM_DIVERGENCE,
+            valid_lidar_distance_threshold=DUMMY_DISTANCE_VALUE / 2,
+        )
+        return lidars, lidar_filenames
+
+    def _read_lidars(self, lidars: Lidars, filepaths: List[Path]) -> List[torch.Tensor]:
+        point_clouds = []
+        for filepath in filepaths:
+            pc = np.fromfile(str(filepath), dtype=np.float32).reshape([-1, 5])
+            pc[..., 3] = pc[..., 3] / MAX_RELECTANCE_VALUE  # normalize reflectance
+            # nuscenes lidar time is the time of the end of the sweep. Add estimated times per point
+            offsets = np.repeat(np.linspace(-1 / LIDAR_FREQUENCY, 0, pc.shape[0] // LIDAR_CHANNELS), LIDAR_CHANNELS)
+            # warning: here we overwrite the beam index channel with time offsets, since we assume x,y,z,r,t format
+            channel_id = pc[..., 4].astype(np.int32)
+            pc[..., 4] = offsets
+            # concatenate pc with channel id
+            pc = np.concatenate([pc, channel_id[:, None]], axis=-1)
+            point_clouds.append(torch.from_numpy(pc))
+
+        if self.config.add_missing_points:
+            # remove ego motion compensation
+            poses = lidars.lidar_to_worlds
+            times = lidars.times.squeeze(-1)
+            missing_points = []
+            for point_cloud, l2w, time in zip(point_clouds, poses, times):
+                pc = point_cloud.clone()
+                # absolute time
+                pc[:, 4] = pc[:, 4] + time
+                # project to world frame
+                pc[..., :3] = transform_points(pc[..., :3], l2w.unsqueeze(0).to(pc))
+                # remove ego motion compensation
+                pc, interpolated_poses = self._remove_ego_motion_compensation(pc, poses, times)
+                # reset time
+                pc[:, 4] = point_cloud[:, 4].clone()
+                # transform to common lidar frame again
+                interpolated_poses = torch.matmul(
+                    pose_utils.inverse(l2w.unsqueeze(0)).float(), pose_utils.to4x4(interpolated_poses).float()
+                )
+                # move channel from index 5 to 3
+                pc = pc[..., [0, 1, 2, 5, 3, 4]]
+                # add missing points
+                missing_points.append(self._get_missing_points(pc, interpolated_poses, "LIDAR_TOP", dist_cutoff=0.05))
+
+            # add missing points to point clouds
+            point_clouds = [torch.cat([pc, missing], dim=0) for pc, missing in zip(point_clouds, missing_points)]
+
+        lidars.lidar_to_worlds = lidars.lidar_to_worlds.float()
+        return point_clouds
+
+    def _get_actor_trajectories(self) -> List[Dict]:
+        trajs = defaultdict(list)
+        curr_sample = self.nusc.get("sample", self.scene["first_sample_token"])
+        while True:
+            for box_token in curr_sample["anns"]:
+                box = self.nusc.get_box(box_token)
+                pose = np.eye(4)
+                pose[:3, :3] = box.orientation.rotation_matrix
+                pose[:3, 3] = np.array(box.center)
+                pose = pose @ WLH_TO_LWH
+                instance_token = self.nusc.get("sample_annotation", box.token)["instance_token"]
+                trajs[instance_token].append(
+                    {
+                        "pose": pose,
+                        "wlh": np.array(box.wlh),
+                        "label": box.name,
+                        "time": curr_sample["timestamp"] / 1e6,
+                    }
+                )
+            if curr_sample["next"]:
+                curr_sample = self.nusc.get("sample", curr_sample["next"])
+            else:
+                break
+        return self._traj_dict_to_list(trajs)
+
+    def _generate_dataparser_outputs(self, split="train"):
+        self.nusc = NuScenesDatabase(
             version=self.config.version,
-            dataroot=str(self.config.data_dir.absolute()),
+            dataroot=str(self.config.data.absolute()),
             verbose=self.config.verbose,
         )
-        cameras = ["CAM_" + camera for camera in self.config.cameras]
-
-        assert (
-            len(cameras) == 1
-        ), "waiting on multiple camera support"  # TODO: remove once multiple cameras are supported
-
-        # get samples for scene
-        samples = [
-            samp for samp in nusc.sample if nusc.get("scene", samp["scene_token"])["name"] == str(self.config.data)
-        ]
-
-        # sort by timestamp (only to make chronological viz easier)
-        samples.sort(key=lambda x: (x["scene_token"], x["timestamp"]))
-
-        transform1 = np.array(
-            [
-                [0, -1, 0, 0],
-                [0, 0, -1, 0],
-                [1, 0, 0, 0],
-                [0, 0, 0, 1],
-            ]
-        )
-        transform2 = np.array(
-            [
-                [0, 0, 1, 0],
-                [0, 1, 0, 0],
-                [-1, 0, 0, 0],
-                [0, 0, 0, 1],
-            ]
-        )
+        self.scene = self.nusc.get("scene", self.nusc.field2token("scene", "name", str(self.config.sequence))[0])
+        out = super()._generate_dataparser_outputs(split)
+        del self.nusc
+        del self.scene
+        return out
+
+    def _find_all_sample_data(self, sample_data_token: str):
+        """Finds all sample data from a given sample token."""
+        curr_token = sample_data_token
+        sd = self.nusc.get("sample_data", curr_token)
+        # Rewind to first sample data
+        while sd["prev"]:
+            curr_token = sd["prev"]
+            sd = self.nusc.get("sample_data", curr_token)
+        # Forward to last sample data
+        all_sample_data = [sd]
+        while sd["next"]:
+            curr_token = sd["next"]
+            sd = self.nusc.get("sample_data", curr_token)
+            all_sample_data.append(sd)
+        return all_sample_data
+
+    def _traj_dict_to_list(self, traj: dict) -> list:
+        """Convert a dictionary of lists with trajectories to a list of dictionaries with trajectories"""
+        allowed_classes: Set[str] = set(ALLOWED_RIGID_CLASSES)
+        if self.config.include_deformable_actors:
+            allowed_classes.update(ALLOWED_DEFORMABLE_CLASSES)
+        traj_out = []
+        for instance_token, traj_list in traj.items():
+            poses = torch.from_numpy(np.stack([t["pose"] for t in traj_list]).astype(np.float32))
+            times = torch.from_numpy(np.array([t["time"] for t in traj_list]))
+            dims = torch.from_numpy(np.array([t["wlh"] for t in traj_list]).astype(np.float32))
+            dims = dims.max(0).values  # take max dimensions (important for deformable objects)
+            dynamic = (poses[:, :3, 3].std(dim=0) > 0.50).any()
+            stationary = not dynamic  # TODO: maybe make this stricter
+            if stationary or not _is_label_allowed(traj_list[0]["label"], allowed_classes):
+                continue
+            traj_dict = {
+                "uuid": instance_token,
+                "label": traj_list[0]["label"],
+                "poses": poses,
+                "timestamps": times,
+                "dims": dims,
+                "stationary": stationary,
+                "symmetric": "human" not in traj_list[0]["label"],
+                "deformable": "human" in traj_list[0]["label"],
+            }
+            traj_out.append(traj_dict)
+        return traj_out
+
+    def _get_nuscenes_sample_indices(self, is_key_frame: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        train_mask = ~is_key_frame
+        eval_mask = is_key_frame
+        return train_mask.nonzero(as_tuple=True)[0], eval_mask.nonzero(as_tuple=True)[0]
+
+    def _get_train_eval_indices(self, sensors: Union[Cameras, Lidars]) -> Tuple[torch.Tensor, torch.Tensor]:
+        if self.config.train_eval_split_type == SplitTypes.LINSPACE:
+            return self._get_linspaced_indices(sensors.metadata["sensor_idxs"].squeeze(-1))
+        elif self.config.train_eval_split_type == SplitTypes.NUSCENES_SAMPLES:
+            is_key_frame = sensors.metadata["is_key_frame"]
+            del sensors.metadata["is_key_frame"]
+            return self._get_nuscenes_sample_indices(is_key_frame)
+        else:
+            raise ValueError(f"Unknown split type {self.config.train_eval_split_type}")
 
-        # get image filenames and camera data
-        image_filenames = []
-        mask_filenames = []
-        mask_dir = self.config.mask_dir if self.config.mask_dir is not None else Path("")
-        intrinsics = []
-        poses = []
-        for sample in samples:
-            for camera in cameras:
-                camera_data = nusc.get("sample_data", sample["data"][camera])
-                calibrated_sensor_data = nusc.get("calibrated_sensor", camera_data["calibrated_sensor_token"])
-                ego_pose_data = nusc.get("ego_pose", camera_data["ego_pose_token"])
-
-                ego_pose = rotation_translation_to_pose(ego_pose_data["rotation"], ego_pose_data["translation"])
-                cam_pose = rotation_translation_to_pose(
-                    calibrated_sensor_data["rotation"], calibrated_sensor_data["translation"]
-                )
-                pose = ego_pose @ cam_pose
 
-                # rotate to opencv frame
-                pose = transform1 @ pose
+def _rotation_translation_to_pose(r_quat, t_vec):
+    """Convert quaternion rotation and translation vectors to 4x4 matrix"""
 
-                # convert from opencv camera to nerfstudio camera
-                pose[0:3, 1:3] *= -1
-                pose = pose[np.array([1, 0, 2, 3]), :]
-                pose[2, :] *= -1
+    pose = np.eye(4)
 
-                # rotate to z-up in viewer
-                pose = transform2 @ pose
+    # NB: Nuscenes recommends pyquaternion, which uses scalar-first format (w x y z)
+    # https://github.com/nutonomy/nuscenes-devkit/issues/545#issuecomment-766509242
+    # https://github.com/KieranWynn/pyquaternion/blob/99025c17bab1c55265d61add13375433b35251af/pyquaternion/quaternion.py#L299
+    # https://fzheng.me/2017/11/12/quaternion_conventions_en/
+    pose[:3, :3] = pyquaternion.Quaternion(r_quat).rotation_matrix
 
-                image_filenames.append(self.config.data_dir / camera_data["filename"])
-                mask_filenames.append(
-                    mask_dir / "masks" / camera / os.path.split(camera_data["filename"])[1].replace("jpg", "png")
-                )
-                intrinsics.append(calibrated_sensor_data["camera_intrinsic"])
-                poses.append(pose)
-        poses = torch.from_numpy(np.stack(poses).astype(np.float32))
-        intrinsics = torch.from_numpy(np.array(intrinsics).astype(np.float32))
-
-        # center poses
-        poses[:, :3, 3] -= poses[:, :3, 3].mean(dim=0)
-
-        # scale poses
-        poses[:, :3, 3] /= poses[:, :3, 3].abs().max()
-
-        # filter image_filenames and poses based on train/eval split percentage
-        num_snapshots = len(samples)
-        num_train_snapshots = math.ceil(num_snapshots * self.config.train_split_fraction)
-        num_eval_snapshots = num_snapshots - num_train_snapshots
-        i_all = np.arange(num_snapshots)
-        i_train = np.linspace(
-            0, num_snapshots - 1, num_train_snapshots, dtype=int
-        )  # equally spaced training snapshots starting and ending at 0 and num_images-1
-        i_eval = np.setdiff1d(i_all, i_train)  # eval images are the remaining images
-        assert len(i_eval) == num_eval_snapshots
-        i_train = (i_train[None, :] * len(cameras) + np.arange(len(cameras))[:, None]).ravel()
-        i_eval = (i_eval[None, :] * len(cameras) + np.arange(len(cameras))[:, None]).ravel()
-        if split == "train":
-            indices = i_train
-        elif split in ["val", "test"]:
-            indices = i_eval
-        else:
-            raise ValueError(f"Unknown dataparser split {split}")
-
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        image_filenames = [image_filenames[i] for i in indices]
-        mask_filenames = [mask_filenames[i] for i in indices]
-        intrinsics = intrinsics[indices]
-        poses = poses[indices]
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = 1.0
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
+    pose[:3, 3] = t_vec
+    return pose
 
-        cameras = Cameras(
-            fx=intrinsics[:, 0, 0].detach().clone(),
-            fy=intrinsics[:, 1, 1].detach().clone(),
-            cx=intrinsics[:, 0, 2].detach().clone(),
-            cy=intrinsics[:, 1, 2].detach().clone(),
-            height=900,
-            width=1600,
-            camera_to_worlds=poses[:, :3, :4],
-            camera_type=CameraType.PERSPECTIVE,
-        )
 
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            mask_filenames=mask_filenames if self.config.mask_dir is not None else None,
-        )
-        return dataparser_outputs
+def _is_label_allowed(label: str, allowed_classes: Set[str]) -> bool:
+    """Check if label is allowed, on all possible hierarchies."""
+    split_label = label.split(".")
+    for i in range(len(split_label)):
+        if ".".join(split_label[: i + 1]) in allowed_classes:
+            return True
+    return False
diff --git a/nerfstudio/data/dataparsers/pandaset_dataparser.py b/nerfstudio/data/dataparsers/pandaset_dataparser.py
new file mode 100644
index 00000000..d6fd61fb
--- /dev/null
+++ b/nerfstudio/data/dataparsers/pandaset_dataparser.py
@@ -0,0 +1,555 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Data parser for PandaSet dataset"""
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+from functools import lru_cache
+from pathlib import Path
+from typing import Dict, List, Literal, Tuple, Type
+
+import numpy as np
+import pandas as pd
+import pyquaternion
+import torch
+import yaml
+from pandaset import DataSet
+
+from nerfstudio.cameras.cameras import Cameras, CameraType
+from nerfstudio.cameras.lidars import Lidars, LidarType
+from nerfstudio.data.dataparsers.ad_dataparser import (
+    DUMMY_DISTANCE_VALUE,
+    OPENCV_TO_NERFSTUDIO,
+    ADDataParser,
+    ADDataParserConfig,
+)
+from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
+from nerfstudio.data.utils.lidar_elevation_mappings import PANDAR64_ELEVATION_MAPPING
+from nerfstudio.utils import poses as pose_utils
+
+PANDASET_ELEVATION_MAPPING = {"Pandar64": PANDAR64_ELEVATION_MAPPING}
+
+LIDAR_NAME_TO_INDEX = {
+    "Pandar64": 0,
+    "PandarGT": 1,
+}
+
+PANDASET_SEQ_LEN = 80
+EXTRINSICS_FILE_PATH = "./nerfstudio/data/dataparsers/pandaset_extrinsics.yaml"
+MAX_RELECTANCE_VALUE = 255.0
+
+ALLOWED_RIGID_CLASSES = (
+    "Car",
+    "Pickup Truck",
+    "Medium-sized Truck",
+    "Semi-truck",
+    "Towed Object",
+    "Motorcycle",
+    "Other Vehicle - Construction Vehicle",
+    "Other Vehicle - Uncommon",
+    "Other Vehicle - Pedicab",
+    "Emergency Vehicle",
+    "Bus",
+    "Personal Mobility Device",
+    "Motorized Scooter",
+    "Bicycle",
+    "Train",
+    "Trolley",
+    "Tram / Subway",
+)
+ALLOWED_DEFORMABLE_CLASSES = (
+    "Pedestrian",
+    "Pedestrian with Object",
+)
+
+LANE_SHIFT_SIGN: Dict[str, Literal[-1, 1]] = defaultdict(lambda: -1)
+LANE_SHIFT_SIGN.update(
+    {
+        "001": -1,
+        "011": 1,
+        "016": 1,
+        "028": -1,
+        "053": 1,
+        "063": -1,
+        "084": -1,
+        "106": -1,
+        "123": -1,
+        "158": -1,
+    }
+)
+
+PANDASET_AZIMUT_RESOLUTION = {"Pandar64": 0.2}
+PANDASET_SKIP_ELEVATION_CHANNELS = {
+    "Pandar64": (
+        62,
+        63,
+    )
+}
+
+HORIZONTAL_BEAM_DIVERGENCE = 3e-3  # radians
+VERTICAL_BEAM_DIVERGENCE = 1.5e-3  # radians
+
+AVAILABLE_CAMERAS = ("front", "front_left", "front_right", "back", "left", "right")
+
+
+@dataclass
+class PandaSetDataParserConfig(ADDataParserConfig):
+    """PandaSet dataset config.
+    PandaSet (https://pandaset.org/) is an autonomous driving dataset containing 100+ 8s clips.
+    Each clip was recorded with a suite of sensors including 6 surround cameras and two lidars.
+    It also includes 3D cuboid annotations around objects.
+    """
+
+    _target: Type = field(default_factory=lambda: PandaSet)
+    """target class to instantiate"""
+    sequence: str = "001"
+    """Name of the scene."""
+    cameras: Tuple[Literal["front", "front_left", "front_right", "back", "left", "right", "none", "all"], ...] = (
+        "front",
+        "front_left",
+        "front_right",
+        "back",
+        "left",
+        "right",
+    )
+    """Which cameras to use."""
+    lidars: Tuple[Literal["Pandar64", "PandarGT", "none"], ...] = ("Pandar64",)
+    """Which lidars to use."""
+    annotation_interval: float = 0.1
+    """Interval between annotations in seconds."""
+    correct_cuboid_time: bool = True
+    """Whether to correct the cuboid time to match the actual time of observation, not the end of the lidar sweep."""
+    min_lidar_dist: Tuple[float, float, float] = (1.0, 2.0, 2.0)
+    """Pandaset lidar is x-right, y-down, z-forward."""
+    lidar_elevation_mapping: Dict[str, Dict] = field(default_factory=lambda: PANDASET_ELEVATION_MAPPING)
+    """Elevation mapping for each lidar."""
+    skip_elevation_channels: Dict[str, Tuple] = field(default_factory=lambda: PANDASET_SKIP_ELEVATION_CHANNELS)
+    """Channels to skip when adding missing points."""
+    lidar_azimuth_resolution: Dict[str, float] = field(default_factory=lambda: PANDASET_AZIMUT_RESOLUTION)
+    """Azimuth resolution for each lidar."""
+    rolling_shutter_offsets: Tuple[float, float] = (-0.03, 0.01)
+    """In pandaset the image time seems to line up with the final row."""
+
+
+@dataclass
+class PandaSet(ADDataParser):
+    """PandaSet DatasetParser"""
+
+    config: PandaSetDataParserConfig
+
+    def _get_lane_shift_sign(self, sequence: str) -> Literal[-1, 1]:
+        return LANE_SHIFT_SIGN.get(sequence, 1)
+
+    def _get_cameras(self) -> Tuple[Cameras, List[Path]]:
+        """Returns camera info and image filenames."""
+        if "all" in self.config.cameras:
+            self.config.cameras = AVAILABLE_CAMERAS
+        cameras = [cam + "_camera" for cam in self.config.cameras]
+        # get image filenames and camera data
+        image_filenames = []
+        times = []
+        intrinsics = []
+        poses = []
+        idxs = []
+        heights = []
+        for i in range(PANDASET_SEQ_LEN):
+            for camera in cameras:
+                curr_cam = self.sequence.camera[camera]
+                file_path = curr_cam._data_structure[i]
+                pose = _pandaset_pose_to_matrix(curr_cam.poses[i])
+                pose[:3, :3] = pose[:3, :3] @ OPENCV_TO_NERFSTUDIO
+                intrinsic_ = curr_cam.intrinsics
+                intrinsic = np.array(
+                    [
+                        [intrinsic_.fx, 0, intrinsic_.cx],
+                        [0, intrinsic_.fy, intrinsic_.cy],
+                        [0, 0, 1],
+                    ]
+                )
+                image_filenames.append(file_path)
+                intrinsics.append(intrinsic)
+                poses.append(pose)
+                times.append(curr_cam.timestamps[i])
+                idxs.append(cameras.index(camera))
+                heights.append(1080 - (250 if camera == "back_camera" else 0))
+
+        intrinsics = torch.tensor(np.array(intrinsics), dtype=torch.float32)
+        poses = torch.tensor(np.array(poses), dtype=torch.float32)
+        times = torch.tensor(times, dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+        cameras = Cameras(
+            fx=intrinsics[:, 0, 0],
+            fy=intrinsics[:, 1, 1],
+            cx=intrinsics[:, 0, 2],
+            cy=intrinsics[:, 1, 2],
+            height=torch.tensor(heights),
+            width=1920,
+            camera_to_worlds=poses[:, :3, :4],
+            camera_type=CameraType.PERSPECTIVE,
+            times=times,
+            metadata={"sensor_idxs": idxs},
+        )
+        return cameras, image_filenames
+
+    def _get_lidars(self) -> Tuple[Lidars, List[Path]]:
+        """Returns lidar info and loaded point clouds."""
+        poses = []
+        times = []
+        idxs = []
+        lidar_filenames = []
+        for i in range(PANDASET_SEQ_LEN):
+            # the pose information in self.sequence.lidar.poses is not correct, so we compute it from the camera pose and extrinsics
+            # the lidar scans are synced such that the middle of a scan is at the same time as the front camera image
+            front_cam = self.sequence.camera["front_camera"]
+            front_cam2w = _pandaset_pose_to_matrix(front_cam.poses[i])
+            front_cam_extrinsics = self.extrinsics["front_camera"]
+            front_cam_extrinsics["position"] = front_cam_extrinsics["extrinsic"]["transform"]["translation"]
+            front_cam_extrinsics["heading"] = front_cam_extrinsics["extrinsic"]["transform"]["rotation"]
+            l2front_cam = _pandaset_pose_to_matrix(front_cam_extrinsics)
+            l2w = torch.from_numpy(front_cam2w @ l2front_cam)
+
+            # Load point cloud
+            filename = self.sequence.lidar._data_structure[i]
+            # since we are using the front camera pose, we need to use the front camera timestamp
+            time = front_cam.timestamps[i]
+
+            for lidar in self.config.lidars:
+                lidar_idx = LIDAR_NAME_TO_INDEX[lidar]
+                lidar_filenames.append(Path(filename))
+                poses.append(l2w)
+                idxs.append(lidar_idx)
+                times.append(time)
+
+        poses = torch.stack(poses)
+        times = torch.tensor(times, dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+
+        lidars = Lidars(
+            lidar_to_worlds=poses[:, :3, :4],
+            lidar_type=LidarType.PANDAR64,
+            times=times,
+            metadata={"sensor_idxs": idxs},
+            horizontal_beam_divergence=HORIZONTAL_BEAM_DIVERGENCE,
+            vertical_beam_divergence=VERTICAL_BEAM_DIVERGENCE,
+            valid_lidar_distance_threshold=DUMMY_DISTANCE_VALUE / 2,
+        )
+        return lidars, lidar_filenames
+
+    def _read_lidars(self, lidars: Lidars, filepaths: List[Path]) -> List[torch.Tensor]:
+        point_clouds = []
+        point_clouds_in_world = []
+
+        @lru_cache(maxsize=1)
+        def read_point_cloud(filepath):
+            return pd.read_pickle(filepath).values
+
+        for i, filename in enumerate(filepaths):
+            lidar = lidars[i]
+            lidar_idx = lidars.metadata["sensor_idxs"][i]
+            l2w = pose_utils.to4x4(lidar.lidar_to_worlds)
+
+            # Load point cloud
+            point_cloud = torch.from_numpy(read_point_cloud(filename))
+            point_cloud[:, 3] /= MAX_RELECTANCE_VALUE
+            if lidar_idx == LIDAR_NAME_TO_INDEX["Pandar64"]:
+                point_clouds_in_world.append(point_cloud[point_cloud[:, -1] == LIDAR_NAME_TO_INDEX["Pandar64"], :-1])
+            points = point_cloud[:, :3]
+            # transform points from world space to sensor space
+            points = torch.hstack((points, torch.ones((points.shape[0], 1))))
+            points = (torch.matmul(torch.linalg.inv(l2w), points.T).T)[:, :3]
+            point_cloud[:, :3] = points
+
+            # and adjust the point cloud timestamps accordingly
+            point_cloud[:, 4] -= lidar.times
+
+            pc = point_cloud[point_cloud[:, -1] == lidar_idx, :-1]
+            point_clouds.append(pc.float())
+
+        assert len(point_clouds) == len(
+            lidars
+        ), f"Number of point clouds ({len(point_clouds)}) does not match number of lidars ({len(lidars)})"
+
+        if self.config.add_missing_points:
+            poses = lidars.lidar_to_worlds
+            times = lidars.times.squeeze(-1)
+
+            pc_without_ego_motion_comp = [
+                self._remove_ego_motion_compensation(pc, poses, times) for pc in point_clouds_in_world
+            ]
+            pc_without_ego_motion_comp = [
+                (self._add_channel_info(pc, dim=3, lidar_name="Pandar64"), pose)
+                for pc, pose in pc_without_ego_motion_comp
+            ]  # TODO: clean up to handle multiple lidars
+
+            # project back to joint lidar pose
+            pc_without_ego_motion_comp = [
+                (pc, torch.matmul(pose_utils.inverse(l2w.unsqueeze(0)).float(), pose_utils.to4x4(pc_poses).float()))
+                for (pc, pc_poses), l2w in zip(pc_without_ego_motion_comp, poses)
+            ]
+            missing_points = [
+                self._get_missing_points(pc, poses, "Pandar64") for pc, poses in pc_without_ego_motion_comp
+            ]
+            # drop channel info again
+            missing_points = [torch.cat([pc[:, :3], pc[:, 4:]], dim=-1) for pc in missing_points]
+            # subtracts lidar time
+            for pc, time in zip(missing_points, times):
+                pc[:, 4] -= time
+            # add missing points to point clouds
+            point_clouds = [torch.cat([pc, missing], dim=0) for pc, missing in zip(point_clouds, missing_points)]
+
+        lidars.lidar_to_worlds = lidars.lidar_to_worlds.float()
+
+        return point_clouds
+
+    def _get_actor_trajectories(self) -> List[Dict]:
+        """Returns a list of actor trajectories."""
+        allowed_classes = ALLOWED_RIGID_CLASSES
+        if self.config.include_deformable_actors:
+            allowed_classes += ALLOWED_DEFORMABLE_CLASSES
+        cuboids = []
+        for i in range(PANDASET_SEQ_LEN):
+            curr_cuboids = self.sequence.cuboids[i]
+            # Remove invalid cuboids
+            is_allowed_class = np.array([label in allowed_classes for label in curr_cuboids["label"]])
+            valid_mask = (~curr_cuboids["stationary"]) & is_allowed_class
+            curr_cuboids = curr_cuboids[valid_mask]
+            if not len(curr_cuboids):
+                continue
+
+            uuid = np.array(curr_cuboids["uuid"])
+            label = np.array(curr_cuboids["label"])
+
+            yaw = curr_cuboids["yaw"].astype(np.float32)
+            rot = _yaw_to_rotation_matrix(yaw)
+
+            stationary = np.array(curr_cuboids["stationary"], dtype=np.bool8)  # True for static objects
+            pos_x = curr_cuboids["position.x"].astype(np.float32)  # x position of cuboid in world coords
+            pos_y = curr_cuboids["position.y"].astype(np.float32)  # y position of cuboid in world coords
+            pos_z = curr_cuboids["position.z"].astype(np.float32)  # z position of cuboid in world coords
+            pos = np.vstack([pos_x, pos_y, pos_z]).T
+
+            cuboid_poses = np.eye(4)[None].repeat(len(uuid), axis=0)
+            cuboid_poses[:, :3, :3] = rot
+            cuboid_poses[:, :3, 3] = pos
+
+            width = curr_cuboids["dimensions.x"].astype(np.float32)  # width of cuboid in world coords
+            length = curr_cuboids["dimensions.y"].astype(np.float32)  # length of cuboid in world coords
+            height = curr_cuboids["dimensions.z"].astype(np.float32)  # height of cuboid in world coords
+            dims = np.vstack([width, length, height]).T
+
+            # if dynamic and visible in both 360 and front facing, two cuboids are annoated. 0 for 360, 1 for front facing, -1 otherwise
+            sensor_id = np.array(curr_cuboids["cuboids.sensor_id"], dtype=np.int32)
+            sibling_id = np.array(
+                curr_cuboids["cuboids.sibling_id"]
+            )  # uuid of sibling cuboid, i.e., if sensor_id != -1
+
+            if self.config.correct_cuboid_time:
+                # correct the cuboid time to match the actual time of observation, not the end of the lidar sweep
+                lidpose = _pandaset_pose_to_matrix(self.sequence.lidar.poses[i])
+                posinlid = pos @ lidpose[:3, :3].T + lidpose[:3, 3]
+                angle = np.arctan2(posinlid[:, 0], posinlid[:, 1]) - np.pi / 2
+                angle = (angle + np.pi) % (2 * np.pi) - np.pi
+                timediff = angle / (2 * np.pi) * np.diff(self.sequence.lidar.timestamps).mean()
+                cuboid_times = self.sequence.camera["front_camera"].timestamps[i] + timediff
+            else:
+                # assume the cuboid time matches the sequence time
+                cuboid_times = np.repeat(self.sequence.camera["front_camera"].timestamps[i], len(uuid))
+
+            for cuboid_index in range(len(uuid)):
+                cuboids.append(
+                    {
+                        "uuid": uuid[cuboid_index],
+                        "label": label[cuboid_index],
+                        "poses": cuboid_poses[cuboid_index],
+                        "stationary": stationary[cuboid_index],
+                        "dims": dims[cuboid_index],
+                        "sensor_ids": sensor_id[cuboid_index],
+                        "sibling_id": sibling_id[cuboid_index] if sensor_id[cuboid_index] != -1 else None,
+                        "timestamps": np.array(cuboid_times[cuboid_index]),
+                    }
+                )
+        return _cuboids_to_trajectories(cuboids)
+
+    def _generate_dataparser_outputs(self, split="train") -> DataparserOutputs:
+        pandaset = DataSet(str(self.config.data.absolute()))
+        self.sequence = pandaset[self.config.sequence]
+        self.sequence.load()
+        self.extrinsics = yaml.load(open(EXTRINSICS_FILE_PATH, "r"), Loader=yaml.FullLoader)
+        return super()._generate_dataparser_outputs(split)
+
+    def _add_channel_info(self, point_cloud: torch.Tensor, dim: int = -1, lidar_name: str = "") -> torch.Tensor:
+        """Infer channel id from point cloud, and add it to the point cloud.
+
+        Args:
+            point_cloud: Point cloud to add channel id to (in sensor frame). Shape: [num_points, 3+x] x,y,z (timestamp, intensity, etc.)
+
+        Returns:
+            Point cloud with channel id. Shape: [num_points, 3+x+1] x,y,z (timestamp, intensity, etc.), channel_id
+            channel_id is added to dim
+        """
+        # these are limits where channels are equally spaced
+        ELEV_HIGH_IDX = 5
+        ELEV_LOW_IDX = -11
+        ELEV_LOW_IDX_ABS = len(self.config.lidar_elevation_mapping[lidar_name]) + ELEV_LOW_IDX
+
+        dist = torch.norm(point_cloud[:, :3], dim=-1)
+        elevation = torch.arcsin(point_cloud[:, 2] / dist)
+        elevation = torch.rad2deg(elevation)
+
+        middle_elev_mask = (elevation < (self.config.lidar_elevation_mapping[lidar_name][ELEV_HIGH_IDX] + 0.2)) & (
+            elevation > (self.config.lidar_elevation_mapping[lidar_name][ELEV_LOW_IDX_ABS] - 0.2)
+        )
+        middle_elev = elevation[middle_elev_mask]
+
+        histc, bin_edges = torch.histogram(middle_elev, bins=2000)
+
+        # channels should be equally spaced
+        expected_channel_edges = (bin_edges[-1] - bin_edges[0]) / 49 * torch.arange(50) + bin_edges[0]
+
+        res = (
+            self.config.lidar_elevation_mapping[lidar_name][ELEV_HIGH_IDX]
+            - self.config.lidar_elevation_mapping[lidar_name][ELEV_HIGH_IDX + 1]
+        )
+
+        # find consecutive empty bins in histogram
+        empty_bins = []
+        empty_bin = []
+        empty_bins_edges = []
+        for i in range(len(histc)):
+            if histc[i] == 0:
+                empty_bin.append(i)
+            else:
+                if len(empty_bin) > 0:
+                    empty_bins.append(empty_bin)
+                    empty_bins_edges.append((bin_edges[empty_bin[0]], bin_edges[empty_bin[-1] + 1]))
+                    empty_bin = []
+
+        # find channel edges, use first expected for init
+        found_channel_edges = [expected_channel_edges[0].tolist()]
+        empty_bins_edges = torch.tensor(empty_bins_edges)
+        for i, edge in enumerate(expected_channel_edges[1:-1]):
+            found_edge = False
+            for empty_bin in empty_bins_edges:
+                # if edge is in empty bin, keep the edge as is
+                if edge > empty_bin[0] and edge < empty_bin[1]:
+                    found_channel_edges.append(edge.tolist())
+                    found_edge = True
+                    break
+            if found_edge:
+                continue
+            distances = torch.abs(edge - empty_bins_edges)
+            min_dist_idx = distances.argmin()
+            if distances.flatten()[min_dist_idx] < 0.03:
+                found_channel_edges.append(empty_bins_edges.flatten()[min_dist_idx].tolist())
+                continue
+
+        found_channel_edges.append(expected_channel_edges[-1].tolist())
+        found_channel_edges = torch.tensor(found_channel_edges)
+
+        if len(found_channel_edges) < len(expected_channel_edges):
+            # we have missing channels, interpolate edges
+            while (num_missing_edges := len(expected_channel_edges) - len(found_channel_edges)) > 0:
+                distances = found_channel_edges.diff().abs()
+                max_dist_idx = distances.argmax()
+                num_edges_to_insert = max((distances[max_dist_idx] / res).round().int() - 1, 1)
+                num_edges_to_insert = min(num_missing_edges, num_edges_to_insert)
+                new_edges = torch.linspace(
+                    found_channel_edges[max_dist_idx], found_channel_edges[max_dist_idx + 1], num_edges_to_insert + 2
+                )[1:-1]
+                found_channel_edges = torch.cat(
+                    [found_channel_edges[: max_dist_idx + 1], new_edges, found_channel_edges[max_dist_idx + 1 :]]
+                )  # insert new edges
+
+        # add remaining edges
+        for i in range(len(self.config.lidar_elevation_mapping[lidar_name])):
+            if i >= ELEV_HIGH_IDX and i <= len(self.config.lidar_elevation_mapping[lidar_name]) + ELEV_LOW_IDX:
+                continue
+            current_elevation = self.config.lidar_elevation_mapping[lidar_name][i]
+            if i == 0:
+                new_edge = 100
+            elif i == len(self.config.lidar_elevation_mapping[lidar_name]) - 1:
+                new_edge = -100
+            elif i < ELEV_HIGH_IDX:
+                dist_to_prev_elevation = abs(current_elevation - self.config.lidar_elevation_mapping[lidar_name][i - 1])
+                new_edge = current_elevation + dist_to_prev_elevation * 0.22
+            elif i > len(self.config.lidar_elevation_mapping[lidar_name]) + ELEV_LOW_IDX:
+                dist_to_next_elevation = (
+                    abs(current_elevation - self.config.lidar_elevation_mapping[lidar_name][i + 1])
+                    if i < len(self.config.lidar_elevation_mapping[lidar_name]) - 1
+                    else 1000.0
+                )
+                new_edge = current_elevation - dist_to_next_elevation * 0.22
+
+            found_channel_edges = torch.cat([found_channel_edges, torch.tensor([new_edge]).float()])
+
+        found_channel_edges, _ = torch.sort(found_channel_edges, descending=True)
+        channel_id = torch.full((point_cloud.shape[0], 1), -1, device=point_cloud.device)
+
+        # assign channel id
+        for i in range(len(self.config.lidar_elevation_mapping[lidar_name])):
+            elevation_mask = (elevation >= found_channel_edges[i + 1]) & (elevation < found_channel_edges[i])
+            channel_id[elevation_mask] = i
+
+        point_cloud = torch.cat([point_cloud[:, :dim], channel_id, point_cloud[:, dim:]], dim=-1)
+        return point_cloud
+
+
+def _pandaset_pose_to_matrix(pose):
+    translation = np.array([pose["position"]["x"], pose["position"]["y"], pose["position"]["z"]])
+    quaternion = np.array([pose["heading"]["w"], pose["heading"]["x"], pose["heading"]["y"], pose["heading"]["z"]])
+    pose = np.eye(4)
+    pose[:3, :3] = pyquaternion.Quaternion(quaternion).rotation_matrix
+    pose[:3, 3] = translation
+    return pose
+
+
+def _yaw_to_rotation_matrix(yaw: np.ndarray):
+    """Converts array of yaw angles to rotation matrices."""
+    rotation_matrices = np.zeros((yaw.shape[0], 3, 3))
+    rotation_matrices[:, 0, 0] = np.cos(yaw)
+    rotation_matrices[:, 0, 1] = -np.sin(yaw)
+    rotation_matrices[:, 1, 0] = np.sin(yaw)
+    rotation_matrices[:, 1, 1] = np.cos(yaw)
+    rotation_matrices[:, 2, 2] = 1
+    return rotation_matrices
+
+
+def _cuboids_to_trajectories(cuboids):
+    """Connects cuboids into trajectories."""
+    trajs = []
+    trajs_dict = {}
+    for cuboid in cuboids:
+        if cuboid["sensor_ids"] == 1:  # TODO: allow for cuboids from front-facing lidar
+            continue  # skip cuboids from front-facing lidar
+
+        if cuboid["uuid"] not in trajs_dict:
+            trajs_dict[cuboid["uuid"]] = []
+
+        trajs_dict[cuboid["uuid"]] += [cuboid]
+
+    for uuid, traj in trajs_dict.items():
+        trajs_dict[uuid] = sorted(traj, key=lambda x: x["timestamps"])
+        trajs.append(
+            {
+                # "uuid": uuid,
+                "poses": torch.from_numpy(np.stack([t["poses"] for t in traj])).float(),
+                "timestamps": torch.from_numpy(np.stack([t["timestamps"] for t in traj])),
+                "dims": torch.from_numpy(np.array([t["dims"] for t in traj]).astype(np.float32).max(axis=0)),
+                "label": traj[0]["label"],
+                "stationary": traj[0]["stationary"],
+                "symmetric": "Pedestrian" not in traj[0]["label"],
+                "deformable": "Pedestrian" in traj[0]["label"],
+            }
+        )
+    return trajs
diff --git a/nerfstudio/data/dataparsers/pandaset_extrinsics.yaml b/nerfstudio/data/dataparsers/pandaset_extrinsics.yaml
new file mode 100644
index 00000000..9b32bf94
--- /dev/null
+++ b/nerfstudio/data/dataparsers/pandaset_extrinsics.yaml
@@ -0,0 +1,65 @@
+back_camera:
+  extrinsic:
+    transform:
+      rotation: {w: 0.713789231075861, x: 0.7003585531940812, y: -0.001595758695393934,
+        z: -0.0005330311533742299}
+      translation: {x: -0.0004217634029916384, y: -0.21683144949675118, z: -1.0553445472201475}
+  intrinsic:
+    D: [-0.1619, 0.0113, -0.00028815, -7.9827e-05, 0.0067]
+    K: [933.4667, 0, 896.4692, 0, 934.6754, 507.3557, 0, 0, 1]
+front_camera:
+  extrinsic:
+    transform:
+      rotation: {w: 0.016213200031258722, x: 0.0030578899383849464, y: 0.7114721800418571,
+        z: -0.7025205466606356}
+      translation: {x: 0.0002585796504896516, y: -0.03907777167811011, z: -0.0440125762408362}
+  intrinsic:
+    D: [-0.5894, 0.66, 0.0011, -0.001, -1.0088]
+    K: [1970.0131, 0, 970.0002, 0, 1970.0091, 483.2988, 0, 0, 1]
+front_gt:
+  extrinsic:
+    transform:
+      rotation: {w: 0.021475754959146356, x: -0.002060907279494794, y: 0.01134678181520767,
+        z: 0.9997028534282365}
+      translation: {x: -0.000451117754, y: -0.605646431446, z: -0.301525235176}
+front_left_camera:
+  extrinsic:
+    transform:
+      rotation: {w: 0.33540022607039827, x: 0.3277491469609924, y: -0.6283486651480494,
+        z: 0.6206973014480826}
+      translation: {x: -0.25842240863267835, y: -0.3070654284505582, z: -0.9244245686318884}
+  intrinsic:
+    D: [-0.165, 0.0099, -0.00075376, 5.3699e-05, 0.01]
+    K: [929.8429, 0, 972.1794, 0, 930.0592, 508.0057, 0, 0, 1]
+front_right_camera:
+  extrinsic:
+    transform:
+      rotation: {w: 0.3537633879725252, x: 0.34931795852655334, y: 0.6120314641083645,
+        z: -0.6150170047424814}
+      translation: {x: 0.2546935700219631, y: -0.24929449717803095, z: -0.8686597280810242}
+  intrinsic:
+    D: [-0.1614, -0.0027, -0.00029662, -0.00028927, 0.0181]
+    K: [930.0407, 0, 965.0525, 0, 930.0324, 463.4161, 0, 0, 1]
+left_camera:
+  extrinsic:
+    transform:
+      rotation: {w: 0.5050391917998245, x: 0.49253073152800625, y: -0.4989265501075421,
+        z: 0.503409565706149}
+      translation: {x: 0.23864835336611942, y: -0.2801448284013492, z: -0.5376795959387791}
+  intrinsic:
+    D: [-0.1582, -0.0266, -0.00015221, 0.00059011, 0.0449]
+    K: [930.4514, 0, 991.6883, 0, 930.0891, 541.6057, 0, 0, 1]
+main_pandar64:
+  extrinsic:
+    transform:
+      rotation: {w: 1.0, x: 0.0, y: 0.0, z: 0.0}
+      translation: {x: 0.0, y: 0.0, z: 0.0}
+right_camera:
+  extrinsic:
+    transform:
+      rotation: {w: 0.5087448402081216, x: 0.4947520981649951, y: 0.4977829953071897,
+        z: -0.49860920419297333}
+      translation: {x: -0.23097163411257893, y: -0.30843497058841024, z: -0.6850441215571058}
+  intrinsic:
+    D: [-0.1648, 0.0191, 0.0027, -8.5282e-07, -9.6983e-05]
+    K: [922.5465, 0, 945.057, 0, 922.4229, 517.575, 0, 0, 1]
diff --git a/nerfstudio/data/dataparsers/phototourism_dataparser.py b/nerfstudio/data/dataparsers/phototourism_dataparser.py
deleted file mode 100644
index 538bad2f..00000000
--- a/nerfstudio/data/dataparsers/phototourism_dataparser.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Phototourism dataset parser. Datasets and documentation here: http://phototour.cs.washington.edu/datasets/"""
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Literal, Type
-
-import numpy as np
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-
-# TODO(1480) use pycolmap instead of colmap_parsing_utils
-# import pycolmap
-from nerfstudio.data.utils.colmap_parsing_utils import read_cameras_binary, read_images_binary
-from nerfstudio.utils.rich_utils import CONSOLE
-
-
-@dataclass
-class PhototourismDataParserConfig(DataParserConfig):
-    """Phototourism dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: Phototourism)
-    """target class to instantiate"""
-    data: Path = Path("data/phototourism/brandenburg-gate")
-    """Directory specifying location of data."""
-    scale_factor: float = 3.0
-    """How much to scale the camera origins by."""
-    alpha_color: str = "white"
-    """alpha color of background"""
-    train_split_fraction: float = 0.9
-    """The fraction of images to use for training. The remaining images are for eval."""
-    scene_scale: float = 1.0
-    """How much to scale the region of interest by."""
-    orientation_method: Literal["pca", "up", "vertical", "none"] = "up"
-    """The method to use for orientation."""
-    center_method: Literal["poses", "focus", "none"] = "poses"
-    """The method to use to center the poses."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-
-
-@dataclass
-class Phototourism(DataParser):
-    """Phototourism dataset. This is based on https://github.com/kwea123/nerf_pl/blob/nerfw/datasets/phototourism.py
-    and uses colmap's utils file to read the poses.
-    """
-
-    config: PhototourismDataParserConfig
-
-    def __init__(self, config: PhototourismDataParserConfig):
-        super().__init__(config=config)
-        self.data: Path = config.data
-
-    def _generate_dataparser_outputs(self, split="train"):
-        image_filenames = []
-        poses = []
-
-        with CONSOLE.status(f"[bold green]Reading phototourism images and poses for {split} split...") as _:
-            # TODO(1480) use pycolmap
-            # recon = pycolmap.Reconstruction(self.data / "dense" / "sparse")
-            # cams = recon.cameras
-            # imgs = recon.images
-            cams = read_cameras_binary(self.data / "dense/sparse/cameras.bin")
-            imgs = read_images_binary(self.data / "dense/sparse/images.bin")
-
-        poses = []
-        fxs = []
-        fys = []
-        cxs = []
-        cys = []
-        image_filenames = []
-
-        flip = torch.eye(3)
-        flip[0, 0] = -1.0
-        flip = flip.double()
-
-        for _id, cam in cams.items():
-            img = imgs[_id]
-
-            assert cam.model == "PINHOLE", "Only pinhole (perspective) camera model is supported at the moment"
-
-            pose = torch.cat([torch.tensor(img.qvec2rotmat()), torch.tensor(img.tvec.reshape(3, 1))], dim=1)
-            pose = torch.cat([pose, torch.tensor([[0.0, 0.0, 0.0, 1.0]])], dim=0)
-            poses.append(torch.linalg.inv(pose))
-            fxs.append(torch.tensor(cam.params[0]))
-            fys.append(torch.tensor(cam.params[1]))
-            cxs.append(torch.tensor(cam.params[2]))
-            cys.append(torch.tensor(cam.params[3]))
-
-            image_filenames.append(self.data / "dense/images" / img.name)
-
-        poses = torch.stack(poses).float()
-        poses[..., 1:3] *= -1
-        fxs = torch.stack(fxs).float()
-        fys = torch.stack(fys).float()
-        cxs = torch.stack(cxs).float()
-        cys = torch.stack(cys).float()
-
-        # filter image_filenames and poses based on train/eval split percentage
-        num_images = len(image_filenames)
-        num_train_images = math.ceil(num_images * self.config.train_split_fraction)
-        num_eval_images = num_images - num_train_images
-        i_all = np.arange(num_images)
-        i_train = np.linspace(
-            0, num_images - 1, num_train_images, dtype=int
-        )  # equally spaced training images starting and ending at 0 and num_images-1
-        i_eval = np.setdiff1d(i_all, i_train)  # eval images are the remaining images
-        i_all = torch.tensor(i_all)
-        i_train = torch.tensor(i_train, dtype=torch.long)
-        i_eval = torch.tensor(i_eval, dtype=torch.long)
-        assert len(i_eval) == num_eval_images
-        if split == "train":
-            indices = i_train
-        elif split in ["val", "test"]:
-            indices = i_eval
-        else:
-            raise ValueError(f"Unknown dataparser split {split}")
-
-        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
-            poses, method=self.config.orientation_method, center_method=self.config.center_method
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
-        scale_factor *= self.config.scale_factor
-
-        poses[:, :3, 3] *= scale_factor
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        cameras = Cameras(
-            camera_to_worlds=poses[:, :3, :4],
-            fx=fxs,
-            fy=fys,
-            cx=cxs,
-            cy=cys,
-            camera_type=CameraType.PERSPECTIVE,
-        )
-
-        cameras = cameras[indices]
-        image_filenames = [image_filenames[i] for i in indices]
-
-        assert len(cameras) == len(image_filenames)
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            dataparser_scale=scale_factor,
-            dataparser_transform=transform_matrix,
-        )
-
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/scannet_dataparser.py b/nerfstudio/data/dataparsers/scannet_dataparser.py
deleted file mode 100644
index eb0c9ae1..00000000
--- a/nerfstudio/data/dataparsers/scannet_dataparser.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for ScanNet dataset"""
-import math
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Literal, Type
-
-import cv2
-import numpy as np
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-
-
-@dataclass
-class ScanNetDataParserConfig(DataParserConfig):
-    """ScanNet dataset config.
-    ScanNet dataset (https://www.scan-net.org/) is a large-scale 3D dataset of indoor scenes.
-    This dataparser assumes that the dense stream was extracted from .sens files.
-    Expected structure of scene directory:
-
-    .. code-block:: text
-
-        root/
-        β”œβ”€β”€ color/
-        β”œβ”€β”€ depth/
-        β”œβ”€β”€ intrinsic/
-        β”œβ”€β”€ pose/
-    """
-
-    _target: Type = field(default_factory=lambda: ScanNet)
-    """target class to instantiate"""
-    data: Path = Path("data/scannet/scene0423_02")
-    """Path to ScanNet folder with densely extracted scenes."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    scene_scale: float = 1.0
-    """How much to scale the region of interest by."""
-    center_method: Literal["poses", "focus", "none"] = "poses"
-    """The method to use to center the poses."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-    train_split_fraction: float = 0.9
-    """The fraction of images to use for training. The remaining images are for eval."""
-    depth_unit_scale_factor: float = 1e-3
-    """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion."""
-
-
-@dataclass
-class ScanNet(DataParser):
-    """ScanNet DatasetParser"""
-
-    config: ScanNetDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        image_dir = self.config.data / "color"
-        depth_dir = self.config.data / "depth"
-        pose_dir = self.config.data / "pose"
-
-        img_dir_sorted = list(sorted(image_dir.iterdir(), key=lambda x: int(x.name.split(".")[0])))
-        depth_dir_sorted = list(sorted(depth_dir.iterdir(), key=lambda x: int(x.name.split(".")[0])))
-        pose_dir_sorted = list(sorted(pose_dir.iterdir(), key=lambda x: int(x.name.split(".")[0])))
-
-        first_img = cv2.imread(str(img_dir_sorted[0].absolute()))  # type: ignore
-        h, w, _ = first_img.shape
-
-        image_filenames, depth_filenames, intrinsics, poses = [], [], [], []
-
-        K = np.loadtxt(self.config.data / "intrinsic" / "intrinsic_color.txt")
-        for img, depth, pose in zip(img_dir_sorted, depth_dir_sorted, pose_dir_sorted):
-            pose = np.loadtxt(pose)
-            pose = np.array(pose).reshape(4, 4)
-            pose[:3, 1] *= -1
-            pose[:3, 2] *= -1
-            pose = torch.from_numpy(pose).float()
-            # We cannot accept files directly, as some of the poses are invalid
-            if np.isinf(pose).any():
-                continue
-
-            poses.append(pose)
-            intrinsics.append(K)
-            image_filenames.append(img)
-            depth_filenames.append(depth)
-
-        # filter image_filenames and poses based on train/eval split percentage
-        num_images = len(image_filenames)
-        num_train_images = math.ceil(num_images * self.config.train_split_fraction)
-        num_eval_images = num_images - num_train_images
-        i_all = np.arange(num_images)
-        i_train = np.linspace(
-            0, num_images - 1, num_train_images, dtype=int
-        )  # equally spaced training images starting and ending at 0 and num_images-1
-        i_eval = np.setdiff1d(i_all, i_train)  # eval images are the remaining images
-        assert len(i_eval) == num_eval_images
-        if split == "train":
-            indices = i_train
-        elif split in ["val", "test"]:
-            indices = i_eval
-        else:
-            raise ValueError(f"Unknown dataparser split {split}")
-
-        poses = torch.from_numpy(np.stack(poses).astype(np.float32))
-        intrinsics = torch.from_numpy(np.stack(intrinsics).astype(np.float32))
-
-        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
-            poses,
-            method="none",
-            center_method=self.config.center_method,
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
-        scale_factor *= self.config.scale_factor
-
-        poses[:, :3, 3] *= scale_factor
-
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        image_filenames = [image_filenames[i] for i in indices]
-        depth_filenames = [depth_filenames[i] for i in indices] if len(depth_filenames) > 0 else []
-        intrinsics = intrinsics[indices.tolist()]
-        poses = poses[indices.tolist()]
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        cameras = Cameras(
-            fx=intrinsics[:, 0, 0],
-            fy=intrinsics[:, 1, 1],
-            cx=intrinsics[:, 0, 2],
-            cy=intrinsics[:, 1, 2],
-            height=h,
-            width=w,
-            camera_to_worlds=poses[:, :3, :4],
-            camera_type=CameraType.PERSPECTIVE,
-        )
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            dataparser_scale=scale_factor,
-            dataparser_transform=transform_matrix,
-            metadata={
-                "depth_filenames": depth_filenames if len(depth_filenames) > 0 else None,
-                "depth_unit_scale_factor": self.config.depth_unit_scale_factor,
-            },
-        )
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/scannetpp_dataparser.py b/nerfstudio/data/dataparsers/scannetpp_dataparser.py
deleted file mode 100644
index 18afa4f4..00000000
--- a/nerfstudio/data/dataparsers/scannetpp_dataparser.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Data parser for ScanNet++ datasets. """
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Literal, Type
-
-import numpy as np
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import CAMERA_MODEL_TO_TYPE, Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.io import load_from_json
-from nerfstudio.utils.rich_utils import CONSOLE
-
-
-@dataclass
-class ScanNetppDataParserConfig(DataParserConfig):
-    """ScanNet++ dataset config.
-    ScanNet++ dataset (https://kaldir.vc.in.tum.de/scannetpp/) is a real-world 3D indoor dataset for semantics understanding and novel view synthesis.
-    This dataparser follow the file structure of the dataset.
-    Expected structure of the directory:
-
-    .. code-block:: text
-
-        root/
-        β”œβ”€β”€ SCENE_ID0
-            β”œβ”€β”€ dslr
-                β”œβ”€β”€ resized_images
-                β”œβ”€β”€ resized_anon_masks
-                β”œβ”€β”€ nerfstudio/transforms.json
-        β”œβ”€β”€ SCENE_ID1/
-        ...
-    """
-
-    _target: Type = field(default_factory=lambda: ScanNetpp)
-    """target class to instantiate"""
-    data: Path = Path("scannetpp/410c470782")
-    """Directory to the root of the data."""
-    scale_factor: float = 1.0
-    """How much to scale the camera origins by."""
-    scene_scale: float = 1.5
-    """How much to scale the region of interest by. Default is 1.5 since the cameras are inside the rooms."""
-    orientation_method: Literal["pca", "up", "vertical", "none"] = "up"
-    """The method to use for orientation."""
-    center_method: Literal["poses", "focus", "none"] = "poses"
-    """The method to use to center the poses."""
-    auto_scale_poses: bool = True
-    """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-    images_dir: Path = Path("dslr/resized_images")
-    """Relative path to the images directory (default: resized_images)"""
-    masks_dir: Path = Path("dslr/resized_anon_masks")
-    """Relative path to the masks directory (default: resized_anon_masks)"""
-    transforms_path: Path = Path("dslr/nerfstudio/transforms.json")
-    """Relative path to the transforms.json file"""
-
-
-@dataclass
-class ScanNetpp(DataParser):
-    """ScanNet++ DatasetParser"""
-
-    config: ScanNetppDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        assert self.config.data.exists(), f"Data directory {self.config.data} does not exist."
-        meta = load_from_json(self.config.data / self.config.transforms_path)
-        data_dir = self.config.data / self.config.images_dir
-        mask_dir = self.config.data / self.config.masks_dir
-
-        image_filenames = []
-        mask_filenames = []
-        poses = []
-        i_train = []
-        i_eval = []
-        # sort the frames by fname
-        frames = meta["frames"] + meta["test_frames"]
-        test_frames = [f["file_path"] for f in meta["test_frames"]]
-        frames.sort(key=lambda x: x["file_path"])
-
-        for idx, frame in enumerate(frames):
-            filepath = Path(frame["file_path"])
-            fname = data_dir / filepath
-
-            image_filenames.append(fname)
-            poses.append(np.array(frame["transform_matrix"]))
-            if meta.get("has_mask", True) and "mask_path" in frame:
-                mask_filepath = Path(frame["mask_path"])
-                mask_fname = mask_dir / mask_filepath
-                mask_filenames.append(mask_fname)
-
-            if frame["file_path"] in test_frames:
-                i_eval.append(idx)
-            else:
-                i_train.append(idx)
-
-        assert len(mask_filenames) == 0 or (len(mask_filenames) == len(image_filenames)), """
-        Different number of image and mask filenames.
-        You should check that mask_path is specified for every frame (or zero frames) in transforms.json.
-        """
-
-        if split == "train":
-            indices = i_train
-        elif split in ["val", "test"]:
-            indices = i_eval
-        else:
-            raise ValueError(f"Unknown dataparser split {split}")
-
-        if "orientation_override" in meta:
-            orientation_method = meta["orientation_override"]
-            CONSOLE.log(f"[yellow] Dataset is overriding orientation method to {orientation_method}")
-        else:
-            orientation_method = self.config.orientation_method
-
-        poses = torch.from_numpy(np.array(poses).astype(np.float32))
-        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
-            poses,
-            method=orientation_method,
-            center_method=self.config.center_method,
-        )
-
-        # Scale poses
-        scale_factor = 1.0
-        if self.config.auto_scale_poses:
-            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
-        scale_factor *= self.config.scale_factor
-
-        poses[:, :3, 3] *= scale_factor
-
-        # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
-        image_filenames = [image_filenames[i] for i in indices]
-        mask_filenames = [mask_filenames[i] for i in indices] if len(mask_filenames) > 0 else []
-
-        idx_tensor = torch.tensor(indices, dtype=torch.long)
-        poses = poses[idx_tensor]
-
-        # in x,y,z order
-        # assumes that the scene is centered at the origin
-        if not self.config.auto_scale_poses:
-            # Set aabb_scale to scene_scale * the max of the absolute values of the poses
-            aabb_scale = self.config.scene_scale * float(torch.max(torch.abs(poses[:, :3, 3])))
-        else:
-            aabb_scale = self.config.scene_scale
-        scene_box = SceneBox(
-            aabb=torch.tensor(
-                [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
-            )
-        )
-
-        if "camera_model" in meta:
-            camera_type = CAMERA_MODEL_TO_TYPE[meta["camera_model"]]
-        else:
-            camera_type = CameraType.PERSPECTIVE
-
-        fx = float(meta["fl_x"])
-        fy = float(meta["fl_y"])
-        cx = float(meta["cx"])
-        cy = float(meta["cy"])
-        height = int(meta["h"])
-        width = int(meta["w"])
-        distortion_params = camera_utils.get_distortion_params(
-            k1=float(meta["k1"]) if "k1" in meta else 0.0,
-            k2=float(meta["k2"]) if "k2" in meta else 0.0,
-            k3=float(meta["k3"]) if "k3" in meta else 0.0,
-            k4=float(meta["k4"]) if "k4" in meta else 0.0,
-            p1=float(meta["p1"]) if "p1" in meta else 0.0,
-            p2=float(meta["p2"]) if "p2" in meta else 0.0,
-        )
-
-        cameras = Cameras(
-            fx=fx,
-            fy=fy,
-            cx=cx,
-            cy=cy,
-            distortion_params=distortion_params,
-            height=height,
-            width=width,
-            camera_to_worlds=poses[:, :3, :4],
-            camera_type=camera_type,
-        )
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
-            dataparser_scale=scale_factor,
-            dataparser_transform=transform_matrix,
-            metadata={},
-        )
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/sdfstudio_dataparser.py b/nerfstudio/data/dataparsers/sdfstudio_dataparser.py
deleted file mode 100644
index 540aa65d..00000000
--- a/nerfstudio/data/dataparsers/sdfstudio_dataparser.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Datapaser for sdfstudio formatted data"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Type
-
-import torch
-
-from nerfstudio.cameras import camera_utils
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.io import load_from_json
-
-
-@dataclass
-class SDFStudioDataParserConfig(DataParserConfig):
-    """Scene dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: SDFStudio)
-    """target class to instantiate"""
-    data: Path = Path("data/DTU/scan65")
-    """Directory specifying location of data."""
-    include_mono_prior: bool = False
-    """whether or not to load monocular depth and normal """
-    depth_unit_scale_factor: float = 1e-3
-    """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion."""
-    include_foreground_mask: bool = False
-    """whether or not to load foreground mask"""
-    downscale_factor: int = 1
-    scene_scale: float = 2.0
-    """
-    Sets the bounding cube to have edge length of this size.
-    The longest dimension of the axis-aligned bbox will be scaled to this value.
-    """
-    skip_every_for_val_split: int = 1
-    """sub sampling validation images"""
-    auto_orient: bool = True
-
-
-@dataclass
-class SDFStudio(DataParser):
-    """SDFStudio Dataset"""
-
-    config: SDFStudioDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        # load meta data
-        meta = load_from_json(self.config.data / "meta_data.json")
-
-        indices = list(range(len(meta["frames"])))
-        # subsample to avoid out-of-memory for validation set
-        if split != "train" and self.config.skip_every_for_val_split >= 1:
-            indices = indices[:: self.config.skip_every_for_val_split]
-
-        image_filenames = []
-        depth_filenames = []
-        normal_filenames = []
-        transform = None
-        fx = []
-        fy = []
-        cx = []
-        cy = []
-        camera_to_worlds = []
-        for i, frame in enumerate(meta["frames"]):
-            if i not in indices:
-                continue
-
-            image_filename = self.config.data / frame["rgb_path"]
-            depth_filename = frame.get("mono_depth_path")
-            normal_filename = frame.get("mono_normal_path")
-
-            intrinsics = torch.tensor(frame["intrinsics"])
-            camtoworld = torch.tensor(frame["camtoworld"])
-
-            # append data
-            image_filenames.append(image_filename)
-            if depth_filename is not None and normal_filename is not None:
-                depth_filenames.append(self.config.data / depth_filename)
-                normal_filenames.append(self.config.data / normal_filename)
-            fx.append(intrinsics[0, 0])
-            fy.append(intrinsics[1, 1])
-            cx.append(intrinsics[0, 2])
-            cy.append(intrinsics[1, 2])
-            camera_to_worlds.append(camtoworld)
-
-        fx = torch.stack(fx)
-        fy = torch.stack(fy)
-        cx = torch.stack(cx)
-        cy = torch.stack(cy)
-        c2w_colmap = torch.stack(camera_to_worlds)
-        camera_to_worlds = torch.stack(camera_to_worlds)
-
-        # Convert from COLMAP's/OPENCV's camera coordinate system to nerfstudio
-        camera_to_worlds[:, 0:3, 1:3] *= -1
-
-        if self.config.auto_orient:
-            camera_to_worlds, transform = camera_utils.auto_orient_and_center_poses(
-                camera_to_worlds,
-                method="up",
-                center_method="none",
-            )
-
-        # scene box from meta data
-        meta_scene_box = meta["scene_box"]
-        aabb = torch.tensor(meta_scene_box["aabb"], dtype=torch.float32)
-        scene_box = SceneBox(
-            aabb=aabb,
-        )
-
-        height, width = meta["height"], meta["width"]
-        cameras = Cameras(
-            fx=fx,
-            fy=fy,
-            cx=cx,
-            cy=cy,
-            height=height,
-            width=width,
-            camera_to_worlds=camera_to_worlds[:, :3, :4],
-            camera_type=CameraType.PERSPECTIVE,
-        )
-
-        # TODO supports downsample
-        # cameras.rescale_output_resolution(scaling_factor=1.0 / self.config.downscale_factor)
-        if self.config.include_mono_prior:
-            assert meta["has_mono_prior"], f"no mono prior in {self.config.data}"
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            metadata={
-                "depth_filenames": depth_filenames if len(depth_filenames) > 0 else None,
-                "normal_filenames": normal_filenames if len(normal_filenames) > 0 else None,
-                "transform": transform,
-                # required for normal maps, these are in colmap format so they require c2w before conversion
-                "camera_to_worlds": c2w_colmap if len(c2w_colmap) > 0 else None,
-                "include_mono_prior": self.config.include_mono_prior,
-                "depth_unit_scale_factor": self.config.depth_unit_scale_factor,
-            },
-        )
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/sitcoms3d_dataparser.py b/nerfstudio/data/dataparsers/sitcoms3d_dataparser.py
deleted file mode 100644
index 221a76cf..00000000
--- a/nerfstudio/data/dataparsers/sitcoms3d_dataparser.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data parser for sitcoms3D dataset.
-
-The dataset is from the paper ["The One Where They Reconstructed 3D Humans and
-Environments in TV Shows"](https://ethanweber.me/sitcoms3D/)
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Type
-
-import torch
-
-from nerfstudio.cameras.cameras import Cameras, CameraType
-from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs, Semantics
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.io import load_from_json
-
-
-@dataclass
-class Sitcoms3DDataParserConfig(DataParserConfig):
-    """sitcoms3D dataset parser config"""
-
-    _target: Type = field(default_factory=lambda: Sitcoms3D)
-    """target class to instantiate"""
-    data: Path = Path("data/sitcoms3d/TBBT-big_living_room")
-    """Directory specifying location of data."""
-    include_semantics: bool = True
-    """whether or not to include loading of semantics data"""
-    downscale_factor: int = 4
-    scene_scale: float = 2.0
-    """
-    Sets the bounding cube to have edge length of this size.
-    The longest dimension of the Sitcoms3D axis-aligned bbox will be scaled to this value.
-    """
-
-
-@dataclass
-class Sitcoms3D(DataParser):
-    """Sitcoms3D Dataset"""
-
-    config: Sitcoms3DDataParserConfig
-
-    def _generate_dataparser_outputs(self, split="train"):
-        cameras_json = load_from_json(self.config.data / "cameras.json")
-        frames = cameras_json["frames"]
-        bbox = torch.tensor(cameras_json["bbox"])
-
-        downscale_suffix = f"_{self.config.downscale_factor}" if self.config.downscale_factor != 1 else ""
-        images_folder = f"images{downscale_suffix}"
-        segmentations_folder = f"segmentations{downscale_suffix}"
-
-        image_filenames = []
-        fx = []
-        fy = []
-        cx = []
-        cy = []
-        camera_to_worlds = []
-        for frame in frames:
-            # unpack data
-            image_filename = self.config.data / images_folder / frame["image_name"]
-            intrinsics = torch.tensor(frame["intrinsics"])
-            camtoworld = torch.tensor(frame["camtoworld"])[:3]
-            # append data
-            image_filenames.append(image_filename)
-            fx.append(intrinsics[0, 0])
-            fy.append(intrinsics[1, 1])
-            cx.append(intrinsics[0, 2])
-            cy.append(intrinsics[1, 2])
-            camera_to_worlds.append(camtoworld)
-        fx = torch.stack(fx)
-        fy = torch.stack(fy)
-        cx = torch.stack(cx)
-        cy = torch.stack(cy)
-        camera_to_worlds = torch.stack(camera_to_worlds)
-
-        # rotate the cameras and box 90 degrees about the x axis to put the z axis up
-        rotation = torch.tensor([[1, 0, 0], [0, 0, -1], [0, 1, 0]], dtype=torch.float32)
-        camera_to_worlds[:, :3] = rotation @ camera_to_worlds[:, :3]
-        bbox = (rotation @ bbox.T).T
-
-        scene_scale = self.config.scene_scale
-
-        # -- set the scene box ---
-        scene_box = SceneBox(aabb=bbox)
-        # center the box and adjust the cameras too
-        center = scene_box.get_center()
-        scene_box.aabb -= center
-        camera_to_worlds[..., 3] -= center
-        # scale the longest dimension to match the cube size
-        lengths = scene_box.aabb[1] - scene_box.aabb[0]
-        longest_dim = torch.argmax(lengths)
-        longest_length = lengths[longest_dim]
-        scale = scene_scale / longest_length.item()
-        scene_box.aabb = scene_box.aabb * scale  # box
-        camera_to_worlds[..., 3] *= scale  # cameras
-
-        # --- semantics ---
-        semantics = None
-        if self.config.include_semantics:
-            empty_path = Path()
-            replace_this_path = str(empty_path / images_folder / empty_path)
-            with_this_path = str(empty_path / segmentations_folder / "thing" / empty_path)
-            filenames = [
-                Path(str(image_filename).replace(replace_this_path, with_this_path).replace(".jpg", ".png"))
-                for image_filename in image_filenames
-            ]
-            panoptic_classes = load_from_json(self.config.data / "panoptic_classes.json")
-            classes = panoptic_classes["thing"]
-            colors = torch.tensor(panoptic_classes["thing_colors"], dtype=torch.float32) / 255.0
-            semantics = Semantics(filenames=filenames, classes=classes, colors=colors, mask_classes=["person"])
-
-        assert torch.all(cx[0] == cx), "Not all cameras have the same cx. Our Cameras class does not support this."
-        assert torch.all(cy[0] == cy), "Not all cameras have the same cy. Our Cameras class does not support this."
-
-        cameras = Cameras(
-            fx=fx,
-            fy=fy,
-            cx=float(cx[0]),
-            cy=float(cy[0]),
-            camera_to_worlds=camera_to_worlds,
-            camera_type=CameraType.PERSPECTIVE,
-        )
-        cameras.rescale_output_resolution(scaling_factor=1.0 / self.config.downscale_factor)
-
-        dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames,
-            cameras=cameras,
-            scene_box=scene_box,
-            metadata={"semantics": semantics} if self.config.include_semantics else {},
-            dataparser_scale=scale,
-        )
-        return dataparser_outputs
diff --git a/nerfstudio/data/dataparsers/zod_dataparser.py b/nerfstudio/data/dataparsers/zod_dataparser.py
new file mode 100644
index 00000000..3487072d
--- /dev/null
+++ b/nerfstudio/data/dataparsers/zod_dataparser.py
@@ -0,0 +1,456 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Data parser for the ZOD dataset"""
+
+import json
+from collections import defaultdict
+from dataclasses import dataclass, field
+from functools import lru_cache
+from pathlib import Path
+from typing import Dict, List, Tuple, Type, Union
+
+import numpy as np
+import torch
+from pyquaternion import Quaternion
+from typing_extensions import Literal
+from zod import Anonymization, Camera as ZodCamera, Lidar as ZodLidar, ZodSequences
+from zod.constants import EGO as ZOD_EGO
+from zod.data_classes.box import Box3D
+from zod.data_classes.sensor import LidarData
+
+from nerfstudio.cameras.cameras import Cameras, CameraType
+from nerfstudio.cameras.lidars import Lidars, LidarType, transform_points
+from nerfstudio.data.dataparsers.ad_dataparser import OPENCV_TO_NERFSTUDIO, ADDataParser, ADDataParserConfig
+from nerfstudio.data.dataparsers.nuscenes_dataparser import WLH_TO_LWH
+from nerfstudio.data.utils.lidar_elevation_mappings import VELODYNE_128_ELEVATION_MAPPING
+from nerfstudio.utils import poses as pose_utils
+
+TOP = "top"
+LEFT = "left"
+RIGHT = "right"
+
+
+ZOD_ELEVATION_MAPPING = {TOP: VELODYNE_128_ELEVATION_MAPPING}
+ZOD_LIDAR_IGNORE_REGIONS = {TOP: [[-34.0, -5.0, -30.0, -1.0], [-158.0, -134.0, -30.0, -1.0]]}
+ZOD_SKIP_ELEVATION_CHANNELS = {
+    TOP: (
+        36,
+        69,
+        54,
+        87,
+        0,
+        97,
+        18,
+        115,
+        44,
+        77,
+        62,
+        95,
+    )
+}  # these are channel indices that correspond to a low elevation angle, as per the VLS128 manual.
+
+HOOD_HEIGHT = 740
+
+
+HORIZONTAL_BEAM_DIVERGENCE = 3.0e-3  # radians, or meters at a distance of 1m
+VERTICAL_BEAM_DIVERGENCE = 1.5e-3  # radians, or meters at a distance of 1m
+HOOD_HEIGHT = 750  # px
+MAX_INTENSITY_VALUE = 255
+
+ALLOWED_CATEGORIES = {
+    "Vehicle",
+    "LargeVehicle",
+    "Motorcyclist",
+    "Bicyclist",
+    "Trailer",
+}
+SYMMETRIC_CATEGORIES = ALLOWED_CATEGORIES
+
+DEFORMABLE_CATEGORIES = {
+    "Pedestrian",
+}
+
+AVAILABLE_CAMERAS = ("front",)
+AVAILABLE_LIDARS = (
+    TOP,
+    LEFT,
+    RIGHT,
+)
+ZOD_LIDAR_TO_INDEX = {
+    TOP: 0,
+    LEFT: 1,
+    RIGHT: 2,
+}
+
+ZOD_INDEX_TO_LIDAR = {v: k for k, v in ZOD_LIDAR_TO_INDEX.items()}
+
+
+ZOD_AZIMUTH_RESOLUTION = {
+    TOP: 0.2,
+    LEFT: 0.2,
+    RIGHT: 0.2,
+}
+
+ZOD_CHANNEL_MAPPING = {
+    TOP: (1, 128),
+    LEFT: (129, 144),
+    RIGHT: (145, 160),
+}
+
+ZOD_LIDAR_NAME_TO_LIDAR_TYPE = {
+    TOP: LidarType.VELODYNE128,
+    LEFT: LidarType.VELODYNE16,
+    RIGHT: LidarType.VELODYNE16,
+}
+
+LANE_SHIFT_SIGN: Dict[str, Literal[-1, 1]] = defaultdict(lambda: -1)
+LANE_SHIFT_SIGN.update(
+    {
+        "000784": -1,
+        "000005": 1,
+        "000030": -1,
+        "000221": -1,
+        "000231": 1,
+        "000387": -1,
+        "001186": -1,
+        "000657": -1,
+        "000581": -1,
+        "000619": 1,
+        "000546": -1,
+        "000244": 1,
+        "000811": -1,
+    }
+)
+
+
+@dataclass
+class ZodDataParserConfig(ADDataParserConfig):
+    """ZOD dataset config.
+    ZOD (https://zod.zenseact.com) is an autonomous driving dataset containing:
+    - 100k frames.
+    - ~1500 20 second sequences.
+    - ~30 multi-minute drives.
+
+    Each clip was recorded with a suite of sensors including a wide angle camera, and a LiDAR.
+    It also includes 3D cuboid annotations around objects.
+    We optionally use these cuboids to mask dynamic objects by specifying the mask_dir flag.
+    To create these masks, install zod and run `zod generate object-masks`.
+    """
+
+    _target: Type = field(default_factory=lambda: Zod)
+    """target class to instantiate"""
+    sequence: str = "000581"
+    """Name of the scene."""
+    data: Path = Path("data/zod")
+    """Path to ZOD dataset."""
+    subset: Literal["sequences"] = "sequences"  # only sequences supported currently
+    """Dataset subset."""
+    version: Literal["mini", "full"] = "full"
+    """Dataset version."""
+    cameras: Tuple[Literal["front", "none", "all"], ...] = ("front",)
+    """Which cameras to use."""
+    lidars: Tuple[Literal["top", "left", "right", "all", "none"], ...] = (
+        "top",
+    )  # we have not implemented the correct transforms for left and right lidars
+    """Which lidars to use."""
+    min_lidar_dist: Tuple[float, float, float] = (1.5, 3.0, 1.5)
+    """Remove all points within this distance from lidar sensor."""
+    anonymization: Anonymization = Anonymization.BLUR
+    """Which cameras to use."""
+    annotation_interval: float = 0.1  # auto annotation interval
+    """Interval between annotations in seconds."""
+    auto_annotation_dir: Union[
+        Literal["default"], Path
+    ] = "default"  # if None, use default, which is /auto_annotations//.json
+    """Directory to load auto annotations from."""
+    add_missing_points: bool = True
+    """Whether to add missing points to the point cloud."""
+    lidar_elevation_mapping: Dict[str, Dict[int, float]] = field(default_factory=lambda: ZOD_ELEVATION_MAPPING)
+    """Elevation mapping for each lidar."""
+    lidar_azimuth_resolution: Dict[str, float] = field(default_factory=lambda: ZOD_AZIMUTH_RESOLUTION)
+    """Azimuth resolution for each lidar."""
+    skip_elevation_channels: Dict[str, Tuple] = field(default_factory=lambda: ZOD_SKIP_ELEVATION_CHANNELS)
+    """Channels to skip when adding missing points."""
+
+
+@dataclass
+class Zod(ADDataParser):
+    """Zenseact Open Dataset DatasetParser"""
+
+    config: ZodDataParserConfig
+
+    @property
+    def actor_transform(self) -> torch.Tensor:
+        """ZOD uses x-forward, so we need to rotate to x-right."""
+        return torch.from_numpy(WLH_TO_LWH)
+
+    def _get_lane_shift_sign(self, sequence: str) -> Literal[-1, 1]:
+        return LANE_SHIFT_SIGN.get(sequence, 1)
+
+    def _get_cameras(self) -> Tuple[Cameras, List[Path]]:
+        """Returns camera info and image filenames."""
+
+        if "all" in self.config.cameras:
+            self.config.cameras = AVAILABLE_CAMERAS
+
+        filenames, times, intrinsics, poses, idxs, distortions = [], [], [], [], [], []
+        heights, widths = [], []
+
+        for camera_idx, cam_name in enumerate(self.config.cameras):
+            zod_cam = getattr(ZodCamera, cam_name.upper())
+            cam_frames = self.zod_seq.info.get_camera_frames(anonymization=self.config.anonymization, camera=zod_cam)
+            for frame in cam_frames:
+                timestamp = frame.time.timestamp()
+
+                filenames.append(frame.filepath)
+                times.append(timestamp)
+
+                intrinsics.append(self.zod_seq.calibration.cameras[zod_cam].intrinsics)
+                ego_pose = self.zod_seq.oxts.get_poses(timestamp)
+                cam_pose = self.zod_seq.calibration.cameras[zod_cam].extrinsics.transform.copy()
+                # Convert from OpenCV to NerfStudio coordinate system
+                cam_pose[:3, :3] = cam_pose[:3, :3] @ OPENCV_TO_NERFSTUDIO
+                pose = ego_pose @ cam_pose
+                poses.append(pose)
+                idxs.append(camera_idx)
+                width, height = self.zod_seq.calibration.cameras[zod_cam].image_dimensions
+                distortion = self.zod_seq.calibration.cameras[zod_cam].distortion
+                # we only have the radial distortion parameters, so we pad with zeros
+                distortion = np.concatenate([distortion, np.zeros(2)])
+                distortions.append(distortion)
+                heights.append(height - HOOD_HEIGHT)
+                widths.append(width)
+
+        # To tensors
+        intrinsics = torch.from_numpy(np.array(intrinsics)).float()
+        distortions = torch.from_numpy(np.array(distortions)).float()
+        poses = torch.tensor(np.array(poses), dtype=torch.float32)
+        times = torch.tensor(times, dtype=torch.float64)  # need higher precision
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+        heights = torch.tensor(heights).int()
+        widths = torch.tensor(widths).int()
+
+        cameras = Cameras(
+            fx=intrinsics[:, 0, 0],
+            fy=intrinsics[:, 1, 1],
+            cx=intrinsics[:, 0, 2],
+            cy=intrinsics[:, 1, 2],
+            height=heights,
+            width=widths,
+            distortion_params=distortions,
+            camera_to_worlds=poses[:, :3, :4],
+            camera_type=CameraType.FISHEYE,
+            times=times,
+            metadata={"sensor_idxs": idxs},
+        )
+        return cameras, filenames
+
+    def _get_lidars(self) -> Tuple[Lidars, List[Path]]:
+        """Returns lidar info and loaded point clouds."""
+        if "all" in self.config.lidars:
+            self.config.lidars = AVAILABLE_LIDARS
+
+        if self.config.add_missing_points and self.config.lidars != (TOP,):
+            raise NotImplementedError(
+                "Adding missing points is only implemented for the top lidar. "
+                "If you want to add missing points for other lidars, please implement it and submit a PR."
+            )
+
+        times, poses, idxs, lidar_filenames = [], [], [], []
+        lidar_frames = self.zod_seq.info.get_lidar_frames(lidar=ZodLidar.VELODYNE)
+        for frame in lidar_frames:
+            timestamp = frame.time.timestamp()
+            ego_pose = self.zod_seq.oxts.get_poses(timestamp)
+            lidar_pose = self.zod_seq.calibration.lidars[ZodLidar.VELODYNE].extrinsics.transform.copy()
+            pose = ego_pose @ lidar_pose
+            # TODO: zod does not provide the correct extrinsics for the left and right lidars
+            # we should move them to their correct origins.
+            for lidar_name in self.config.lidars:
+                poses.append(pose)
+                idxs.append(ZOD_LIDAR_TO_INDEX[lidar_name])
+                lidar_filenames.append(Path(frame.filepath))
+                times.append(timestamp)
+
+        # To tensors
+        poses = torch.tensor(np.array(poses), dtype=torch.float64)  # will be converted to float32 later
+        times = torch.tensor(times, dtype=torch.float64)
+        idxs = torch.tensor(idxs).int().unsqueeze(-1)
+
+        lidars = Lidars(
+            lidar_to_worlds=poses[:, :3, :4],
+            lidar_type=LidarType.VELODYNE128,
+            times=times,
+            metadata={"sensor_idxs": idxs},
+            horizontal_beam_divergence=HORIZONTAL_BEAM_DIVERGENCE,
+            vertical_beam_divergence=VERTICAL_BEAM_DIVERGENCE,
+        )
+
+        return lidars, lidar_filenames
+
+    def _read_lidars(self, lidars: Lidars, filepaths: List[Path]) -> List[torch.Tensor]:
+        point_clouds = []
+
+        @lru_cache(maxsize=1)
+        def read_pc(fp):
+            pc = LidarData.from_npy(str(fp))
+            return pc
+
+        for i, fp in enumerate(filepaths):
+            lidar_idx = lidars.metadata["sensor_idxs"][i].item()
+            lidar_type = ZOD_INDEX_TO_LIDAR[lidar_idx]
+            min_channel, max_channel = ZOD_CHANNEL_MAPPING[lidar_type]
+
+            pc = read_pc(fp)
+            valid_channels = np.logical_and(min_channel <= pc.diode_idx, pc.diode_idx <= max_channel)
+            xyz = pc.points[valid_channels]  # N x 3
+            intensity = pc.intensity[valid_channels] / MAX_INTENSITY_VALUE  # N,
+            t = pc.timestamps[valid_channels] - pc.core_timestamp  # N, relative timestamps
+            diode_idx = pc.diode_idx[valid_channels]  # N,
+            diode_idx -= min_channel  # make diode_idx start at 0
+            pc = np.hstack((xyz, intensity[:, None], t[:, None], diode_idx[:, None]))
+            point_clouds.append(torch.from_numpy(pc).float())
+
+        times = lidars.times
+        poses = lidars.lidar_to_worlds
+
+        if self.config.add_missing_points:
+            assert self.config.lidars == (TOP,), "Only top lidar supported for missing points"
+            # add missing points
+            missing_points = []
+            ego_times = torch.from_numpy(self.zod_seq.oxts.timestamps)
+            times_close_to_lidar = (ego_times > (times.min() - 0.1)) & (ego_times < (times.max() + 0.1))
+            ego_times = ego_times[times_close_to_lidar]
+            ego_poses = torch.from_numpy(self.zod_seq.oxts.poses)[times_close_to_lidar]
+            lidar2ego = torch.from_numpy(self.zod_seq.calibration.lidars[ZodLidar.VELODYNE].extrinsics.transform.copy())
+            oxts_lidar_poses = ego_poses @ lidar2ego.unsqueeze(0)
+            for point_cloud, l2w, time in zip(point_clouds, poses, times):
+                pc = point_cloud.clone().to(torch.float64)
+                # absolute time
+                pc[:, 4] = pc[:, 4] + time
+                # project to world frame
+                pc[..., :3] = transform_points(pc[..., :3], l2w.unsqueeze(0).to(pc))
+                # remove ego motion compensation and move to sensor frame
+                pc, interpolated_poses = self._remove_ego_motion_compensation(pc, oxts_lidar_poses, ego_times)
+                # reset time
+                pc[:, 4] = point_cloud[:, 4].clone()
+                # transform to common lidar frame again
+                interpolated_poses = torch.matmul(
+                    pose_utils.inverse(l2w.unsqueeze(0)).float(), pose_utils.to4x4(interpolated_poses).float()
+                )
+                # move channel from index 5 to 3
+                pc = pc[..., [0, 1, 2, 5, 3, 4]]
+                # add missing points
+                mp = self._get_missing_points(
+                    pc, interpolated_poses, TOP, dist_cutoff=0.0, ignore_regions=ZOD_LIDAR_IGNORE_REGIONS[TOP]
+                ).float()
+                # move channel from index 3 to 5
+                mp = mp[..., [0, 1, 2, 4, 5, 3]]
+                missing_points.append(mp)
+            # add missing points to point clouds
+            point_clouds = [torch.cat([pc, missing], dim=0) for pc, missing in zip(point_clouds, missing_points)]
+
+        lidars.lidar_to_worlds = lidars.lidar_to_worlds.float()
+        return point_clouds
+
+    def _get_actor_trajectories(self) -> List[Dict]:
+        """Returns a list of actor trajectories.
+
+        Each trajectory is a dictionary with the following keys:
+            - poses: the poses of the actor (float32)
+            - timestamps: the timestamps of the actor (float64)
+            - dims: the dimensions of the actor, wlh order (float32)
+            - label: the label of the actor (str)
+            - stationary: whether the actor is stationary (bool)
+            - symmetric: whether the actor is expected to be symmetric (bool)
+            - deformable: whether the actor is expected to be deformable (e.g. pedestrian)
+        """
+        anno_dir = self.config.data / "auto_annotations" / self.config.subset / (self.config.sequence + ".json")
+        anno_dir = anno_dir if self.config.auto_annotation_dir == "default" else self.config.auto_annotation_dir
+
+        with open(str(anno_dir), "r") as f:
+            annos = json.load(f)
+
+        allowed_cats = ALLOWED_CATEGORIES
+        if self.config.include_deformable_actors:
+            allowed_cats.union(DEFORMABLE_CATEGORIES)
+
+        trajs = []
+        # loop over trajectories
+        for boxes in annos.values():
+            # they should be alive at least two frames
+            if len(boxes) < 2:
+                continue
+            label = boxes[0]["name"]
+
+            if label not in allowed_cats:
+                continue
+
+            poses = []
+            timestamps = []
+            wlh = []
+            for box in boxes:
+                bbox = Box3D(
+                    center=np.array(box["center"]),
+                    size=np.array(box["size"]),
+                    orientation=Quaternion(box["orientation"]),
+                    frame=ZodLidar.VELODYNE,
+                )
+                bbox.convert_to(ZOD_EGO, self.zod_seq.calibration)
+                pose = np.eye(4)
+                pose[:3, :3] = bbox.orientation.rotation_matrix
+                pose[:3, 3] = bbox.center
+                # TODO, do we need to do lwh to wlh here?
+                pose = pose @ WLH_TO_LWH
+                ego_pose = self.zod_seq.oxts.get_poses(box["timestamp"])
+                pose = ego_pose @ pose
+                poses.append(pose)
+                timestamps.append(box["timestamp"])
+                l, w, h = bbox.size  # noqa: E741
+                wlh.append(np.array([w, l, h]))
+
+            poses = np.array(poses)
+            # dynamic if we move more that 1m in any direction
+            dynamic = np.any(np.std(poses[:, :3, 3], axis=0) > 1.0)
+            # we skip all stationary objects
+            if not dynamic:
+                continue
+
+            symmetric = label in SYMMETRIC_CATEGORIES
+            deformable = label in DEFORMABLE_CATEGORIES
+
+            trajs.append(
+                {
+                    "poses": torch.tensor(poses, dtype=torch.float32),
+                    "timestamps": torch.tensor(timestamps, dtype=torch.float64),
+                    "dims": torch.tensor(np.median(wlh, axis=0), dtype=torch.float32),
+                    "label": label,
+                    "stationary": not dynamic,
+                    "symmetric": symmetric,
+                    "deformable": deformable,
+                }
+            )
+
+        return trajs
+
+    def _generate_dataparser_outputs(self, split="train"):
+        self.zod = ZodSequences(dataset_root=str(self.config.data), version=self.config.version, mp=False)
+        assert self.config.sequence in self.zod.get_all_ids(), f"Sequence {self.config.sequence} not found in dataset"
+
+        self.zod_seq = self.zod[self.config.sequence]
+
+        out = super()._generate_dataparser_outputs(split=split)
+        del self.zod
+        del self.zod_seq
+        return out
diff --git a/nerfstudio/data/datasets/base_dataset.py b/nerfstudio/data/datasets/base_dataset.py
index e16ea334..d9855809 100644
--- a/nerfstudio/data/datasets/base_dataset.py
+++ b/nerfstudio/data/datasets/base_dataset.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -85,6 +86,10 @@ def get_image_float32(self, image_idx: int) -> Float[Tensor, "image_height image
             image_idx: The image index in the dataset.
         """
         image = torch.from_numpy(self.get_numpy_image(image_idx).astype("float32") / 255.0)
+        height = min(self.cameras.height[image_idx], int(image.shape[0]))
+        width = min(self.cameras.width[image_idx], int(image.shape[1]))
+        image = image[:height]
+        image = image[:, :width]
         if self._dataparser_outputs.alpha_color is not None and image.shape[-1] == 4:
             assert (self._dataparser_outputs.alpha_color >= 0).all() and (
                 self._dataparser_outputs.alpha_color <= 1
diff --git a/nerfstudio/data/datasets/lidar_dataset.py b/nerfstudio/data/datasets/lidar_dataset.py
new file mode 100644
index 00000000..60420d8d
--- /dev/null
+++ b/nerfstudio/data/datasets/lidar_dataset.py
@@ -0,0 +1,82 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Lidar Dataset.
+"""
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Dict, List
+
+import torch
+
+from nerfstudio.cameras.lidars import Lidars
+from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
+from nerfstudio.data.datasets.base_dataset import Dataset
+
+
+class LidarDataset(Dataset):
+    """Dataset that returns lidar data.
+
+    Args:
+        dataparser_outputs: description of where and how to read data.
+        downsample_factor: The downsample factor for the dataparser outputs (lidar only)
+    """
+
+    exclude_batch_keys_from_device: List[str] = []
+    lidars: Lidars
+
+    def __init__(self, dataparser_outputs: DataparserOutputs, downsample_factor: float = 1.0) -> None:
+        super().__init__()
+        self.downsample_factor = downsample_factor
+        self._dataparser_outputs = dataparser_outputs
+        self.metadata = deepcopy(dataparser_outputs.metadata)
+        self.lidars: Lidars = deepcopy(self.metadata.pop("lidars"))
+        self.point_clouds = deepcopy(self.metadata.pop("point_clouds"))
+        self.has_masks = dataparser_outputs.mask_filenames is not None
+        self.scene_box = deepcopy(dataparser_outputs.scene_box)
+
+    def __len__(self):
+        return len(self.lidars)
+
+    # pylint: disable=no-self-use
+    def get_metadata(self, data: Dict) -> Dict:
+        """Method that can be used to process any additional metadata that may be part of the model inputs.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        return {}
+
+    def get_data(self, lidar_idx: int) -> Dict:
+        """Returns the LidarDataset data as a dictionary.
+
+        Args:
+            lidar_idx: The lidar index in the dataset.
+        """
+        data = {"lidar_idx": lidar_idx}
+        point_cloud = self.point_clouds[lidar_idx]
+        if point_cloud.shape[-1] == 4:  # no time offset
+            # add column of zeros
+            point_cloud = torch.cat([point_cloud, torch.zeros(point_cloud.shape[0], 1)], dim=1)
+        data["lidar"] = point_cloud
+
+        metadata = self.get_metadata(data)
+        data.update(metadata)
+        return data
+
+    def __getitem__(self, lidar_idx: int) -> Dict:
+        data = self.get_data(lidar_idx)
+        return data
diff --git a/nerfstudio/data/datasets/sdf_dataset.py b/nerfstudio/data/datasets/sdf_dataset.py
deleted file mode 100644
index 7342efa9..00000000
--- a/nerfstudio/data/datasets/sdf_dataset.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-SDFStudio dataset.
-"""
-
-from pathlib import Path
-from typing import Dict
-
-import numpy as np
-import torch
-from torch import Tensor
-
-from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
-from nerfstudio.data.datasets.base_dataset import InputDataset
-
-
-class SDFDataset(InputDataset):
-    """Dataset that returns images and depths.
-
-    Args:
-        dataparser_outputs: description of where and how to read input images.
-        scale_factor: The scaling factor for the dataparser outputs.
-    """
-
-    exclude_batch_keys_from_device = InputDataset.exclude_batch_keys_from_device + ["depth", "normal"]
-
-    def __init__(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0):
-        super().__init__(dataparser_outputs, scale_factor)
-
-        # can be none if monoprior not included
-        self.depth_filenames = self.metadata["depth_filenames"]
-        self.normal_filenames = self.metadata["normal_filenames"]
-        self.camera_to_worlds = self.metadata["camera_to_worlds"]
-        # can be none if auto orient not enabled in dataparser
-        self.transform = self.metadata["transform"]
-        self.include_mono_prior = self.metadata["include_mono_prior"]
-
-    def get_metadata(self, data: Dict) -> Dict:
-        # TODO supports foreground_masks
-        metadata = {}
-        if self.include_mono_prior:
-            depth_filepath = self.depth_filenames[data["image_idx"]]
-            normal_filepath = self.normal_filenames[data["image_idx"]]
-            camtoworld = self.camera_to_worlds[data["image_idx"]]
-
-            # Scale depth images to meter units and also by scaling applied to cameras
-            depth_image, normal_image = self.get_depths_and_normals(
-                depth_filepath=depth_filepath, normal_filename=normal_filepath, camtoworld=camtoworld
-            )
-            metadata["depth"] = depth_image
-            metadata["normal"] = normal_image
-
-        return metadata
-
-    def get_depths_and_normals(self, depth_filepath: Path, normal_filename: Path, camtoworld: Tensor):
-        """function to process additional depths and normal information
-        Args:
-            depth_filepath: path to depth file
-            normal_filename: path to normal file
-            camtoworld: camera to world transformation matrix
-        """
-
-        # load mono depth
-        depth = np.load(depth_filepath)
-        depth = torch.from_numpy(depth).float()
-
-        # load mono normal
-        normal = np.load(normal_filename)
-
-        # transform normal to world coordinate system
-        normal = normal * 2.0 - 1.0  # omnidata output is normalized so we convert it back to normal here
-        normal = torch.from_numpy(normal).float()
-
-        rot = camtoworld[:3, :3]
-
-        normal_map = normal.reshape(3, -1)
-        normal_map = torch.nn.functional.normalize(normal_map, p=2, dim=0)
-
-        normal_map = rot @ normal_map
-        normal = normal_map.permute(1, 0).reshape(*normal.shape[1:], 3)
-
-        if self.transform is not None:
-            h, w, _ = normal.shape
-            normal = self.transform[:3, :3] @ normal.reshape(-1, 3).permute(1, 0)
-            normal = normal.permute(1, 0).reshape(h, w, 3)
-
-        return depth, normal
diff --git a/nerfstudio/data/datasets/semantic_dataset.py b/nerfstudio/data/datasets/semantic_dataset.py
deleted file mode 100644
index ea209086..00000000
--- a/nerfstudio/data/datasets/semantic_dataset.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Semantic dataset.
-"""
-
-from typing import Dict
-
-import torch
-
-from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs, Semantics
-from nerfstudio.data.datasets.base_dataset import InputDataset
-from nerfstudio.data.utils.data_utils import get_semantics_and_mask_tensors_from_path
-
-
-class SemanticDataset(InputDataset):
-    """Dataset that returns images and semantics and masks.
-
-    Args:
-        dataparser_outputs: description of where and how to read input images.
-    """
-
-    exclude_batch_keys_from_device = InputDataset.exclude_batch_keys_from_device + ["mask", "semantics"]
-
-    def __init__(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0):
-        super().__init__(dataparser_outputs, scale_factor)
-        assert "semantics" in dataparser_outputs.metadata.keys() and isinstance(self.metadata["semantics"], Semantics)
-        self.semantics = self.metadata["semantics"]
-        self.mask_indices = torch.tensor(
-            [self.semantics.classes.index(mask_class) for mask_class in self.semantics.mask_classes]
-        ).view(1, 1, -1)
-
-    def get_metadata(self, data: Dict) -> Dict:
-        # handle mask
-        filepath = self.semantics.filenames[data["image_idx"]]
-        semantic_label, mask = get_semantics_and_mask_tensors_from_path(
-            filepath=filepath, mask_indices=self.mask_indices, scale_factor=self.scale_factor
-        )
-        if "mask" in data.keys():
-            mask = mask & data["mask"]
-        return {"mask": mask, "semantics": semantic_label}
diff --git a/nerfstudio/data/pixel_samplers.py b/nerfstudio/data/pixel_samplers.py
index 2704d852..51a94fd4 100644
--- a/nerfstudio/data/pixel_samplers.py
+++ b/nerfstudio/data/pixel_samplers.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,13 +17,14 @@
 Code for sampling pixels.
 """
 
+import math
 import random
 import warnings
 from dataclasses import dataclass, field
 from typing import Dict, Optional, Type, Union
 
 import torch
-from jaxtyping import Int
+from jaxtyping import Float, Int
 from torch import Tensor
 
 from nerfstudio.configs.base_config import InstantiateConfig
@@ -302,19 +304,22 @@ def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int,
         all_images = []
         all_depth_images = []
 
-        num_rays_in_batch = num_rays_per_batch // num_images
-        if num_rays_in_batch % 2 != 0:
-            num_rays_in_batch += 1
+        # find the optimal number of rays per image such that it is divisible by 2 and sums to the total number of rays
+        num_rays_per_image = num_rays_per_batch / num_images
+        residual = num_rays_per_image % 2
+        num_rays_per_image_under = int(num_rays_per_image - residual)
+        num_rays_per_image_over = int(num_rays_per_image_under + 2)
+        num_images_under = math.ceil(num_images * (1 - residual / 2))
+        num_images_over = num_images - num_images_under
+        num_rays_per_image = num_images_under * [num_rays_per_image_under] + num_images_over * [num_rays_per_image_over]
+        num_rays_per_image[-1] += num_rays_per_batch - sum(num_rays_per_image)
 
         if "mask" in batch:
-            for i in range(num_images):
+            for i, num_rays in enumerate(num_rays_per_image):
                 image_height, image_width, _ = batch["image"][i].shape
 
-                if i == num_images - 1:
-                    num_rays_in_batch = num_rays_per_batch - (num_images - 1) * num_rays_in_batch
-
                 indices = self.sample_method(
-                    num_rays_in_batch, 1, image_height, image_width, mask=batch["mask"][i].unsqueeze(0), device=device
+                    num_rays, 1, image_height, image_width, mask=batch["mask"][i].unsqueeze(0), device=device
                 )
                 indices[:, 0] = i
                 all_indices.append(indices)
@@ -323,16 +328,13 @@ def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int,
                     all_depth_images.append(batch["depth_image"][i][indices[:, 1], indices[:, 2]])
 
         else:
-            for i in range(num_images):
+            for i, num_rays in enumerate(num_rays_per_image):
                 image_height, image_width, _ = batch["image"][i].shape
-                if i == num_images - 1:
-                    num_rays_in_batch = num_rays_per_batch - (num_images - 1) * num_rays_in_batch
+
                 if self.config.is_equirectangular:
-                    indices = self.sample_method_equirectangular(
-                        num_rays_in_batch, 1, image_height, image_width, device=device
-                    )
+                    indices = self.sample_method_equirectangular(num_rays, 1, image_height, image_width, device=device)
                 else:
-                    indices = self.sample_method(num_rays_in_batch, 1, image_height, image_width, device=device)
+                    indices = self.sample_method(num_rays, 1, image_height, image_width, device=device)
                 indices[:, 0] = i
                 all_indices.append(indices)
                 all_images.append(batch["image"][i][indices[:, 1], indices[:, 2]])
@@ -469,6 +471,303 @@ def sample_method(
         return indices
 
 
+@dataclass
+class LidarPointSamplerConfig(PixelSamplerConfig):
+    """Config dataclass for LidarPointSampler."""
+
+    _target: Type = field(default_factory=lambda: LidarPointSampler)
+    """Target class to instantiate."""
+
+
+class LidarPointSampler(PixelSampler):
+    """Samples point from a point cloud."""
+
+    def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int, keep_full_image: bool = False):
+        """
+        Does the same as collate_image_dataset_batch, except it will operate over a list of images / masks inside
+        a list.
+
+        We will use this with the intent of DEPRECIATING it as soon as we find a viable alternative.
+        The intention will be to replace this with a more efficient implementation that doesn't require a for loop, but
+        since pytorch's ragged tensors are still in beta (this would allow for some vectorization), this will do.
+
+        Args:
+            batch: batch of images to sample from
+            num_rays_per_batch: number of rays to sample per batch
+            keep_full_image: whether or not to include a reference to the full image in returned batch
+        """
+
+        device = batch["lidar"][0].device
+        num_lidars = len(batch["lidar"])
+
+        # only sample within the mask, if the mask is in the batch
+        all_indices = []
+        all_lidars = []
+
+        num_rays_in_batch = num_rays_per_batch // num_lidars
+        for i in range(num_lidars):
+            if i == num_lidars - 1:
+                num_rays_in_batch = num_rays_per_batch - (num_lidars - 1) * num_rays_in_batch
+            num_points, _ = batch["lidar"][i].shape
+            # Use pixel sampler method with a "height" of num_points and a "width" of 1
+            indices = self.sample_method(num_rays_in_batch, 1, num_points, 1, device=device)
+            indices = indices[:, :-1]
+            indices[:, 0] = i
+            all_indices.append(indices)
+            all_lidars.append(batch["lidar"][i][indices[:, 1]])
+
+        indices = torch.cat(all_indices, dim=0)
+
+        c, n = (i.flatten() for i in torch.split(indices, 1, dim=-1))
+        collated_batch = {
+            key: value[c, n] for key, value in batch.items() if key not in ("lidar", "lidar_idx") and value is not None
+        }
+        collated_batch["lidar"] = torch.cat(all_lidars, dim=0)
+
+        # Needed to correct the random indices to their actual lidar idx locations.
+        indices[:, 0] = batch["lidar_idx"][c]
+        collated_batch["indices"] = indices  # with the abs camera indices
+        if keep_full_image:
+            raise NotImplementedError("keep_full_image not implemented for lidar")
+
+        return collated_batch
+
+    def collate_image_dataset_batch(self, batch: Dict, num_rays_per_batch: int, keep_full_image: bool = False):
+        """
+        Operates on a batch of images and samples pixels to use for generating rays.
+        Returns a collated batch which is input to the Graph.
+        It will sample only within the valid 'mask' if it's specified.
+
+        Args:
+            batch: batch of images to sample from
+            num_rays_per_batch: number of rays to sample per batch
+            keep_full_image: whether or not to include a reference to the full image in returned batch
+        """
+        device = batch["lidar"].device
+        num_lidars = len(batch["points_per_lidar"])
+        shuffle_lidars = torch.randperm(num_lidars, device=device)
+
+        num_rays_per_lidar = math.ceil(num_rays_per_batch / num_lidars)
+
+        n_points_per_lidar = batch["points_per_lidar"].to(device).to(torch.int64)
+        cum_points_per_lidar = torch.zeros((num_lidars,), device=device, dtype=torch.int64)
+        cum_points_per_lidar[1:] = torch.cumsum(n_points_per_lidar, dim=0)[:-1]
+        cum_points_per_lidar = cum_points_per_lidar.view(num_lidars, 1)
+        point_indices = torch.rand((num_lidars, num_rays_per_lidar), device=device, dtype=torch.float64)
+        point_indices = torch.floor(point_indices * n_points_per_lidar.view(num_lidars, 1)).long()
+        lidar_indices = torch.arange(num_lidars, device=device).unsqueeze(1).repeat(1, num_rays_per_lidar)
+
+        # shuffle the lidars to avoid dropping the same each time
+        lidar_indices = lidar_indices[shuffle_lidars]
+        point_indices = point_indices[shuffle_lidars]
+        cum_points_per_lidar = cum_points_per_lidar[shuffle_lidars]
+
+        indices = torch.stack((lidar_indices.flatten(), point_indices.flatten()), dim=-1)[:num_rays_per_batch]
+
+        flat_indices = (point_indices + cum_points_per_lidar).flatten()
+        all_lidars = batch["lidar"][flat_indices][:num_rays_per_batch]
+
+        c, n = (i.flatten() for i in torch.split(indices, 1, dim=-1))
+        collated_batch = {
+            key: value[c, n]
+            for key, value in batch.items()
+            if key not in ("lidar", "lidar_idx", "points_per_lidar") and value is not None
+        }
+        collated_batch["lidar"] = all_lidars
+
+        # Needed to correct the random indices to their actual lidar idx locations.
+        indices[:, 0] = batch["lidar_idx"][c]
+        collated_batch["indices"] = indices  # with the abs camera indices
+        if keep_full_image:
+            raise NotImplementedError("keep_full_image not implemented for lidar")
+
+        return collated_batch
+
+    def sample(self, image_batch: Dict):
+        """Sample an -image- lidar batch and return a -pixel- point batch.
+
+        Args:
+            image_batch: batch of -images- lidars to sample from
+        """
+        if isinstance(image_batch["lidar"], list):
+            image_batch = dict(image_batch.items())  # copy the dictionary so we don't modify the original
+            point_batch = self.collate_image_dataset_batch_list(
+                image_batch, self.num_rays_per_batch, keep_full_image=self.config.keep_full_image
+            )
+        elif isinstance(image_batch["lidar"], torch.Tensor):
+            point_batch = self.collate_image_dataset_batch(
+                image_batch, self.num_rays_per_batch, keep_full_image=self.config.keep_full_image
+            )
+        else:
+            raise ValueError("image_batch['lidar'] must be a list or torch.Tensor")
+        return point_batch
+
+
+@dataclass
+class ScaledPatchSamplerConfig(PixelSamplerConfig):
+    """Config dataclass for ScaledPatchSampler."""
+
+    _target: Type = field(default_factory=lambda: ScaledPatchSampler)
+    """Target class to instantiate."""
+    patch_scale: int = 1
+    """The upsampling ratio between sampled rays and pixel ground truths."""
+    patch_size: int = 1
+    """The size of sampled patches."""
+
+
+class ScaledPatchSampler(PixelSampler):
+    def __init__(
+        self,
+        config: ScaledPatchSamplerConfig,
+        num_rays_per_batch: int,
+        keep_full_image: bool = False,
+        **kwargs,
+    ) -> None:
+        super().__init__(config, num_rays_per_batch=num_rays_per_batch, keep_full_image=keep_full_image, **kwargs)
+        self.config: ScaledPatchSamplerConfig
+        self.patch_scale = self.config.patch_scale
+        self.patch_size = self.config.patch_size
+        self.sampling_weights: Optional[Tensor] = None
+        self.sampling_scale: int = 1
+
+    def collate_image_dataset_batch(self, batch: Dict, num_rays_per_batch: int, keep_full_image: bool = False):
+        """
+        Operates on a batch of images and samples pixels to use for generating rays.
+        Returns a collated batch which is input to the Graph.
+        It will sample only within the valid 'mask' if it's specified.
+
+        Args:
+            batch: batch of images to sample from
+            num_rays_per_batch: number of rays to sample per batch
+            keep_full_image: whether or not to include a reference to the full image in returned batch
+        """
+        self._check_extra_keys(batch)
+        device = batch["image"].device
+        num_images, image_height, image_width, _ = batch["image"].shape
+        image = batch["image"]
+        rgb_size = self.patch_size * self.patch_scale
+        num_patches = num_rays_per_batch // (self.patch_size**2)
+
+        patch_center_indices = self.sample_method(
+            num_patches, num_images, image_height, image_width, rgb_size, self.sampling_weights, device
+        )
+        ray_indices, img_patches = self._patches_from_centers(image, patch_center_indices, rgb_size, device)
+        ray_indices[:, 0] = batch["image_idx"][ray_indices[:, 0]]
+
+        collated_batch = {
+            "indices": ray_indices,
+            "image": img_patches,
+        }
+        if keep_full_image:
+            collated_batch["full_image"] = batch["image"]
+
+        return collated_batch
+
+    def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int, keep_full_image: bool = False):
+        self._check_extra_keys(batch)
+        device = batch["image"][0].device
+        num_images = len(batch["image"])
+        rgb_size = self.patch_size * self.patch_scale
+        num_patches = num_rays_per_batch // (self.patch_size**2)
+
+        assert self.sampling_weights is None, "sampling_weights not supported for ScaledPatchSampler in list mode"
+        img_indices = torch.randint(0, num_images, (num_patches,), device=device)  # TODO: use sampling weights
+        img_indices, img_counts = torch.unique(img_indices, return_counts=True)
+
+        all_img_patches = []
+        all_ray_indices = []
+        for img_idx, patches_in_img in zip(img_indices, img_counts):
+            image = batch["image"][img_idx].unsqueeze(0)
+            _, height, width, _ = image.shape
+            patch_center_indices = self.sample_method(
+                patches_in_img, 1, height, width, rgb_size, sampling_weights=None, device=device
+            )
+            ray_indices, img_patches = self._patches_from_centers(image, patch_center_indices, rgb_size, device)
+            all_img_patches.append(img_patches)
+            ray_indices[:, 0] = img_idx
+            all_ray_indices.append(ray_indices)
+        ray_indices = torch.cat(all_ray_indices, dim=0)
+        img_patches = torch.cat(all_img_patches, dim=0)
+        ray_indices[:, 0] = batch["image_idx"][ray_indices[:, 0]]  # use the "global" image idx
+
+        collated_batch = {"indices": ray_indices, "image": img_patches}
+        if keep_full_image:
+            collated_batch["full_image"] = batch["image"]
+        return collated_batch
+
+    def _patches_from_centers(
+        self,
+        image: torch.Tensor,
+        patch_center_indices: torch.Tensor,
+        rgb_size: int,
+        device: Union[torch.device, str] = "cpu",
+    ):
+        """Convert patch center coordinates to the full set of ray indices and image patches."""
+        offsets = torch.arange(-(rgb_size // 2), (rgb_size // 2) + rgb_size % 2, device=device)
+        zeros = offsets.new_zeros((rgb_size, rgb_size))
+        relative_indices = torch.stack((zeros, *torch.meshgrid(offsets, offsets, indexing="ij")), dim=-1)[
+            None
+        ]  # 1xKxKx3
+        rgb_indices = patch_center_indices[:, None, None] + relative_indices  # NxKxKx3
+        ray_indices = rgb_indices[
+            :, self.patch_scale // 2 :: self.patch_scale, self.patch_scale // 2 :: self.patch_scale
+        ]  # NxKfxKfx3
+        ray_indices = ray_indices.reshape(-1, 3)  # (N*Kf*Kf)x3
+        img_patches = image[rgb_indices[..., 0], rgb_indices[..., 1], rgb_indices[..., 2]]
+        return ray_indices, img_patches
+
+    def sample_method(
+        self,
+        batch_size: int,
+        num_images: int,
+        image_height: int,
+        image_width: int,
+        rgb_size: int,
+        sampling_weights: Optional[Tensor] = None,
+        device: Union[torch.device, str] = "cpu",
+    ):
+        """Samples (img, y, x) indices from the image batch, optionally according to per-pixel weights."""
+        if sampling_weights is None:
+            cropped_h, cropped_w = image_height - rgb_size + 1, image_width - rgb_size + 1
+            crop = rgb_size // 2
+            centers = super().sample_method(batch_size, num_images, cropped_h, cropped_w, device=device)
+            centers[:, 1:] = centers[:, 1:] + crop
+        else:
+            sampled_flat_indices = torch.multinomial(sampling_weights, batch_size, replacement=True)
+            scaled_h, scaled_w = self.sampling_shape[-2:]
+            image_indices = sampled_flat_indices // (scaled_h * scaled_w)
+            h_indices = (sampled_flat_indices % (scaled_h * scaled_w)) // scaled_w
+            w_indices = (sampled_flat_indices % (scaled_h * scaled_w)) % scaled_w
+            # Apply random jitter to the centers to compensate for sampling at a lower scale
+            if self.sampling_scale > 1:
+                jitter = torch.randint(0, self.sampling_scale, (batch_size, 2), device=device)
+                h_indices = h_indices * self.sampling_scale + jitter[:, 0]
+                w_indices = w_indices * self.sampling_scale + jitter[:, 1]
+            # move the centers if they are outside the crop range
+            h_indices = h_indices.clip(min=rgb_size // 2, max=image_height - rgb_size // 2 - 1)
+            w_indices = w_indices.clip(min=rgb_size // 2, max=image_width - rgb_size // 2 - 1)
+            centers = torch.stack((image_indices, h_indices, w_indices), dim=-1)
+        return centers  # img_idx, pixel_h, pixel_w
+
+    def update_sampling_weights(
+        self, scores: Float[Tensor, "n h w"], ratio_uniform: float = 0.5, sampling_scale: int = 1
+    ):
+        """Update the sampling weights based on the scores."""
+        # Downsample scores for efficient sampling
+        scores = torch.nn.functional.avg_pool2d(scores, sampling_scale, sampling_scale, ceil_mode=True)
+        self.sampling_scale = sampling_scale
+        self.sampling_shape = scores.shape
+        scores = scores.flatten()
+        score_probs = scores / scores.sum()
+        self.sampling_weights = ratio_uniform / scores.numel() + (1 - ratio_uniform) * score_probs
+
+    def _check_extra_keys(self, batch):
+        """Check for extra keys in the batch."""
+        extra_keys = set(batch.keys()) - {"image", "image_idx"}
+        if extra_keys:
+            raise NotImplementedError("Patch sampler not implemented for extra_keys")
+
+
 @dataclass
 class PairPixelSamplerConfig(PixelSamplerConfig):
     """Config dataclass for PairPixelSampler."""
diff --git a/nerfstudio/data/scene_box.py b/nerfstudio/data/scene_box.py
index 4abf60ea..242bf525 100644
--- a/nerfstudio/data/scene_box.py
+++ b/nerfstudio/data/scene_box.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -59,15 +60,22 @@ def get_centered_and_scaled_scene_box(self, scale_factor: Union[float, torch.Ten
         return SceneBox(aabb=(self.aabb - self.get_center()) * scale_factor)
 
     @staticmethod
-    def get_normalized_positions(positions: Float[Tensor, "*batch 3"], aabb: Float[Tensor, "2 3"]):
+    def get_normalized_positions(
+        positions: Float[Tensor, "*batch 3"], aabb: Float[Tensor, "* 2 3"], per_dim_norm: bool = True
+    ):
         """Return normalized positions in range [0, 1] based on the aabb axis-aligned bounding box.
 
         Args:
             positions: the xyz positions
             aabb: the axis-aligned bounding box
         """
-        aabb_lengths = aabb[1] - aabb[0]
-        normalized_positions = (positions - aabb[0]) / aabb_lengths
+        batch_shape = positions.shape[:-1]
+        if len(aabb.shape) > 2 and batch_shape != aabb.shape[:-2]:
+            # add singleton dimension
+            aabb = aabb.unsqueeze(1)
+        aabb_lengths = aabb[..., 1, :] - aabb[..., 0, :]
+        aabb_lengths = aabb_lengths if per_dim_norm else aabb_lengths.max(dim=-1, keepdim=True)[0]
+        normalized_positions = (positions - aabb[..., 0, :]) / aabb_lengths
         return normalized_positions
 
     @staticmethod
diff --git a/nerfstudio/data/utils/data_utils.py b/nerfstudio/data/utils/data_utils.py
index c81101c4..c8d19d18 100644
--- a/nerfstudio/data/utils/data_utils.py
+++ b/nerfstudio/data/utils/data_utils.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,6 +22,8 @@
 import torch
 from PIL import Image
 
+from nerfstudio.cameras.lidars import transform_points
+
 
 def get_image_mask_tensor_from_path(filepath: Path, scale_factor: float = 1.0) -> torch.Tensor:
     """
@@ -84,3 +87,53 @@ def get_depth_image_from_path(
         image = image.astype(np.float64) * scale_factor
         image = cv2.resize(image, (width, height), interpolation=interpolation)
     return torch.from_numpy(image[:, :, np.newaxis])
+
+
+def points_in_box(points, box2world, size):
+    """Return a mask of points that are inside a box"""
+    # points is [N, 3]
+
+    mask = torch.ones(points.shape[0], dtype=torch.bool, device=points.device)
+
+    # transform points to box frame
+    world2box = torch.inverse(box2world)
+    points = transform_points(points, world2box)
+
+    # check if points are in box
+    mask = mask & (points[:, 0] > -size[0] / 2) & (points[:, 0] < size[0] / 2)
+    mask = mask & (points[:, 1] > -size[1] / 2) & (points[:, 1] < size[1] / 2)
+    mask = mask & (points[:, 2] > -size[2] / 2) & (points[:, 2] < size[2] / 2)
+
+    return mask
+
+
+def remove_dynamic_points(point_clouds, l2w, timestamps, trajectories, extra_padding=0.15):
+    """Remove dynamic points from point clouds.
+
+    point_clouds - List of point clouds, assumed to be in sensor coordinates.
+    l2w - List of sensor-to-world transforms.
+    trajectories - List of object trajectories, assumed to be in in world coordinates.
+    extra_padding - Extra padding around the object bounding box.
+
+    Returns:
+        List of pruned point clouds, in sensor coordinates.
+    """
+    pruned_point_clouds = []
+
+    dynamic_trajs = [traj for traj in trajectories if not traj["stationary"]]
+
+    for i, pc in enumerate(point_clouds):
+        points_in_box_mask = torch.zeros(pc.shape[0], dtype=torch.bool, device=pc.device)
+        pc_in_world = transform_points(pc[:, :3], l2w[i])
+        for traj in dynamic_trajs:
+            time_index = ((traj["timestamps"] - timestamps[i]).abs() < 1e-3).nonzero()
+            if len(time_index) == 0:  # no matching timestamp
+                continue
+            time_index = time_index[0]
+            pose = traj["poses"][time_index]
+            size = traj["dims"] * (1 + extra_padding)
+            points_in_box_mask = points_in_box_mask | points_in_box(pc_in_world, pose, size)
+
+        pruned_point_clouds.append(pc[~points_in_box_mask])
+
+    return pruned_point_clouds
diff --git a/nerfstudio/data/utils/dataloaders.py b/nerfstudio/data/utils/dataloaders.py
index 6a64ba73..8e048688 100644
--- a/nerfstudio/data/utils/dataloaders.py
+++ b/nerfstudio/data/utils/dataloaders.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -29,8 +30,10 @@
 from torch.utils.data.dataloader import DataLoader
 
 from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.cameras.lidars import Lidars
 from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.data.datasets.base_dataset import InputDataset
+from nerfstudio.data.datasets.lidar_dataset import LidarDataset
 from nerfstudio.data.utils.nerfstudio_collate import nerfstudio_collate
 from nerfstudio.utils.misc import get_dict_to_torch
 from nerfstudio.utils.rich_utils import CONSOLE
@@ -150,21 +153,24 @@ class EvalDataloader(DataLoader):
     """Evaluation dataloader base class
 
     Args:
-        input_dataset: InputDataset to load data from
+        dataset: InputDataset to load data from
         device: Device to load data to
     """
 
     def __init__(
         self,
-        input_dataset: InputDataset,
+        dataset: Union[InputDataset, LidarDataset],
         device: Union[torch.device, str] = "cpu",
         **kwargs,
     ):
-        self.input_dataset = input_dataset
-        self.cameras = input_dataset.cameras.to(device)
+        self.dataset = dataset
+        if isinstance(dataset, InputDataset):
+            self.sensors = dataset.cameras.to(device)
+        else:
+            self.sensors = dataset.lidars.to(device)
         self.device = device
         self.kwargs = kwargs
-        super().__init__(dataset=input_dataset)
+        super().__init__(dataset=dataset)
 
     @abstractmethod
     def __iter__(self):
@@ -175,17 +181,17 @@ def __iter__(self):
     def __next__(self) -> Tuple[RayBundle, Dict]:
         """Returns the next batch of data"""
 
-    def get_camera(self, image_idx: int = 0) -> Tuple[Cameras, Dict]:
+    def get_camera(self, image_idx: int = 0) -> Tuple[Union[Cameras, Lidars], Dict]:
         """Get camera for the given image index
 
         Args:
             image_idx: Camera image index
         """
-        camera = self.cameras[image_idx : image_idx + 1]
-        batch = self.input_dataset[image_idx]
+        sensor = self.sensors[image_idx : image_idx + 1]
+        batch = self.dataset[image_idx]
         batch = get_dict_to_torch(batch, device=self.device, exclude=["image"])
         assert isinstance(batch, dict)
-        return camera, batch
+        return sensor, batch
 
     def get_data_from_image_idx(self, image_idx: int) -> Tuple[RayBundle, Dict]:
         """Returns the data for a specific image index.
@@ -193,8 +199,15 @@ def get_data_from_image_idx(self, image_idx: int) -> Tuple[RayBundle, Dict]:
         Args:
             image_idx: Camera image index
         """
-        ray_bundle = self.cameras.generate_rays(camera_indices=image_idx, keep_shape=True)
-        batch = self.input_dataset[image_idx]
+        if isinstance(self.dataset, InputDataset):
+            ray_bundle = self.sensors.generate_rays(image_idx, keep_shape=True)
+        elif isinstance(self.dataset, LidarDataset):
+            point_cloud = self.dataset.point_clouds[image_idx]
+            lidar_idx = torch.full((point_cloud.shape[0], 1), image_idx, dtype=torch.int64, device=self.device)
+            ray_bundle = self.sensors.generate_rays(lidar_idx, points=point_cloud)
+        else:
+            raise NotImplementedError(f"Dataset type {type(self.dataset)} not supported.")
+        batch = self.dataset[image_idx]
         batch = get_dict_to_torch(batch, device=self.device, exclude=["image"])
         assert isinstance(batch, dict)
         return ray_bundle, batch
@@ -204,21 +217,21 @@ class FixedIndicesEvalDataloader(EvalDataloader):
     """Dataloader that returns a fixed set of indices.
 
     Args:
-        input_dataset: InputDataset to load data from
+        dataset: InputDataset to load data from
         image_indices: List of image indices to load data from. If None, then use all images.
         device: Device to load data to
     """
 
     def __init__(
         self,
-        input_dataset: InputDataset,
+        dataset: Union[InputDataset, LidarDataset],
         image_indices: Optional[Tuple[int]] = None,
         device: Union[torch.device, str] = "cpu",
         **kwargs,
     ):
-        super().__init__(input_dataset, device, **kwargs)
+        super().__init__(dataset, device, **kwargs)
         if image_indices is None:
-            self.image_indices = list(range(len(input_dataset)))
+            self.image_indices = list(range(len(dataset)))
         else:
             self.image_indices = image_indices
         self.count = 0
@@ -239,7 +252,7 @@ def __next__(self):
 class RandIndicesEvalDataloader(EvalDataloader):
     """Dataloader that returns random images.
     Args:
-        input_dataset: InputDataset to load data from
+        dataset: InputDataset to load data from
         device: Device to load data to
     """
 
@@ -248,6 +261,5 @@ def __iter__(self):
 
     def __next__(self):
         # choose a random image index
-        image_idx = random.randint(0, len(self.cameras) - 1)
-        camera, batch = self.get_camera(image_idx)
-        return camera, batch
+        image_idx = random.randint(0, len(self.sensors) - 1)
+        return self.get_camera(image_idx)
diff --git a/nerfstudio/data/utils/lidar_elevation_mappings.py b/nerfstudio/data/utils/lidar_elevation_mappings.py
new file mode 100644
index 00000000..f2c891a5
--- /dev/null
+++ b/nerfstudio/data/utils/lidar_elevation_mappings.py
@@ -0,0 +1,252 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Lidar elevation mappings for different sensors. Maps diode index to elevation angle in degrees."""
+
+import numpy as np
+
+PANDAR64_ELEVATION_MAPPING = {
+    0: 14.882,
+    1: 11.032,
+    2: 8.059,
+    3: 5.057,
+    4: 3.04,
+    5: 2.028,
+    6: 1.86,
+    7: 1.688,
+    8: 1.522,
+    9: 1.351,
+    10: 1.184,
+    11: 1.013,
+    12: 0.846,
+    13: 0.675,
+    14: 0.508,
+    15: 0.337,
+    16: 0.169,
+    17: 0.0,
+    18: -0.169,
+    19: -0.337,
+    20: -0.508,
+    21: -0.675,
+    22: -0.846,
+    23: -1.013,
+    24: -1.184,
+    25: -1.351,
+    26: -1.522,
+    27: -1.688,
+    28: -1.86,
+    29: -2.028,
+    30: -2.198,
+    31: -2.365,
+    32: -2.536,
+    33: -2.7,
+    34: -2.873,
+    35: -3.04,
+    36: -3.21,
+    37: -3.375,
+    38: -3.548,
+    39: -3.712,
+    40: -3.884,
+    41: -4.05,
+    42: -4.221,
+    43: -4.385,
+    44: -4.558,
+    45: -4.72,
+    46: -4.892,
+    47: -5.057,
+    48: -5.229,
+    49: -5.391,
+    50: -5.565,
+    51: -5.726,
+    52: -5.898,
+    53: -6.061,
+    54: -7.063,
+    55: -8.059,
+    56: -9.06,
+    57: -9.885,
+    58: -11.032,
+    59: -12.006,
+    60: -12.974,
+    61: -13.93,
+    62: -18.889,
+    63: -24.897,
+}  # degrees
+
+VELODYNE_128_ELEVATION_MAPPING = {
+    0: -11.742,
+    1: -1.99,
+    2: 3.4,
+    3: -5.29,
+    4: -0.78,
+    5: 4.61,
+    6: -4.08,
+    7: 1.31,
+    8: -6.5,
+    9: -1.11,
+    10: 4.28,
+    11: -4.41,
+    12: 0.1,
+    13: 6.48,
+    14: -3.2,
+    15: 2.19,
+    16: -3.86,
+    17: 1.53,
+    18: -9.244,
+    19: -1.77,
+    20: 2.74,
+    21: -5.95,
+    22: -0.56,
+    23: 4.83,
+    24: -2.98,
+    25: 2.41,
+    26: -6.28,
+    27: -0.89,
+    28: 3.62,
+    29: -5.07,
+    30: 0.32,
+    31: 7.58,
+    32: -0.34,
+    33: 5.18,
+    34: -3.64,
+    35: 1.75,
+    36: -25.0,
+    37: -2.43,
+    38: 2.96,
+    39: -5.73,
+    40: 0.54,
+    41: 9.7,
+    42: -2.76,
+    43: 2.63,
+    44: -7.65,
+    45: -1.55,
+    46: 3.84,
+    47: -4.85,
+    48: 3.18,
+    49: -5.51,
+    50: -0.12,
+    51: 5.73,
+    52: -4.3,
+    53: 1.09,
+    54: -16.042,
+    55: -2.21,
+    56: 4.06,
+    57: -4.63,
+    58: 0.76,
+    59: 15.0,
+    60: -3.42,
+    61: 1.97,
+    62: -6.85,
+    63: -1.33,
+    64: -5.62,
+    65: -0.23,
+    66: 5.43,
+    67: -3.53,
+    68: 0.98,
+    69: -19.582,
+    70: -2.32,
+    71: 3.07,
+    72: -4.74,
+    73: 0.65,
+    74: 11.75,
+    75: -2.65,
+    76: 1.86,
+    77: -7.15,
+    78: -1.44,
+    79: 3.95,
+    80: -2.1,
+    81: 3.29,
+    82: -5.4,
+    83: -0.01,
+    84: 4.5,
+    85: -4.19,
+    86: 1.2,
+    87: -13.565,
+    88: -1.22,
+    89: 4.17,
+    90: -4.52,
+    91: 0.87,
+    92: 8.95,
+    93: -3.31,
+    94: 2.08,
+    95: -6.65,
+    96: 1.42,
+    97: -10.346,
+    98: -1.88,
+    99: 3.51,
+    100: -6.06,
+    101: -0.67,
+    102: 4.72,
+    103: -3.97,
+    104: 2.3,
+    105: -6.39,
+    106: -1.0,
+    107: 4.39,
+    108: -5.18,
+    109: 0.21,
+    110: 6.98,
+    111: -3.09,
+    112: 4.98,
+    113: -3.75,
+    114: 1.64,
+    115: -8.352,
+    116: -2.54,
+    117: 2.85,
+    118: -5.84,
+    119: -0.45,
+    120: 8.43,
+    121: -2.87,
+    122: 2.52,
+    123: -6.17,
+    124: -1.66,
+    125: 3.73,
+    126: -4.96,
+    127: 0.43,
+}  # degrees
+
+VELODYNE_VLP32C_ELEVATION_MAPPING = {
+    0: -25.0,
+    1: -1.0,
+    2: -1.667,
+    3: -15.639,
+    4: -11.31,
+    5: 0.0,
+    6: -0.667,
+    7: -8.843,
+    8: -7.254,
+    9: 0.333,
+    10: -0.333,
+    11: -6.148,
+    12: -5.333,
+    13: 1.333,
+    14: 0.667,
+    15: -4.0,
+    16: -4.667,
+    17: 1.667,
+    18: 1.0,
+    19: -3.667,
+    20: -3.333,
+    21: 3.333,
+    22: 2.333,
+    23: -2.667,
+    24: -3.0,
+    25: 7.0,
+    26: 4.667,
+    27: -2.333,
+    28: -2.0,
+    29: 15.0,
+    30: 10.333,
+    31: -1.333,
+}
+
+VELODYNE_HDL32E_ELEVATION_MAPPING = dict(zip(np.arange(32), tuple(np.linspace(-30.67, 10.67, 32))))
diff --git a/nerfstudio/engine/optimizers.py b/nerfstudio/engine/optimizers.py
index 0a879478..cb34fb76 100644
--- a/nerfstudio/engine/optimizers.py
+++ b/nerfstudio/engine/optimizers.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -70,6 +71,15 @@ class RAdamOptimizerConfig(OptimizerConfig):
     """The weight decay to use."""
 
 
+@dataclass
+class AdamWOptimizerConfig(OptimizerConfig):
+    """Basic optimizer config with Adam"""
+
+    _target: Type = torch.optim.AdamW
+    weight_decay: float = 0
+    """The weight decay to use."""
+
+
 class Optimizers:
     """A set of optimizers.
 
diff --git a/nerfstudio/engine/trainer.py b/nerfstudio/engine/trainer.py
index 9b4d6d8a..48f1bb0b 100644
--- a/nerfstudio/engine/trainer.py
+++ b/nerfstudio/engine/trainer.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,6 +16,7 @@
 """
 Code to train model.
 """
+
 from __future__ import annotations
 
 import dataclasses
@@ -33,7 +35,10 @@
 from rich.table import Table
 from torch.cuda.amp.grad_scaler import GradScaler
 
+from nerfstudio.configs.base_config import InstantiateConfig
 from nerfstudio.configs.experiment_config import ExperimentConfig
+from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager
+from nerfstudio.data.datamanagers.parallel_datamanager import ParallelDataManager
 from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
 from nerfstudio.engine.optimizers import Optimizers
 from nerfstudio.pipelines.base_pipeline import VanillaPipeline
@@ -49,6 +54,53 @@
 TORCH_DEVICE = str
 
 
+@dataclass
+class MetricTrackerConfig(InstantiateConfig):
+    """Configuration for the metric tracker, used for early stopping and checkpoint saving."""
+
+    _target: Type = field(default_factory=lambda: MetricTracker)
+    """target class to instantiate"""
+    metric: Optional[str] = "psnr"
+    """The metric to track for early stopping and checkpoint saving."""
+    higher_is_better: bool = True
+    """Whether a higher value of the metric is better."""
+    margin: float = 0.0
+    """Margin for comparison (0.1 = 10%)"""
+
+
+class MetricTracker:
+    """Class to track a metric to detect degradation."""
+
+    def __init__(self, config: MetricTrackerConfig) -> None:
+        self.config = config
+        self.best, self.latest = None, None
+
+    def did_degrade(self, fallback: bool = False) -> bool:
+        if (self.latest is None) or (self.best is None):
+            return fallback  # we can't tell
+        # apply margin to the best value (to be robust to noise in the metric)
+        best = self.best * (1 + (-self.config.margin if self.config.higher_is_better else self.config.margin))
+        return not self._is_new_better(best, self.latest)
+
+    def reset_latest(self) -> None:
+        self.latest = None
+
+    def update(self, metrics: Dict[str, float]) -> None:
+        self.latest = metrics.get(self.config.metric, None) if self.config.metric else None
+        if isinstance(self.latest, torch.Tensor):
+            self.latest = self.latest.item()
+        if self.latest is None:
+            return
+        if self.best is None:
+            self.best = self.latest
+        elif self._is_new_better(self.best, self.latest):
+            self.best = self.latest
+
+    def _is_new_better(self, old: float, new: float) -> bool:
+        """Check if new is better than old."""
+        return new >= old if self.config.higher_is_better else new <= old
+
+
 @dataclass
 class TrainerConfig(ExperimentConfig):
     """Configuration for training regimen"""
@@ -71,6 +123,10 @@ class TrainerConfig(ExperimentConfig):
     """Use gradient scaler even if the automatic mixed precision is disabled."""
     save_only_latest_checkpoint: bool = True
     """Whether to only save the latest checkpoint or all checkpoints."""
+    checkpoint_saving_tracker: MetricTrackerConfig = field(default_factory=lambda: MetricTrackerConfig(margin=0.05))
+    """Configuration for the metric tracker."""
+    early_stopping_tracker: MetricTrackerConfig = field(default_factory=lambda: MetricTrackerConfig(margin=0.1))
+    """Configuration for the metric tracker."""
     # optional parameters if we want to resume training
     load_dir: Optional[Path] = None
     """Optionally specify a pre-trained model directory to load from."""
@@ -80,6 +136,8 @@ class TrainerConfig(ExperimentConfig):
     """Path to config YAML file."""
     load_checkpoint: Optional[Path] = None
     """Path to checkpoint file."""
+    training_start_step: Optional[int] = None
+    """Optionally specify the starting step for training. Useful to load checkpoint but start from a different step."""
     log_gradients: bool = False
     """Optionally log gradients during training"""
     gradient_accumulation_steps: Dict[str, int] = field(default_factory=lambda: {})
@@ -135,6 +193,9 @@ def __init__(self, config: TrainerConfig, local_rank: int = 0, world_size: int =
         self.checkpoint_dir: Path = config.get_checkpoint_dir()
         CONSOLE.log(f"Saving checkpoints to: {self.checkpoint_dir}")
 
+        self.checkpoint_saving_tracker = self.config.checkpoint_saving_tracker.setup()
+        self.early_stopping_tracker = self.config.early_stopping_tracker.setup()
+
         self.viewer_state = None
 
     def setup(self, test_mode: Literal["test", "val", "inference"] = "val") -> None:
@@ -225,9 +286,11 @@ def train(self) -> None:
         """Train the model."""
         assert self.pipeline.datamanager.train_dataset is not None, "Missing DatsetInputs"
 
-        self.pipeline.datamanager.train_dataparser_outputs.save_dataparser_transform(
-            self.base_dir / "dataparser_transforms.json"
-        )
+        # don't want to call save_dataparser_transform if pipeline's datamanager does not have a dataparser
+        if isinstance(self.pipeline.datamanager, (VanillaDataManager, ParallelDataManager)):
+            self.pipeline.datamanager.train_dataparser_outputs.save_dataparser_transform(
+                self.base_dir / "dataparser_transforms.json"
+            )
 
         self._init_viewer_state()
         with TimeWriter(writer, EventName.TOTAL_TRAIN_TIME):
@@ -284,7 +347,12 @@ def train(self) -> None:
 
                 # Do not perform evaluation if there are no validation images
                 if self.pipeline.datamanager.eval_dataset:
-                    self.eval_iteration(step)
+                    with self.train_lock:  # avoid race conditions with eval
+                        self.eval_iteration(step)
+
+                if self.early_stopping_tracker.did_degrade():
+                    print("Early stopping due to degradation")
+                    break
 
                 if step_check(step, self.config.steps_per_save):
                     self.save_checkpoint(step)
@@ -332,7 +400,7 @@ def _check_viewer_warnings(self) -> None:
     @check_viewer_enabled
     def _init_viewer_state(self) -> None:
         """Initializes viewer scene with given train dataset"""
-        assert self.viewer_state and self.pipeline.datamanager.train_dataset
+        assert self.viewer_state and self.pipeline.datamanager.train_dataset is not None
         self.viewer_state.init_scene(
             train_dataset=self.pipeline.datamanager.train_dataset,
             train_state="training",
@@ -399,10 +467,14 @@ def _load_checkpoint(self) -> None:
             load_path: Path = load_dir / f"step-{load_step:09d}.ckpt"
             assert load_path.exists(), f"Checkpoint {load_path} does not exist"
             loaded_state = torch.load(load_path, map_location="cpu")
+            loaded_state["step"] = (
+                self.config.training_start_step if self.config.training_start_step is not None else loaded_state["step"]
+            )
             self._start_step = loaded_state["step"] + 1
             # load the checkpoints for pipeline, optimizers, and gradient scalar
             self.pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"])
-            self.optimizers.load_optimizers(loaded_state["optimizers"])
+            if self.config.load_optimizer:
+                self.optimizers.load_optimizers(loaded_state["optimizers"])
             if "schedulers" in loaded_state and self.config.load_scheduler:
                 self.optimizers.load_schedulers(loaded_state["schedulers"])
             self.grad_scaler.load_state_dict(loaded_state["scalers"])
@@ -410,10 +482,14 @@ def _load_checkpoint(self) -> None:
         elif load_checkpoint is not None:
             assert load_checkpoint.exists(), f"Checkpoint {load_checkpoint} does not exist"
             loaded_state = torch.load(load_checkpoint, map_location="cpu")
+            loaded_state["step"] = (
+                self.config.training_start_step if self.config.training_start_step is not None else loaded_state["step"]
+            )
             self._start_step = loaded_state["step"] + 1
             # load the checkpoints for pipeline, optimizers, and gradient scalar
             self.pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"])
-            self.optimizers.load_optimizers(loaded_state["optimizers"])
+            if self.config.load_optimizer:
+                self.optimizers.load_optimizers(loaded_state["optimizers"])
             if "schedulers" in loaded_state and self.config.load_scheduler:
                 self.optimizers.load_schedulers(loaded_state["schedulers"])
             self.grad_scaler.load_state_dict(loaded_state["scalers"])
@@ -431,14 +507,19 @@ def save_checkpoint(self, step: int) -> None:
         # possibly make the checkpoint directory
         if not self.checkpoint_dir.exists():
             self.checkpoint_dir.mkdir(parents=True, exist_ok=True)
+        if self.checkpoint_saving_tracker.did_degrade(fallback=True):
+            return
+        self.checkpoint_saving_tracker.reset_latest()  # we only want to save the best once
         # save the checkpoint
         ckpt_path: Path = self.checkpoint_dir / f"step-{step:09d}.ckpt"
         torch.save(
             {
                 "step": step,
-                "pipeline": self.pipeline.module.state_dict()  # type: ignore
-                if hasattr(self.pipeline, "module")
-                else self.pipeline.state_dict(),
+                "pipeline": (
+                    self.pipeline.module.state_dict()  # type: ignore
+                    if hasattr(self.pipeline, "module")
+                    else self.pipeline.state_dict()
+                ),
                 "optimizers": {k: v.state_dict() for (k, v) in self.optimizers.optimizers.items()},
                 "schedulers": {k: v.state_dict() for (k, v) in self.optimizers.schedulers.items()},
                 "scalers": self.grad_scaler.state_dict(),
@@ -500,6 +581,7 @@ def train_iteration(self, step: int) -> TRAIN_INTERATION_OUTPUT:
 
     @check_eval_enabled
     @profiler.time_function
+    @torch.no_grad()
     def eval_iteration(self, step: int) -> None:
         """Run one iteration with different batch/image/all image evaluations depending on step size.
 
@@ -509,6 +591,8 @@ def eval_iteration(self, step: int) -> None:
         # a batch of eval rays
         if step_check(step, self.config.steps_per_eval_batch):
             _, eval_loss_dict, eval_metrics_dict = self.pipeline.get_eval_loss_dict(step=step)
+            eval_loss_dict = detach_items_if_tensor(eval_loss_dict)
+            eval_metrics_dict = detach_items_if_tensor(eval_metrics_dict)
             eval_loss = functools.reduce(torch.add, eval_loss_dict.values())
             writer.put_scalar(name="Eval Loss", scalar=eval_loss, step=step)
             writer.put_dict(name="Eval Loss Dict", scalar_dict=eval_loss_dict, step=step)
@@ -524,7 +608,7 @@ def eval_iteration(self, step: int) -> None:
                 step=step,
                 avg_over_steps=True,
             )
-            writer.put_dict(name="Eval Images Metrics", scalar_dict=metrics_dict, step=step)
+            writer.put_dict(name="Eval Images Metrics", scalar_dict=detach_items_if_tensor(metrics_dict), step=step)
             group = "Eval Images"
             for image_name, image in images_dict.items():
                 writer.put_image(name=group + "/" + image_name, image=image, step=step)
@@ -532,4 +616,15 @@ def eval_iteration(self, step: int) -> None:
         # all eval images
         if step_check(step, self.config.steps_per_eval_all_images):
             metrics_dict = self.pipeline.get_average_eval_image_metrics(step=step)
-            writer.put_dict(name="Eval Images Metrics Dict (all images)", scalar_dict=metrics_dict, step=step)
+            self.early_stopping_tracker.update(metrics_dict)
+            self.checkpoint_saving_tracker.update(metrics_dict)
+            writer.put_dict(
+                name="Eval Images Metrics Dict (all images)",
+                scalar_dict=detach_items_if_tensor(metrics_dict),
+                step=step,
+            )
+
+
+def detach_items_if_tensor(dict):
+    """Detach items in dictionary if they are tensors"""
+    return {k: v.detach() if isinstance(v, torch.Tensor) else v for k, v in dict.items()}
diff --git a/nerfstudio/field_components/encodings.py b/nerfstudio/field_components/encodings.py
index 8d63cd92..50f047f2 100644
--- a/nerfstudio/field_components/encodings.py
+++ b/nerfstudio/field_components/encodings.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -319,6 +320,7 @@ class HashEncoding(Encoding):
         hash_init_scale: Value to initialize hash grid.
         implementation: Implementation of hash encoding. Fallback to torch if tcnn not available.
         interpolation: Interpolation override for tcnn hashgrid. Not supported for torch unless linear.
+        n_input_dims: Number of input dimensions (typically 3 for x,y,z)
     """
 
     def __init__(
@@ -331,6 +333,7 @@ def __init__(
         hash_init_scale: float = 0.001,
         implementation: Literal["tcnn", "torch"] = "tcnn",
         interpolation: Optional[Literal["Nearest", "Linear", "Smoothstep"]] = None,
+        n_input_dims: int = 3,
     ) -> None:
         super().__init__(in_dim=3)
         self.num_levels = num_levels
@@ -339,10 +342,12 @@ def __init__(
         self.hash_init_scale = hash_init_scale
         self.log2_hashmap_size = log2_hashmap_size
         self.hash_table_size = 2**log2_hashmap_size
+        self.min_res = min_res
+        self.hash_init_scale = hash_init_scale
 
         levels = torch.arange(num_levels)
-        self.growth_factor = np.exp((np.log(max_res) - np.log(min_res)) / (num_levels - 1)) if num_levels > 1 else 1
-        self.scalings = torch.floor(min_res * self.growth_factor**levels)
+        self.growth_factor = np.exp((np.log(max_res) - np.log(min_res)) / (num_levels - 1)) if num_levels > 1 else 1.0
+        self.register_buffer("scalings", torch.floor(min_res * self.growth_factor**levels))
 
         self.hash_offset = levels * self.hash_table_size
 
@@ -363,7 +368,7 @@ def __init__(
                 interpolation=interpolation,
             )
             self.tcnn_encoding = tcnn.Encoding(
-                n_input_dims=3,
+                n_input_dims=n_input_dims,
                 encoding_config=encoding_config,
             )
 
@@ -759,7 +764,7 @@ class SHEncoding(Encoding):
         levels: Number of spherical harmonic levels to encode.
     """
 
-    def __init__(self, levels: int = 4, implementation: Literal["tcnn", "torch"] = "torch") -> None:
+    def __init__(self, levels: int = 4, implementation: Literal["tcnn", "torch"] = "tcnn") -> None:
         super().__init__(in_dim=3)
 
         if levels <= 0 or levels > 4:
diff --git a/nerfstudio/field_components/field_heads.py b/nerfstudio/field_components/field_heads.py
index 44df9f60..96c206d1 100644
--- a/nerfstudio/field_components/field_heads.py
+++ b/nerfstudio/field_components/field_heads.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -41,6 +42,8 @@ class FieldHeadNames(Enum):
     SDF = "sdf"
     ALPHA = "alpha"
     GRADIENT = "gradient"
+    FEATURE = "feature"
+    LIDAR_RETURN_PROB = "lidar_return_prob"
 
 
 class FieldHead(FieldComponent):
diff --git a/nerfstudio/field_components/neurad_encoding.py b/nerfstudio/field_components/neurad_encoding.py
new file mode 100644
index 00000000..6e7b6d41
--- /dev/null
+++ b/nerfstudio/field_components/neurad_encoding.py
@@ -0,0 +1,301 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from contextlib import nullcontext
+from dataclasses import dataclass, field
+from typing import Dict, Literal, Optional, Tuple, Type, overload
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from nerfstudio.cameras.lidars import transform_points_pairwise
+from nerfstudio.configs.base_config import InstantiateConfig
+from nerfstudio.field_components.encodings import HashEncoding
+from nerfstudio.field_components.spatial_distortions import ScaledSceneContraction
+from nerfstudio.model_components.dynamic_actors import DynamicActors
+from nerfstudio.utils.math import GaussiansStd
+from nerfstudio.utils.poses import inverse as pose_inverse
+
+EPS = 1.0e-7
+
+
+@dataclass
+class StaticSettings:
+    hashgrid_dim: int = 4
+    """Number of dimensions of each hashgrid feature (per level)."""
+    num_levels: int = 8  # TODO: reduce this and/or increase res and dim
+    """Number of levels of the hashmap for the base mlp."""
+    base_res: int = 32
+    """Resolution of the base grid for the hasgrid."""
+    max_res: int = 8192
+    """Maximum resolution of the hashmap for the base mlp."""
+    log2_hashmap_size: int = 22
+    """Maximum size of the static-world hashmap (per level)."""
+
+
+@dataclass
+class ActorSettings:
+    flip_prob: float = 0.5
+    """The probability of flipping the sign of positions and directions fed to actor networks."""
+    actor_scale: float = 10.0
+    """Actor scale, in meters. This is applied before scene contraction."""
+    # Hashgrid settings
+    hashgrid_dim: int = 4
+    """Number of dimensions of each hashgrid feature (per level)."""
+    num_levels: int = 4
+    """Number of levels of the hashmap for the base mlp."""
+    base_res: int = 64
+    """Resolution of the base grid for the hasgrid."""
+    max_res: int = 1024
+    """Maximum resolution of the hashmap for the base mlp."""
+    log2_hashmap_size: int = 17
+    """Maximum size of the static-world hashmap (per level)."""
+    use_4d_hashgrid: bool = True
+    """Whether to use a 4D hashgrid (with actor idx as 4th dimension), or a list of hashgrids."""
+
+
+@dataclass
+class NeuRADHashEncodingConfig(InstantiateConfig):
+    """Hashgrid config"""
+
+    _target: Type = field(default_factory=lambda: NeuRADHashEncoding)
+    """Target class to instantiate."""
+    static: StaticSettings = field(default_factory=StaticSettings)
+    """Static settings"""
+    actor: ActorSettings = field(default_factory=ActorSettings)
+    """Dynamic settings"""
+    disable_actors: bool = False
+    """Whether to disable actors."""
+    require_actor_grad: bool = True
+    """Whether to propagate gradients to actor trajectories."""
+
+
+class NeuRADHashEncoding(nn.Module):
+    def __init__(
+        self,
+        config: NeuRADHashEncodingConfig,
+        dynamic_actors: DynamicActors,
+        static_scale: float,
+        implementation: Literal["tcnn", "torch"] = "tcnn",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.implementation = implementation
+        self.actors = dynamic_actors
+
+        self.static_contraction = ScaledSceneContraction(order=float("inf"), scale=static_scale)
+        self.actor_contraction = ScaledSceneContraction(order=float("inf"), scale=config.actor.actor_scale)
+
+        self.static_grid = HashEncoding(
+            implementation=implementation,
+            features_per_level=config.static.hashgrid_dim,
+            num_levels=config.static.num_levels,
+            min_res=config.static.base_res,
+            max_res=config.static.max_res,
+            log2_hashmap_size=config.static.log2_hashmap_size,
+        )
+
+        if config.actor.use_4d_hashgrid:
+            self._get_actor_features = self._get_actor_features_fast
+            n_grids, n_input_dims = 1, 4
+        else:
+            self._get_actor_features = self._get_actor_features_slow
+            n_grids, n_input_dims = self.actors.n_actors, 3
+        self.actor_grids = nn.ModuleList(
+            [
+                HashEncoding(
+                    implementation=implementation,
+                    features_per_level=config.actor.hashgrid_dim,
+                    num_levels=config.actor.num_levels,
+                    min_res=config.actor.base_res,
+                    max_res=config.actor.max_res,
+                    log2_hashmap_size=config.actor.log2_hashmap_size,
+                    n_input_dims=n_input_dims,
+                )
+                for _ in range(n_grids)
+            ]
+        )
+
+        self.scene_repr_dim = self.static_grid.get_out_dim()
+
+    def get_out_dim(self) -> int:
+        return self.scene_repr_dim
+
+    def get_param_groups(self, param_groups: Dict):
+        """Get parameter groups."""
+        param_groups["hashgrids"] += list(self.static_grid.parameters()) + list(self.actor_grids.parameters())
+
+    @overload
+    def forward(self, positions: GaussiansStd, times: Tensor, directions: None) -> Tuple[Tensor, None]:
+        ...
+
+    @overload
+    def forward(self, positions: GaussiansStd, times: Tensor, directions: Tensor) -> Tuple[Tensor, Tensor]:
+        ...
+
+    def forward(
+        self,
+        positions: GaussiansStd,
+        times: Tensor,
+        directions: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """Evaluates the hash grids at the provided positions.
+
+        Args:
+            positions: Positions to get features for.
+
+        Returns:
+            features: Hashgrid features at the provided positions.
+            directions: Directions, transformed to actor space if applicable, and normalized.
+
+        """
+        # Static
+        static_positions = self.static_contraction(positions)
+        static_features = self._get_static_features(static_positions)
+        static_features = static_features.to(positions.dtype)
+        out_shape = (*times[..., 0].shape, self.scene_repr_dim)
+        features = static_features.reshape(out_shape)
+
+        # Actors
+        with torch.no_grad() if not self.config.require_actor_grad else nullcontext():
+            indices, actor_positions, directions = self._split_static_vs_actors(positions, times, directions)
+            ray_idx, sample_idx, actor_idx = indices
+        if actor_positions is None or actor_idx.shape[0] == 0:
+            return features.view(-1, features.shape[-1]), directions
+        actor_positions = self.actor_contraction(actor_positions)
+
+        actor_hashgrid_idx = self.actors.actor_to_id[actor_idx]
+        actor_features = self._get_actor_features(actor_positions, actor_hashgrid_idx)
+        padded_actor_features = F.pad(actor_features, (0, self.scene_repr_dim - actor_features.shape[-1]))
+        features[ray_idx, sample_idx] = padded_actor_features
+
+        return features.view(-1, features.shape[-1]), directions
+
+    def _split_static_vs_actors(self, positions: GaussiansStd, times: Tensor, directions: Optional[Tensor]):
+        if self.config.disable_actors or self.actors.n_actors == 0:
+            # # haxx to disable actors
+            return (torch.empty(0), torch.empty(0), torch.empty(0)), None, directions
+
+        boxes2world, valid = self.actors.get_boxes2world(times[:, 0].squeeze(-1), flatten=False)
+        world2boxes = pose_inverse(boxes2world)
+
+        ray_idx, sample_idx, actor_idx = self._get_actor_indices(positions.mean, boxes2world, valid, world2boxes)
+
+        w2b = world2boxes[ray_idx, actor_idx]
+        pos = positions.mean[ray_idx, sample_idx]
+        pos = transform_points_pairwise(pos, w2b.unsqueeze(-3))
+
+        # Transform directions and/or positions to actor space
+        if directions is not None:
+            directions = directions.clone()
+            dirs = directions[ray_idx, sample_idx]
+            dirs = transform_points_pairwise(dirs, w2b, with_translation=False).squeeze(1)
+            dirs = dirs / (torch.linalg.norm(dirs, dim=-1, keepdim=True) + EPS)
+            directions[ray_idx, sample_idx] = dirs
+
+        # Apply random flip if applicable
+        if self.training and self.config.actor.flip_prob > EPS:
+            # -1 with prob flip_prob, else 1. Per ray.
+            ray_flip = torch.bernoulli(torch.full_like(world2boxes[:, 0, 0, 0], self.config.actor.flip_prob)) * -2 + 1
+            flip = torch.ones_like(pos[..., 0:1, :])
+            flip[..., 0] = ray_flip[ray_idx].unsqueeze(-1)
+            pos = pos * flip
+            if directions is not None:
+                directions[ray_idx, sample_idx, 0] = directions[ray_idx, sample_idx, 0] * flip[..., 0].squeeze(-1)
+
+        dynamic_positions = GaussiansStd(pos, positions.std[ray_idx, sample_idx])
+
+        return (ray_idx, sample_idx, actor_idx), dynamic_positions, directions
+
+    @torch.no_grad()
+    def _get_actor_indices(self, pos, boxes2world, valid, world2boxes):
+        # Finds all sample-actor pairs that are close enough (can be multiple per sample)
+        actor_radii = (actor_bounds := self.actors.actor_bounds()).norm(dim=-1)
+        sample_mean_pos = pos.mean(-2)
+
+        # Check if actor is close to ray (defined by first and last sample)
+        point_on_line = sample_mean_pos[:, 0, :]  # n_rays x 3
+        line_direction = sample_mean_pos[:, -1, :] - point_on_line
+        line_direction = line_direction / (torch.linalg.norm(line_direction, dim=-1, keepdim=True) + EPS)
+        line_direction = line_direction.unsqueeze(-2)  # n_rays x 1 x 3
+        vec_from_line = boxes2world[..., :3, 3] - point_on_line.unsqueeze(-2)  # n_rays x n_actors x 3
+        cross_prod = torch.cross(vec_from_line, line_direction, dim=-1)  # n_rays x n_actors x 3
+        distance = torch.linalg.norm(cross_prod, dim=-1)  # n_rays x n_actors
+        close_to_line_and_valid_mask = (distance < actor_radii) & valid  # n_rays x n_actors
+        ray_idx, actor_idx = close_to_line_and_valid_mask.nonzero(as_tuple=False).T  # N x 2 (ray_idx, actor_idx)
+
+        sample_pos = sample_mean_pos[ray_idx]  # N x n_samples x 3
+        actor_pos = boxes2world[ray_idx, actor_idx, :3, 3].unsqueeze(-2).repeat(1, sample_pos.shape[-2], 1)  # N x 3
+        distance = torch.linalg.norm(sample_pos - actor_pos, dim=-1)  # N x n_samples
+        within_range_mask = distance < actor_radii[actor_idx].unsqueeze(-1)  # N x n_samples
+        within_range_idx = within_range_mask.nonzero(as_tuple=False)  # N x 2 (ray_idx, sample_idx)
+        indices = torch.stack(
+            [ray_idx[within_range_idx[:, 0]], within_range_idx[:, 1], actor_idx[within_range_idx[:, 0]]], dim=-1
+        )
+        # Check each selected pair to see if it is inside the box
+        selected_smp = sample_mean_pos[indices[:, 0], indices[:, 1]]
+        selected_w2b = world2boxes[indices[:, 0], indices[:, 2]]
+        pos_in_box = transform_points_pairwise(selected_smp, selected_w2b)
+        inside_box = (pos_in_box.abs() < actor_bounds[indices[:, 2]]).all(dim=-1)
+        indices = indices[inside_box]
+        # # remove dupliacate ray-sample pairs (can happen if multiple actors are close to the same ray-sample pair)
+        # uniques, inverse_indices = torch.unique(indices[:, :2], dim=0, return_inverse=True, sorted=False)
+        # if uniques.shape[0] != indices.shape[0]:
+        #     first_occurrences = torch.zeros_like(inverse_indices, dtype=torch.bool)
+        #     first_occurrences.scatter_(0, inverse_indices, 1)
+        #     indices = indices[first_occurrences]
+        # NOTE: here we return potential duplicates, and will "randomly" discard them during feature merging
+        return indices[:, 0], indices[:, 1], indices[:, 2]
+
+    def _get_static_features(self, positions: GaussiansStd) -> Tensor:
+        features = self.static_grid(positions.mean.view(-1, 3))
+        features = self._rescale_grid_features(features, positions, self.static_grid)
+        return features
+
+    def _get_actor_features_fast(self, positions: GaussiansStd, actor_indices: Tensor) -> Tensor:
+        # Create 4D query positions
+        spatial_pos = positions.mean.view(-1, 3)
+        actor_indices = actor_indices.unsqueeze(-1).repeat(1, positions.mean.shape[-2])
+        actoridx_pos = actor_indices / self.actors.n_actors
+        pos = torch.cat([spatial_pos, actoridx_pos.view(-1, 1)], dim=-1)
+
+        actor_grid: HashEncoding = self.actor_grids[0]
+        features = actor_grid(pos)
+        features = self._rescale_grid_features(features, positions, actor_grid)
+
+        return features
+
+    def _get_actor_features_slow(self, positions: GaussiansStd, actor_indices: Tensor) -> Tensor:
+        feats = None
+        for i_actor in actor_indices.unique():
+            grid: HashEncoding = self.actor_grids[i_actor]
+            mask = (actor_indices == i_actor).view(-1)
+            actor_pos = positions[mask]
+            pos = actor_pos.mean.view(-1, 3) if isinstance(actor_pos, GaussiansStd) else actor_pos
+            actor_feats = grid(pos)
+            actor_feats = self._rescale_grid_features(actor_feats, actor_pos, grid)
+            if feats is None:
+                feats = torch.zeros((*mask.shape, actor_feats.shape[-1]), device=pos.device, dtype=actor_feats.dtype)
+            feats[mask] = actor_feats
+        return feats
+
+    def _rescale_grid_features(self, grid_features: Tensor, x: GaussiansStd, hash_encoding: HashEncoding) -> Tensor:
+        prefix_shape = list(x.mean.shape[:-1])
+        # Reshape into [..., n_samples, n_levels, n_features_per_level]
+        grid_feats = grid_features.view(prefix_shape + [hash_encoding.num_levels * hash_encoding.features_per_level])
+        grid_feats = grid_feats.unflatten(-1, (hash_encoding.num_levels, hash_encoding.features_per_level))
+        weights = 1 / (hash_encoding.scalings.squeeze(-1) * 2 * x.std).clamp_min(1.0)
+        grid_feats = (grid_feats * weights[..., None]).mean(dim=-3).flatten(-2, -1)
+        return grid_feats
diff --git a/nerfstudio/field_components/spatial_distortions.py b/nerfstudio/field_components/spatial_distortions.py
index 5e9aeab8..c1aaad85 100644
--- a/nerfstudio/field_components/spatial_distortions.py
+++ b/nerfstudio/field_components/spatial_distortions.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,14 +16,14 @@
 """Space distortions."""
 
 import abc
-from typing import Optional, Union
+from typing import Optional, Union, overload
 
 import torch
 from functorch import jacrev, vmap
 from jaxtyping import Float
 from torch import Tensor, nn
 
-from nerfstudio.utils.math import Gaussians
+from nerfstudio.utils.math import Gaussians, GaussiansStd
 
 
 class SpatialDistortion(nn.Module):
@@ -63,7 +64,19 @@ def __init__(self, order: Optional[Union[float, int]] = None) -> None:
         super().__init__()
         self.order = order
 
-    def forward(self, positions):
+    @overload
+    def forward(self, positions: Gaussians) -> Gaussians:
+        ...
+
+    @overload
+    def forward(self, positions: GaussiansStd) -> GaussiansStd:
+        ...
+
+    @overload
+    def forward(self, positions: Float[Tensor, "*bs 3"]) -> Float[Tensor, "*bs 3"]:
+        ...
+
+    def forward(self, positions: Union[Gaussians, GaussiansStd, Tensor]) -> Union[Gaussians, GaussiansStd, Tensor]:
         def contract(x):
             mag = torch.linalg.norm(x, ord=self.order, dim=-1)[..., None]
             return torch.where(mag < 1, x, (2 - (1 / mag)) * (x / mag))
@@ -87,4 +100,42 @@ def contract_gauss(x):
 
             return Gaussians(mean=means, cov=cov)
 
+        elif isinstance(positions, GaussiansStd):
+            # ZipNerf-style linearized contraction
+            means = positions.mean.clone()
+            std = positions.std.clone()
+            mag = torch.linalg.norm(means, ord=self.order, dim=-1)[..., None]
+            mask = mag < 1
+            clamped_mag = mag.clamp_min(1.0)
+            means = torch.where(mask, means, (2 - (1 / clamped_mag)) * (means / clamped_mag))
+            std_scaling = ((2 * clamped_mag - 1).pow(1 / 3) / clamped_mag) ** 2
+            std = torch.where(mask, std, std * std_scaling)
+            return GaussiansStd(mean=means, std=std)
+
         return contract(positions)
+
+
+class ScaledSceneContraction(SceneContraction):
+    """This is a SceneContraction with a scale factor applied before the contraction."""
+
+    def __init__(self, order: Optional[Union[float, int]] = None, scale: float = 1.0, normalize: bool = True) -> None:
+        super().__init__(order=order)
+        self.scale = scale
+        self.normalize = normalize
+
+    def forward(self, positions):
+        if isinstance(positions, Gaussians):
+            positions = super().forward(Gaussians(mean=positions.mean / self.scale, cov=positions.cov / self.scale**2))
+            if self.normalize:
+                positions.mean = (positions.mean + 2.0) / 4.0
+                positions.cov = positions.cov / 16.0
+        elif isinstance(positions, GaussiansStd):
+            positions = super().forward(GaussiansStd(mean=positions.mean / self.scale, std=positions.std / self.scale))
+            if self.normalize:
+                positions.mean = (positions.mean + 2.0) / 4.0
+                positions.std = positions.std / 4.0
+        else:
+            positions = super().forward(positions / self.scale)
+            if self.normalize:
+                positions = (positions + 2.0) / 4.0
+        return positions
diff --git a/nerfstudio/fields/density_fields.py b/nerfstudio/fields/density_fields.py
index d4f6f28f..b08892bf 100644
--- a/nerfstudio/fields/density_fields.py
+++ b/nerfstudio/fields/density_fields.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -93,11 +94,12 @@ def __init__(
             self.linear = torch.nn.Linear(self.encoding.get_out_dim(), 1)
 
     def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, None]:
+        # Normalize positions within box to [0, 1], points outside box are outside range
+        positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
         if self.spatial_distortion is not None:
-            positions = self.spatial_distortion(ray_samples.frustums.get_positions())
-            positions = (positions + 2.0) / 4.0
-        else:
-            positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
+            positions = positions * 2.0 - 1.0  # inside box from [0, 1] -> [-1, 1], outside [-inf, inf]
+            positions = self.spatial_distortion(positions)  # [-inf, inf] -> [-2, 2]
+            positions = (positions + 2.0) / 4.0  # [-2, 2] -> [0, 1]
         # Make sure the tcnn gets inputs between 0 and 1.
         selector = ((positions > 0.0) & (positions < 1.0)).all(dim=-1)
         positions = positions * selector[..., None]
diff --git a/nerfstudio/fields/generfacto_field.py b/nerfstudio/fields/generfacto_field.py
deleted file mode 100644
index ee31bcb0..00000000
--- a/nerfstudio/fields/generfacto_field.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# Copyright 2022 The Nerfstudio Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Field for Generfacto model
-"""
-
-
-from typing import Dict, Literal, Optional, Tuple
-
-import numpy as np
-import torch
-from torch import Tensor, nn
-from torch.nn.parameter import Parameter
-
-from nerfstudio.cameras.rays import RayBundle, RaySamples
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.field_components.activations import trunc_exp
-from nerfstudio.field_components.encodings import HashEncoding, SHEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.field_components.mlp import MLP
-from nerfstudio.fields.base_field import Field, get_normalized_directions
-
-
-class GenerfactoField(Field):
-    """Generfacto Field that uses TCNN
-
-    Args:
-        aabb: parameters of scene aabb bounds
-        num_layers: number of hidden layers
-        hidden_dim: dimension of hidden layers
-        geo_feat_dim: output geo feat dimensions
-        num_levels: number of levels of the hashmap for the base mlp
-        max_res: maximum resolution of the hashmap for the base mlp
-        log2_hashmap_size: size of the hashmap for the base mlp
-        num_layers_color: number of hidden layers for color network
-        hidden_dim_color: dimension of hidden layers for color network
-    """
-
-    def __init__(
-        self,
-        aabb: Tensor,
-        num_layers: int = 2,
-        hidden_dim: int = 64,
-        geo_feat_dim: int = 15,
-        num_levels: int = 16,
-        max_res: int = 256,
-        log2_hashmap_size: int = 19,
-        num_layers_color: int = 3,
-        hidden_dim_color: int = 64,
-        implementation: Literal["tcnn", "torch"] = "tcnn",
-    ) -> None:
-        super().__init__()
-
-        self.aabb = Parameter(aabb, requires_grad=False)
-        self.geo_feat_dim = geo_feat_dim
-
-        base_res = 16
-        features_per_level = 2
-        np.exp((np.log(max_res) - np.log(base_res)) / (num_levels - 1))
-
-        self.direction_encoding = SHEncoding(
-            levels=4,
-            implementation=implementation,
-        )
-
-        encoder = HashEncoding(
-            num_levels=num_levels,
-            min_res=base_res,
-            max_res=max_res,
-            log2_hashmap_size=log2_hashmap_size,
-            features_per_level=features_per_level,
-            implementation=implementation,
-        )
-        network = MLP(
-            in_dim=encoder.get_out_dim(),
-            num_layers=num_layers,
-            layer_width=hidden_dim,
-            out_dim=1 + self.geo_feat_dim,
-            activation=nn.ReLU(),
-            out_activation=None,
-            implementation=implementation,
-        )
-        self.mlp_base = torch.nn.Sequential(encoder, network)
-
-        self.mlp_background_color = MLP(
-            in_dim=self.direction_encoding.get_out_dim(),
-            num_layers=2,
-            layer_width=32,
-            out_dim=3,
-            activation=nn.ReLU(),
-            out_activation=nn.Sigmoid(),
-            implementation=implementation,
-        )
-
-        self.mlp_head = MLP(
-            in_dim=self.geo_feat_dim,
-            num_layers=num_layers_color,
-            layer_width=hidden_dim_color,
-            out_dim=3,
-            activation=nn.ReLU(),
-            out_activation=nn.Sigmoid(),
-            implementation=implementation,
-        )
-
-    def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
-        """Computes and returns the densities."""
-        positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
-        self._sample_locations = positions
-        if not self._sample_locations.requires_grad:
-            self._sample_locations.requires_grad = True
-        positions_flat = positions.view(-1, 3)
-        h = self.mlp_base(positions_flat).view(*ray_samples.frustums.shape, -1)
-        density_before_activation, base_mlp_out = torch.split(h, [1, self.geo_feat_dim], dim=-1)
-        self._density_before_activation = density_before_activation
-
-        # Rectifying the density with an exponential is much more stable than a ReLU or
-        # softplus, because it enables high post-activation (float32) density outputs
-        # from smaller internal (float16) parameters.
-        density = trunc_exp(density_before_activation.to(positions))
-        return density, base_mlp_out
-
-    def get_background_rgb(self, ray_bundle: RayBundle) -> Tensor:
-        """Predicts background colors at infinity."""
-        directions = get_normalized_directions(ray_bundle.directions)
-
-        outputs_shape = ray_bundle.directions.shape[:-1]
-        directions_flat = self.direction_encoding(directions.view(-1, 3))
-        background_rgb = self.mlp_background_color(directions_flat).view(*outputs_shape, -1).to(directions)
-
-        return background_rgb
-
-    def get_outputs(
-        self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None
-    ) -> Dict[FieldHeadNames, Tensor]:
-        assert density_embedding is not None
-        outputs = {}
-
-        directions = get_normalized_directions(ray_samples.frustums.directions)
-
-        outputs_shape = ray_samples.frustums.directions.shape[:-1]
-
-        h = density_embedding.view(-1, self.geo_feat_dim)
-
-        rgb = self.mlp_head(h).view(*outputs_shape, -1).to(directions)
-        outputs.update({FieldHeadNames.RGB: rgb})
-
-        return outputs
diff --git a/nerfstudio/fields/nerfacto_field.py b/nerfstudio/fields/nerfacto_field.py
index 52a4de4b..06137f77 100644
--- a/nerfstudio/fields/nerfacto_field.py
+++ b/nerfstudio/fields/nerfacto_field.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -203,12 +204,12 @@ def __init__(
 
     def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
         """Computes and returns the densities."""
+        # Normalize positions within box to [0, 1], points outside box are outside range
+        positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
         if self.spatial_distortion is not None:
-            positions = ray_samples.frustums.get_positions()
-            positions = self.spatial_distortion(positions)
-            positions = (positions + 2.0) / 4.0
-        else:
-            positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
+            positions = positions * 2.0 - 1.0  # inside box from [0, 1] -> [-1, 1], outside [-inf, inf]
+            positions = self.spatial_distortion(positions)  # [-inf, inf] -> [-2, 2]
+            positions = (positions + 2.0) / 4.0  # [-2, 2] -> [0, 1]
         # Make sure the tcnn gets inputs between 0 and 1.
         selector = ((positions > 0.0) & (positions < 1.0)).all(dim=-1)
         positions = positions * selector[..., None]
diff --git a/nerfstudio/fields/nerfw_field.py b/nerfstudio/fields/nerfw_field.py
deleted file mode 100644
index 1a5a16cd..00000000
--- a/nerfstudio/fields/nerfw_field.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Fields for nerf-w"""
-
-from typing import Dict, Optional, Tuple
-
-import torch
-from jaxtyping import Float
-from torch import Tensor, nn
-
-from nerfstudio.cameras.rays import RaySamples
-from nerfstudio.field_components.embedding import Embedding
-from nerfstudio.field_components.encodings import Encoding, Identity
-from nerfstudio.field_components.field_heads import (
-    DensityFieldHead,
-    FieldHeadNames,
-    RGBFieldHead,
-    TransientDensityFieldHead,
-    TransientRGBFieldHead,
-    UncertaintyFieldHead,
-)
-from nerfstudio.field_components.mlp import MLP
-from nerfstudio.fields.base_field import Field
-
-
-class VanillaNerfWField(Field):
-    """The NeRF-W field which has appearance and transient conditioning.
-
-    Args:
-        num_images: How many images exist in the dataset.
-        position_encoding: Position encoder.
-        direction_encoding: Direction encoder.
-        base_mlp_num_layers: Number of layers for base MLP.
-        base_mlp_layer_width: Width of base MLP layers.
-        head_mlp_num_layers: Number of layer for output head MLP.
-        head_mlp_layer_width: Width of output head MLP layers.
-        appearance_embedding_dim:: Dimension of appearance embedding.
-        transient_embedding_dim:: Dimension of transient embedding.
-        skip_connections: Where to add skip connection in base MLP.
-    """
-
-    def __init__(
-        self,
-        num_images: int,
-        position_encoding: Encoding = Identity(in_dim=3),
-        direction_encoding: Encoding = Identity(in_dim=3),
-        base_mlp_num_layers: int = 8,
-        base_mlp_layer_width: int = 256,
-        head_mlp_num_layers: int = 2,
-        head_mlp_layer_width: int = 128,
-        appearance_embedding_dim: int = 48,
-        transient_embedding_dim: int = 16,
-        skip_connections: Tuple[int] = (4,),
-    ) -> None:
-        super().__init__()
-        self.num_images = num_images
-        self.position_encoding = position_encoding
-        self.direction_encoding = direction_encoding
-        self.base_mlp_num_layers = base_mlp_num_layers
-        self.base_mlp_layer_width = base_mlp_layer_width
-        self.head_mlp_num_layers = head_mlp_num_layers
-        self.head_mlp_layer_width = head_mlp_layer_width
-        self.appearance_embedding_dim = appearance_embedding_dim
-        self.transient_embedding_dim = transient_embedding_dim
-
-        if self.appearance_embedding_dim > 0:
-            self.embedding_appearance = Embedding(self.num_images, self.appearance_embedding_dim)
-        else:
-            self.embedding_appearance = None
-        self.embedding_transient = Embedding(self.num_images, self.transient_embedding_dim)
-
-        self.mlp_base = MLP(
-            in_dim=self.position_encoding.get_out_dim(),
-            num_layers=base_mlp_num_layers,
-            layer_width=base_mlp_layer_width,
-            skip_connections=skip_connections,
-            out_activation=nn.ReLU(),
-        )
-        self.mlp_transient = MLP(
-            in_dim=self.mlp_base.get_out_dim() + self.embedding_transient.get_out_dim(),
-            out_dim=base_mlp_layer_width // 2,
-            num_layers=4,
-            layer_width=base_mlp_layer_width,
-            activation=nn.ReLU(),
-            out_activation=nn.ReLU(),
-        )
-        self.mlp_head = MLP(
-            in_dim=self.mlp_base.get_out_dim()
-            + self.direction_encoding.get_out_dim()
-            + (self.embedding_appearance.get_out_dim() if self.embedding_appearance is not None else 0),
-            num_layers=head_mlp_num_layers,
-            layer_width=head_mlp_layer_width,
-            out_activation=nn.ReLU(),
-        )
-
-        self.field_head_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
-        self.field_head_rgb = RGBFieldHead(in_dim=self.mlp_head.get_out_dim())
-
-        self.field_head_transient_uncertainty = UncertaintyFieldHead(in_dim=self.mlp_transient.get_out_dim())
-        self.field_head_transient_rgb = TransientRGBFieldHead(in_dim=self.mlp_transient.get_out_dim())
-        self.field_head_transient_density = TransientDensityFieldHead(in_dim=self.mlp_transient.get_out_dim())
-
-    def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
-        """Computes and returns the densities."""
-        encoded_xyz = self.position_encoding(ray_samples.frustums.get_positions())
-        encoded_xyz = self.position_encoding(ray_samples.frustums.get_positions())
-        base_mlp_out = self.mlp_base(encoded_xyz)
-        density = self.field_head_density(base_mlp_out)
-        return density, base_mlp_out
-
-    def get_outputs(
-        self, ray_samples: RaySamples, density_embedding: Optional[Float[Tensor, "*batch embedding_size"]] = None
-    ) -> Dict[FieldHeadNames, Tensor]:
-        """Returns the outputs of the NeRF-W field.
-
-        Args:
-            ray_samples: Ray samples.
-            density_embedding: Density embedding.
-
-        Returns:
-            Outputs of the NeRF-W field.
-        """
-        outputs = {}
-        encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
-        if ray_samples.camera_indices is None:
-            raise AttributeError("Camera indices are not provided.")
-        camera_indices = ray_samples.camera_indices.squeeze().to(ray_samples.frustums.origins.device)
-        mlp_in = [density_embedding, encoded_dir]
-        if self.embedding_appearance is not None:
-            embedded_appearance = self.embedding_appearance(camera_indices)
-            mlp_in.append(embedded_appearance)
-        mlp_head_out = self.mlp_head(torch.cat(mlp_in, dim=-1))
-        outputs[self.field_head_rgb.field_head_name] = self.field_head_rgb(mlp_head_out)  # static rgb
-        embedded_transient = self.embedding_transient(camera_indices)
-        transient_mlp_in = torch.cat([density_embedding, embedded_transient], dim=-1)  # type: ignore
-        transient_mlp_out = self.mlp_transient(transient_mlp_in)
-        outputs[self.field_head_transient_uncertainty.field_head_name] = self.field_head_transient_uncertainty(
-            transient_mlp_out
-        )  # uncertainty
-        outputs[self.field_head_transient_rgb.field_head_name] = self.field_head_transient_rgb(
-            transient_mlp_out
-        )  # transient rgb
-        outputs[self.field_head_transient_density.field_head_name] = self.field_head_transient_density(
-            transient_mlp_out
-        )  # transient density
-        return outputs
diff --git a/nerfstudio/fields/neurad_field.py b/nerfstudio/fields/neurad_field.py
new file mode 100644
index 00000000..7dab8bfc
--- /dev/null
+++ b/nerfstudio/fields/neurad_field.py
@@ -0,0 +1,216 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Field for compound nerf model, adds scene contraction and image embeddings to instant ngp
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, Literal, Optional, Tuple, Type
+
+import torch
+from torch import Tensor, nn
+
+from nerfstudio.cameras.rays import RaySamples
+from nerfstudio.configs.base_config import InstantiateConfig
+from nerfstudio.field_components.activations import trunc_exp
+from nerfstudio.field_components.encodings import SHEncoding
+from nerfstudio.field_components.field_heads import FieldHeadNames
+from nerfstudio.field_components.mlp import MLP
+from nerfstudio.field_components.neurad_encoding import (
+    ActorSettings,
+    NeuRADHashEncoding,
+    NeuRADHashEncodingConfig,
+    StaticSettings,
+)
+from nerfstudio.fields.base_field import Field, get_normalized_directions
+from nerfstudio.model_components.dynamic_actors import DynamicActors
+from nerfstudio.model_components.utils import SigmoidDensity
+
+EPS = 1.0e-7
+
+
+@dataclass
+class NeuRADFieldConfig(InstantiateConfig):
+    _target: Type = field(default_factory=lambda: NeuRADField)
+
+    grid: NeuRADHashEncodingConfig = field(
+        default_factory=lambda: NeuRADHashEncodingConfig(
+            require_actor_grad=True,
+            actor=ActorSettings(flip_prob=0.25),
+        )
+    )
+    """Hashgrid config"""
+
+    geo_hidden_dim: int = 32
+    """Dimensionality of the hidden units in the first mlp, describing geometry."""
+    geo_num_layers: int = 2
+    """Number of layers in the first, geometry, mlp."""
+    nff_hidden_dim: int = 32
+    """Dimensionality of the hidden units in the nff mlp."""
+    nff_num_layers: int = 3
+    """Number of layers in the second mlp."""
+    nff_out_dim: int = 32
+    """Dimensionality of the neural feature field output."""
+
+    num_multisamples: int = 1
+    """Number of multisamples to use for the fast gaussian approximation."""
+
+    use_sdf: bool = True
+    """Whether to learn an SDF or a more 'vanilla' density-field."""
+    sdf_beta: float = 20.0  # TODO: tune this (maybe 20.0 like in NeuSim)
+    """Slope of the sigmoid function used to convert SDF to density."""
+    learnable_beta: bool = True
+    """Whether to learn the beta (sdf to density) parameter or not."""
+
+
+class NeuRADField(Field):
+    """NeuRAD field."""
+
+    def __init__(
+        self,
+        config: NeuRADFieldConfig,
+        actors: DynamicActors,
+        static_scale: float,
+        implementation: Literal["tcnn", "torch"] = "tcnn",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.implementation = implementation
+
+        self.hashgrid: NeuRADHashEncoding = self.config.grid.setup(
+            dynamic_actors=actors,
+            static_scale=static_scale,
+            implementation=implementation,
+        )
+        self.geo_feat_dim = self.config.nff_out_dim
+        self.mlp_geo = MLP(
+            in_dim=self.hashgrid.get_out_dim(),
+            num_layers=self.config.geo_num_layers,
+            layer_width=self.config.geo_hidden_dim,
+            out_dim=self.geo_feat_dim + 1,
+            activation=nn.ReLU(),
+            out_activation=None,
+            implementation=implementation,
+        )
+        self.direction_encoding = SHEncoding(levels=4, implementation=implementation)
+        direction_dim = self.direction_encoding.get_out_dim()
+        self.mlp_feature = MLP(
+            in_dim=direction_dim + self.geo_feat_dim,
+            num_layers=self.config.nff_num_layers,
+            layer_width=self.config.nff_hidden_dim,
+            out_dim=self.config.nff_out_dim,
+            activation=nn.ReLU(),
+            out_activation=None,
+            implementation=implementation,
+        )
+        if self.config.use_sdf:
+            self.sdf_to_density = SigmoidDensity(self.config.sdf_beta, learnable_beta=self.config.learnable_beta)
+
+    def get_param_groups(self, param_groups: Dict):
+        """Get camera optimizer parameters"""
+        self.hashgrid.get_param_groups(param_groups)
+        param_groups["fields"] += list(self.mlp_geo.parameters()) + list(self.mlp_feature.parameters())
+        if self.config.use_sdf:
+            param_groups["fields"] += list(self.sdf_to_density.parameters())
+
+    def forward(self, ray_samples: RaySamples, compute_normals: bool = False) -> Dict[FieldHeadNames, Tensor]:
+        """Evaluates the field at points along the ray.
+
+        Args:
+            ray_samples: Samples to evaluate field on.
+        """
+        gaussians = ray_samples.frustums.get_fast_isotropic_gaussian(self.config.num_multisamples)
+        features, directions = self.hashgrid(gaussians, ray_samples.times, ray_samples.frustums.directions)
+
+        # First output is the geometry, second is the pass-along vector.
+        geo_out, geo_embedding = torch.split(self.mlp_geo(features), [1, self.geo_feat_dim], dim=-1)
+        geo_out = geo_out.view(*ray_samples.shape, 1)
+        direction_embedding = self.direction_encoding(get_normalized_directions(directions).view(-1, 3))
+        feature = geo_embedding + self.mlp_feature(torch.cat([geo_embedding, direction_embedding], dim=-1))
+        feature = feature.view(*ray_samples.shape, self.config.nff_out_dim)
+
+        outputs = {FieldHeadNames.FEATURE: feature}
+        if self.config.use_sdf:
+            signed_distance = geo_out
+            outputs[FieldHeadNames.SDF] = signed_distance
+            outputs[FieldHeadNames.ALPHA] = self.sdf_to_density(signed_distance)
+        else:
+            outputs[FieldHeadNames.DENSITY] = trunc_exp(geo_out)
+
+        return outputs
+
+
+@dataclass
+class NeuRADProposalFieldConfig(InstantiateConfig):
+    """Configuration of the NeuRAD proposal field."""
+
+    _target: Type = field(default_factory=lambda: NeuRADProposalField)
+    """Target class for instantiation."""
+    grid: NeuRADHashEncodingConfig = field(
+        default_factory=lambda: NeuRADHashEncodingConfig(
+            static=StaticSettings(
+                log2_hashmap_size=20,
+                num_levels=6,
+                max_res=4096,
+                base_res=128,
+                hashgrid_dim=1,
+            ),
+            actor=ActorSettings(
+                log2_hashmap_size=15,
+                num_levels=4,
+                base_res=64,
+                max_res=1024,
+                hashgrid_dim=1,
+            ),
+            require_actor_grad=False,  # No need for trajectory gradients in proposal field
+        )
+    )
+    """Hashgrid config"""
+    hidden_dim: int = 16
+    """Dimensionality of the hidden units in the density mlp."""
+
+
+class NeuRADProposalField(Field):
+    """Simplified NeuRAD field, for use as a proposal field."""
+
+    def __init__(
+        self,
+        config: NeuRADProposalFieldConfig,
+        actors: DynamicActors,
+        static_scale: float,
+        implementation: Literal["tcnn", "torch"] = "tcnn",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.implementation = implementation
+        self.hashgrid: NeuRADHashEncoding = self.config.grid.setup(
+            dynamic_actors=actors, static_scale=static_scale, implementation=implementation
+        )
+        self.density_decoder = nn.Linear(self.hashgrid.get_out_dim(), 1, bias=False)
+
+    def get_param_groups(self, param_groups: Dict):
+        """Get camera optimizer parameters"""
+        self.hashgrid.get_param_groups(param_groups)
+        param_groups["fields"] += list(self.density_decoder.parameters())
+
+    def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, None]:
+        pos = ray_samples.frustums.get_fast_isotropic_gaussian(num_multisamples=1)
+        density, _ = self.hashgrid(pos, ray_samples.times, None)
+        density = self.density_decoder(density)
+        density = trunc_exp(density.to(pos.dtype)).view(*ray_samples.shape, 1)
+        return density, None
+
+    def get_outputs(self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None) -> dict:
+        return {}
diff --git a/nerfstudio/fields/sdf_field.py b/nerfstudio/fields/sdf_field.py
deleted file mode 100644
index ed936d4b..00000000
--- a/nerfstudio/fields/sdf_field.py
+++ /dev/null
@@ -1,459 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Field for SDF based model, rather then estimating density to generate a surface,
-a signed distance function (SDF) for surface representation is used to help with extracting high fidelity surfaces
-"""
-
-from dataclasses import dataclass, field
-from typing import Dict, Literal, Optional, Type
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from jaxtyping import Float
-from torch import Tensor, nn
-from torch.nn.parameter import Parameter
-
-from nerfstudio.cameras.rays import RaySamples
-from nerfstudio.field_components.embedding import Embedding
-from nerfstudio.field_components.encodings import NeRFEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.field_components.spatial_distortions import SpatialDistortion
-from nerfstudio.fields.base_field import Field, FieldConfig
-from nerfstudio.utils.external import tcnn
-
-
-class LearnedVariance(nn.Module):
-    """Variance network in NeuS
-
-    Args:
-        init_val: initial value in NeuS variance network
-    """
-
-    variance: Tensor
-
-    def __init__(self, init_val):
-        super().__init__()
-        self.register_parameter("variance", nn.Parameter(init_val * torch.ones(1), requires_grad=True))
-
-    def forward(self, x: Float[Tensor, "1"]) -> Float[Tensor, "1"]:
-        """Returns current variance value"""
-        return torch.ones([len(x), 1], device=x.device) * torch.exp(self.variance * 10.0)
-
-    def get_variance(self) -> Float[Tensor, "1"]:
-        """return current variance value"""
-        return torch.exp(self.variance * 10.0).clip(1e-6, 1e6)
-
-
-@dataclass
-class SDFFieldConfig(FieldConfig):
-    """SDF Field Config"""
-
-    _target: Type = field(default_factory=lambda: SDFField)
-    num_layers: int = 8
-    """Number of layers for geometric network"""
-    hidden_dim: int = 256
-    """Number of hidden dimension of geometric network"""
-    geo_feat_dim: int = 256
-    """Dimension of geometric feature"""
-    num_layers_color: int = 4
-    """Number of layers for color network"""
-    hidden_dim_color: int = 256
-    """Number of hidden dimension of color network"""
-    appearance_embedding_dim: int = 32
-    """Dimension of appearance embedding"""
-    use_appearance_embedding: bool = False
-    """Whether to use appearance embedding"""
-    bias: float = 0.8
-    """Sphere size of geometric initialization"""
-    geometric_init: bool = True
-    """Whether to use geometric initialization"""
-    inside_outside: bool = True
-    """Whether to revert signed distance value, set to True for indoor scene"""
-    weight_norm: bool = True
-    """Whether to use weight norm for linear layer"""
-    use_grid_feature: bool = False
-    """Whether to use multi-resolution feature grids"""
-    divide_factor: float = 2.0
-    """Normalization factor for multi-resolution grids"""
-    beta_init: float = 0.1
-    """Init learnable beta value for transformation of sdf to density"""
-    encoding_type: Literal["hash", "periodic", "tensorf_vm"] = "hash"
-    num_levels: int = 16
-    """Number of encoding levels"""
-    max_res: int = 2048
-    """Maximum resolution of the encoding"""
-    base_res: int = 16
-    """Base resolution of the encoding"""
-    log2_hashmap_size: int = 19
-    """Size of the hash map"""
-    features_per_level: int = 2
-    """Number of features per encoding level"""
-    use_hash: bool = True
-    """Whether to use hash encoding"""
-    smoothstep: bool = True
-    """Whether to use the smoothstep function"""
-
-
-class SDFField(Field):
-    """
-    A field for Signed Distance Functions (SDF).
-
-    Args:
-        config: The configuration for the SDF field.
-        aabb: An axis-aligned bounding box for the SDF field.
-        num_images: The number of images for embedding appearance.
-        use_average_appearance_embedding: Whether to use average appearance embedding. Defaults to False.
-        spatial_distortion: The spatial distortion. Defaults to None.
-    """
-
-    config: SDFFieldConfig
-
-    def __init__(
-        self,
-        config: SDFFieldConfig,
-        aabb: Float[Tensor, "2 3"],
-        num_images: int,
-        use_average_appearance_embedding: bool = False,
-        spatial_distortion: Optional[SpatialDistortion] = None,
-    ) -> None:
-        super().__init__()
-        self.config = config
-
-        self.aabb = Parameter(aabb, requires_grad=False)
-
-        self.spatial_distortion = spatial_distortion
-        self.num_images = num_images
-
-        self.embedding_appearance = Embedding(self.num_images, self.config.appearance_embedding_dim)
-        self.use_average_appearance_embedding = use_average_appearance_embedding
-        self.use_grid_feature = self.config.use_grid_feature
-        self.divide_factor = self.config.divide_factor
-
-        growth_factor = np.exp((np.log(config.max_res) - np.log(config.base_res)) / (config.num_levels - 1))
-
-        if self.config.encoding_type == "hash":
-            # feature encoding
-            self.encoding = tcnn.Encoding(
-                n_input_dims=3,
-                encoding_config={
-                    "otype": "HashGrid" if config.use_hash else "DenseGrid",
-                    "n_levels": config.num_levels,
-                    "n_features_per_level": config.features_per_level,
-                    "log2_hashmap_size": config.log2_hashmap_size,
-                    "base_resolution": config.base_res,
-                    "per_level_scale": growth_factor,
-                    "interpolation": "Smoothstep" if config.smoothstep else "Linear",
-                },
-            )
-
-        # we concat inputs position ourselves
-        self.position_encoding = NeRFEncoding(
-            in_dim=3, num_frequencies=6, min_freq_exp=0.0, max_freq_exp=5.0, include_input=False
-        )
-
-        self.direction_encoding = NeRFEncoding(
-            in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=3.0, include_input=True
-        )
-
-        # initialize geometric network
-        self.initialize_geo_layers()
-
-        # deviation_network to compute alpha from sdf from NeuS
-        self.deviation_network = LearnedVariance(init_val=self.config.beta_init)
-
-        # color network
-        dims = [self.config.hidden_dim_color for _ in range(self.config.num_layers_color)]
-        # point, view_direction, normal, feature, embedding
-        in_dim = (
-            3
-            + self.direction_encoding.get_out_dim()
-            + 3
-            + self.config.geo_feat_dim
-            + self.embedding_appearance.get_out_dim()
-        )
-        dims = [in_dim] + dims + [3]
-        self.num_layers_color = len(dims)
-
-        for layer in range(0, self.num_layers_color - 1):
-            out_dim = dims[layer + 1]
-            lin = nn.Linear(dims[layer], out_dim)
-
-            if self.config.weight_norm:
-                lin = nn.utils.weight_norm(lin)
-            setattr(self, "clin" + str(layer), lin)
-
-        self.softplus = nn.Softplus(beta=100)
-        self.relu = nn.ReLU()
-        self.sigmoid = torch.nn.Sigmoid()
-
-        self._cos_anneal_ratio = 1.0
-
-        if self.use_grid_feature:
-            assert self.spatial_distortion is not None, "spatial distortion must be provided when using grid feature"
-
-    def initialize_geo_layers(self) -> None:
-        """
-        Initialize layers for geometric network (sdf)
-        """
-        # MLP with geometric initialization
-        dims = [self.config.hidden_dim for _ in range(self.config.num_layers)]
-        in_dim = 3 + self.position_encoding.get_out_dim() + self.encoding.n_output_dims
-        dims = [in_dim] + dims + [1 + self.config.geo_feat_dim]
-        self.num_layers = len(dims)
-        self.skip_in = [4]
-
-        for layer in range(0, self.num_layers - 1):
-            if layer + 1 in self.skip_in:
-                out_dim = dims[layer + 1] - dims[0]
-            else:
-                out_dim = dims[layer + 1]
-
-            lin = nn.Linear(dims[layer], out_dim)
-
-            if self.config.geometric_init:
-                if layer == self.num_layers - 2:
-                    if not self.config.inside_outside:
-                        torch.nn.init.normal_(lin.weight, mean=np.sqrt(np.pi) / np.sqrt(dims[layer]), std=0.0001)
-                        torch.nn.init.constant_(lin.bias, -self.config.bias)
-                    else:
-                        torch.nn.init.normal_(lin.weight, mean=-np.sqrt(np.pi) / np.sqrt(dims[layer]), std=0.0001)
-                        torch.nn.init.constant_(lin.bias, self.config.bias)
-                elif layer == 0:
-                    torch.nn.init.constant_(lin.bias, 0.0)
-                    torch.nn.init.constant_(lin.weight[:, 3:], 0.0)
-                    torch.nn.init.normal_(lin.weight[:, :3], 0.0, np.sqrt(2) / np.sqrt(out_dim))
-                elif layer in self.skip_in:
-                    torch.nn.init.constant_(lin.bias, 0.0)
-                    torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))
-                    torch.nn.init.constant_(lin.weight[:, -(dims[0] - 3) :], 0.0)
-                else:
-                    torch.nn.init.constant_(lin.bias, 0.0)
-                    torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))
-
-            if self.config.weight_norm:
-                lin = nn.utils.weight_norm(lin)
-            setattr(self, "glin" + str(layer), lin)
-
-    def set_cos_anneal_ratio(self, anneal: float) -> None:
-        """Set the anneal value for the proposal network."""
-        self._cos_anneal_ratio = anneal
-
-    def forward_geonetwork(self, inputs: Float[Tensor, "*batch 3"]) -> Float[Tensor, "*batch geo_features+1"]:
-        """forward the geonetwork"""
-        if self.use_grid_feature:
-            assert self.spatial_distortion is not None, "spatial distortion must be provided when using grid feature"
-            positions = self.spatial_distortion(inputs)
-            # map range [-2, 2] to [0, 1]
-            positions = (positions + 2.0) / 4.0
-            feature = self.encoding(positions)
-        else:
-            feature = torch.zeros_like(inputs[:, :1].repeat(1, self.encoding.n_output_dims))
-
-        pe = self.position_encoding(inputs)
-
-        inputs = torch.cat((inputs, pe, feature), dim=-1)
-
-        # Pass through layers
-        outputs = inputs
-
-        for layer in range(0, self.num_layers - 1):
-            lin = getattr(self, "glin" + str(layer))
-
-            if layer in self.skip_in:
-                outputs = torch.cat([outputs, inputs], 1) / np.sqrt(2)
-
-            outputs = lin(outputs)
-
-            if layer < self.num_layers - 2:
-                outputs = self.softplus(outputs)
-        return outputs
-
-    # TODO: fix ... in shape annotations.
-    def get_sdf(self, ray_samples: RaySamples) -> Float[Tensor, "num_samples ... 1"]:
-        """predict the sdf value for ray samples"""
-        positions = ray_samples.frustums.get_start_positions()
-        positions_flat = positions.view(-1, 3)
-        hidden_output = self.forward_geonetwork(positions_flat).view(*ray_samples.frustums.shape, -1)
-        sdf, _ = torch.split(hidden_output, [1, self.config.geo_feat_dim], dim=-1)
-        return sdf
-
-    def get_alpha(
-        self,
-        ray_samples: RaySamples,
-        sdf: Optional[Float[Tensor, "num_samples ... 1"]] = None,
-        gradients: Optional[Float[Tensor, "num_samples ... 1"]] = None,
-    ) -> Float[Tensor, "num_samples ... 1"]:
-        """compute alpha from sdf as in NeuS"""
-        if sdf is None or gradients is None:
-            inputs = ray_samples.frustums.get_start_positions()
-            inputs.requires_grad_(True)
-            with torch.enable_grad():
-                hidden_output = self.forward_geonetwork(inputs)
-                sdf, _ = torch.split(hidden_output, [1, self.config.geo_feat_dim], dim=-1)
-            d_output = torch.ones_like(sdf, requires_grad=False, device=sdf.device)
-            gradients = torch.autograd.grad(
-                outputs=sdf,
-                inputs=inputs,
-                grad_outputs=d_output,
-                create_graph=True,
-                retain_graph=True,
-                only_inputs=True,
-            )[0]
-
-        inv_s = self.deviation_network.get_variance()  # Single parameter
-
-        true_cos = (ray_samples.frustums.directions * gradients).sum(-1, keepdim=True)
-
-        # anneal as NeuS
-        cos_anneal_ratio = self._cos_anneal_ratio
-
-        # "cos_anneal_ratio" grows from 0 to 1 in the beginning training iterations. The anneal strategy below makes
-        # the cos value "not dead" at the beginning training iterations, for better convergence.
-        iter_cos = -(
-            F.relu(-true_cos * 0.5 + 0.5) * (1.0 - cos_anneal_ratio) + F.relu(-true_cos) * cos_anneal_ratio
-        )  # always non-positive
-
-        # Estimate signed distances at section points
-        estimated_next_sdf = sdf + iter_cos * ray_samples.deltas * 0.5
-        estimated_prev_sdf = sdf - iter_cos * ray_samples.deltas * 0.5
-
-        prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_s)
-        next_cdf = torch.sigmoid(estimated_next_sdf * inv_s)
-
-        p = prev_cdf - next_cdf
-        c = prev_cdf
-
-        alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0)
-
-        return alpha
-
-    def get_density(self, ray_samples: RaySamples):
-        raise NotImplementedError
-
-    def get_colors(
-        self,
-        points: Float[Tensor, "*batch 3"],
-        directions: Float[Tensor, "*batch 3"],
-        normals: Float[Tensor, "*batch 3"],
-        geo_features: Float[Tensor, "*batch geo_feat_dim"],
-        camera_indices: Tensor,
-    ) -> Float[Tensor, "*batch 3"]:
-        """compute colors"""
-        d = self.direction_encoding(directions)
-
-        # appearance
-        if self.training:
-            embedded_appearance = self.embedding_appearance(camera_indices)
-            # set it to zero if don't use it
-            if not self.config.use_appearance_embedding:
-                embedded_appearance = torch.zeros_like(embedded_appearance)
-        else:
-            if self.use_average_appearance_embedding:
-                embedded_appearance = torch.ones(
-                    (*directions.shape[:-1], self.config.appearance_embedding_dim), device=directions.device
-                ) * self.embedding_appearance.mean(dim=0)
-            else:
-                embedded_appearance = torch.zeros(
-                    (*directions.shape[:-1], self.config.appearance_embedding_dim), device=directions.device
-                )
-
-        hidden_input = torch.cat(
-            [
-                points,
-                d,
-                normals,
-                geo_features.view(-1, self.config.geo_feat_dim),
-                embedded_appearance.view(-1, self.config.appearance_embedding_dim),
-            ],
-            dim=-1,
-        )
-
-        for layer in range(0, self.num_layers_color - 1):
-            lin = getattr(self, "clin" + str(layer))
-
-            hidden_input = lin(hidden_input)
-
-            if layer < self.num_layers_color - 2:
-                hidden_input = self.relu(hidden_input)
-
-        rgb = self.sigmoid(hidden_input)
-
-        return rgb
-
-    def get_outputs(
-        self,
-        ray_samples: RaySamples,
-        density_embedding: Optional[Tensor] = None,
-        return_alphas: bool = False,
-    ) -> Dict[FieldHeadNames, Tensor]:
-        """compute output of ray samples"""
-        if ray_samples.camera_indices is None:
-            raise AttributeError("Camera indices are not provided.")
-
-        outputs = {}
-
-        camera_indices = ray_samples.camera_indices.squeeze()
-
-        inputs = ray_samples.frustums.get_start_positions()
-        inputs = inputs.view(-1, 3)
-
-        directions = ray_samples.frustums.directions
-        directions_flat = directions.reshape(-1, 3)
-
-        inputs.requires_grad_(True)
-        with torch.enable_grad():
-            hidden_output = self.forward_geonetwork(inputs)
-            sdf, geo_feature = torch.split(hidden_output, [1, self.config.geo_feat_dim], dim=-1)
-        d_output = torch.ones_like(sdf, requires_grad=False, device=sdf.device)
-        gradients = torch.autograd.grad(
-            outputs=sdf, inputs=inputs, grad_outputs=d_output, create_graph=True, retain_graph=True, only_inputs=True
-        )[0]
-
-        rgb = self.get_colors(inputs, directions_flat, gradients, geo_feature, camera_indices)
-
-        rgb = rgb.view(*ray_samples.frustums.directions.shape[:-1], -1)
-        sdf = sdf.view(*ray_samples.frustums.directions.shape[:-1], -1)
-        gradients = gradients.view(*ray_samples.frustums.directions.shape[:-1], -1)
-        normals = torch.nn.functional.normalize(gradients, p=2, dim=-1)
-
-        outputs.update(
-            {
-                FieldHeadNames.RGB: rgb,
-                FieldHeadNames.SDF: sdf,
-                FieldHeadNames.NORMALS: normals,
-                FieldHeadNames.GRADIENT: gradients,
-            }
-        )
-
-        if return_alphas:
-            alphas = self.get_alpha(ray_samples, sdf, gradients)
-            outputs.update({FieldHeadNames.ALPHA: alphas})
-
-        return outputs
-
-    def forward(
-        self, ray_samples: RaySamples, compute_normals: bool = False, return_alphas: bool = False
-    ) -> Dict[FieldHeadNames, Tensor]:
-        """Evaluates the field at points along the ray.
-
-        Args:
-            ray_samples: Samples to evaluate field on.
-            compute normals: not currently used in this implementation.
-            return_alphas: Whether to return alpha values
-        """
-        field_outputs = self.get_outputs(ray_samples, return_alphas=return_alphas)
-        return field_outputs
diff --git a/nerfstudio/fields/semantic_nerf_field.py b/nerfstudio/fields/semantic_nerf_field.py
deleted file mode 100644
index 62b75094..00000000
--- a/nerfstudio/fields/semantic_nerf_field.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Semantic NeRF field implementation.
-"""
-from typing import Dict, Optional, Tuple
-
-import torch
-from torch import Tensor, nn
-
-from nerfstudio.cameras.rays import RaySamples
-from nerfstudio.field_components.encodings import Encoding, Identity
-from nerfstudio.field_components.field_heads import DensityFieldHead, FieldHeadNames, RGBFieldHead, SemanticFieldHead
-from nerfstudio.field_components.mlp import MLP
-from nerfstudio.fields.base_field import Field
-
-
-class SemanticNerfField(Field):
-    """Semantic-NeRF field
-
-    Args:
-        num_semantic_classes: Number of distinct semantic classes.
-        position_encoding: Position encoder.
-        direction_encoding: Direction encoder.
-        base_mlp_num_layers: Number of layers for base MLP.
-        base_mlp_layer_width: Width of base MLP layers.
-        head_mlp_num_layers: Number of layer for output head MLP.
-        head_mlp_layer_width: Width of output head MLP layers.
-        skip_connections: Where to add skip connection in base MLP.
-    """
-
-    def __init__(
-        self,
-        num_semantic_classes: int,
-        position_encoding: Encoding = Identity(in_dim=3),
-        direction_encoding: Encoding = Identity(in_dim=3),
-        base_mlp_num_layers: int = 8,
-        base_mlp_layer_width: int = 256,
-        head_mlp_num_layers: int = 2,
-        head_mlp_layer_width: int = 128,
-        skip_connections: Tuple[int] = (4,),
-    ) -> None:
-        super().__init__()
-        self.num_semantic_classes = num_semantic_classes
-        self.position_encoding = position_encoding
-        self.direction_encoding = direction_encoding
-        self.mlp_base = MLP(
-            in_dim=self.position_encoding.get_out_dim(),
-            num_layers=base_mlp_num_layers,
-            layer_width=base_mlp_layer_width,
-            skip_connections=skip_connections,
-            out_activation=nn.ReLU(),
-        )
-        self.mlp_head = MLP(
-            in_dim=self.mlp_base.get_out_dim() + self.direction_encoding.get_out_dim(),
-            num_layers=head_mlp_num_layers,
-            layer_width=head_mlp_layer_width,
-            out_activation=nn.ReLU(),
-        )
-        self.mlp_semantic = MLP(
-            in_dim=self.mlp_head.get_out_dim(),
-            layer_width=self.mlp_head.layer_width // 2,
-            num_layers=1,
-            activation=nn.ReLU(),
-            out_activation=nn.ReLU(),
-        )
-        self.field_head_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
-        self.field_head_rgb = RGBFieldHead(in_dim=self.mlp_head.get_out_dim())
-        self.field_head_semantic = SemanticFieldHead(
-            in_dim=self.mlp_semantic.get_out_dim(), num_classes=self.num_semantic_classes
-        )
-
-    def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
-        encoded_xyz = self.position_encoding(ray_samples.frustums.get_positions())
-        base_mlp_out = self.mlp_base(encoded_xyz)
-        density = self.field_head_density(base_mlp_out)
-        return density, base_mlp_out
-
-    def get_outputs(
-        self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None
-    ) -> Dict[FieldHeadNames, Tensor]:
-        encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
-        mlp_out = self.mlp_head(torch.cat([encoded_dir, density_embedding], dim=-1))  # type: ignore
-        outputs = {}
-        # rgb
-        outputs[self.field_head_rgb.field_head_name] = self.field_head_rgb(mlp_out)
-        # semantic
-        mlp_out_sem = self.mlp_semantic(mlp_out)
-        outputs[self.field_head_semantic.field_head_name] = self.field_head_semantic(mlp_out_sem)
-        return outputs
diff --git a/nerfstudio/fields/tensorf_field.py b/nerfstudio/fields/tensorf_field.py
deleted file mode 100644
index b3aba4e5..00000000
--- a/nerfstudio/fields/tensorf_field.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""TensoRF Field"""
-
-
-from typing import Dict, Optional
-
-import torch
-from torch import Tensor, nn
-from torch.nn.parameter import Parameter
-
-from nerfstudio.cameras.rays import RaySamples
-from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.field_components.encodings import Encoding, Identity, SHEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames, RGBFieldHead
-from nerfstudio.field_components.mlp import MLP
-from nerfstudio.fields.base_field import Field
-
-
-class TensoRFField(Field):
-    """TensoRF Field"""
-
-    def __init__(
-        self,
-        aabb: Tensor,
-        # the aabb bounding box of the dataset
-        feature_encoding: Encoding = Identity(in_dim=3),
-        # the encoding method used for appearance encoding outputs
-        direction_encoding: Encoding = Identity(in_dim=3),
-        # the encoding method used for ray direction
-        density_encoding: Encoding = Identity(in_dim=3),
-        # the tensor encoding method used for scene density
-        color_encoding: Encoding = Identity(in_dim=3),
-        # the tensor encoding method used for scene color
-        appearance_dim: int = 27,
-        # the number of dimensions for the appearance embedding
-        head_mlp_num_layers: int = 2,
-        # number of layers for the MLP
-        head_mlp_layer_width: int = 128,
-        # layer width for the MLP
-        use_sh: bool = False,
-        # whether to use spherical harmonics as the feature decoding function
-        sh_levels: int = 2,
-        # number of levels to use for spherical harmonics
-    ) -> None:
-        super().__init__()
-        self.aabb = Parameter(aabb, requires_grad=False)
-        self.feature_encoding = feature_encoding
-        self.direction_encoding = direction_encoding
-        self.density_encoding = density_encoding
-        self.color_encoding = color_encoding
-
-        self.mlp_head = MLP(
-            in_dim=appearance_dim + 3 + self.direction_encoding.get_out_dim() + self.feature_encoding.get_out_dim(),
-            num_layers=head_mlp_num_layers,
-            layer_width=head_mlp_layer_width,
-            activation=nn.ReLU(),
-            out_activation=nn.ReLU(),
-        )
-
-        self.use_sh = use_sh
-
-        if self.use_sh:
-            self.sh = SHEncoding(sh_levels)
-            self.B = nn.Linear(
-                in_features=self.color_encoding.get_out_dim(), out_features=3 * self.sh.get_out_dim(), bias=False
-            )
-        else:
-            self.B = nn.Linear(in_features=self.color_encoding.get_out_dim(), out_features=appearance_dim, bias=False)
-
-        self.field_output_rgb = RGBFieldHead(in_dim=self.mlp_head.get_out_dim(), activation=nn.Sigmoid())
-
-    def get_density(self, ray_samples: RaySamples) -> Tensor:
-        positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
-        positions = positions * 2 - 1
-        density = self.density_encoding(positions)
-        density_enc = torch.sum(density, dim=-1)[:, :, None]
-        relu = torch.nn.ReLU()
-        density_enc = relu(density_enc)
-        return density_enc
-
-    def get_outputs(self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None) -> Tensor:
-        d = ray_samples.frustums.directions
-        positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
-        positions = positions * 2 - 1
-        rgb_features = self.color_encoding(positions)
-        rgb_features = self.B(rgb_features)
-
-        if self.use_sh:
-            sh_mult = self.sh(d)[:, :, None]
-            rgb_sh = rgb_features.view(sh_mult.shape[0], sh_mult.shape[1], 3, sh_mult.shape[-1])
-            rgb = torch.relu(torch.sum(sh_mult * rgb_sh, dim=-1) + 0.5)
-        else:
-            d_encoded = self.direction_encoding(d)
-            rgb_features_encoded = self.feature_encoding(rgb_features)
-
-            out = self.mlp_head(torch.cat([rgb_features, d, rgb_features_encoded, d_encoded], dim=-1))  # type: ignore
-            rgb = self.field_output_rgb(out)
-
-        return rgb
-
-    def forward(
-        self,
-        ray_samples: RaySamples,
-        compute_normals: bool = False,
-        mask: Optional[Tensor] = None,
-        bg_color: Optional[Tensor] = None,
-    ) -> Dict[FieldHeadNames, Tensor]:
-        if compute_normals is True:
-            raise ValueError("Surface normals are not currently supported with TensoRF")
-        if mask is not None and bg_color is not None:
-            base_density = torch.zeros(ray_samples.shape)[:, :, None].to(mask.device)
-            base_rgb = bg_color.repeat(ray_samples[:, :, None].shape)
-            if mask.any():
-                input_rays = ray_samples[mask, :]
-                density = self.get_density(input_rays)
-                rgb = self.get_outputs(input_rays, None)
-
-                base_density[mask] = density
-                base_rgb[mask] = rgb
-
-                base_density.requires_grad_()
-                base_rgb.requires_grad_()
-
-            density = base_density
-            rgb = base_rgb
-        else:
-            density = self.get_density(ray_samples)
-            rgb = self.get_outputs(ray_samples, None)
-
-        return {FieldHeadNames.DENSITY: density, FieldHeadNames.RGB: rgb}
diff --git a/nerfstudio/fields/vanilla_nerf_field.py b/nerfstudio/fields/vanilla_nerf_field.py
deleted file mode 100644
index efb3589e..00000000
--- a/nerfstudio/fields/vanilla_nerf_field.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Classic NeRF field"""
-
-
-from typing import Dict, Optional, Tuple, Type
-
-import torch
-from torch import Tensor, nn
-
-from nerfstudio.cameras.rays import RaySamples
-from nerfstudio.field_components.encodings import Encoding, Identity
-from nerfstudio.field_components.field_heads import DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead
-from nerfstudio.field_components.mlp import MLP
-from nerfstudio.field_components.spatial_distortions import SpatialDistortion
-from nerfstudio.fields.base_field import Field
-
-
-class NeRFField(Field):
-    """NeRF Field
-
-    Args:
-        position_encoding: Position encoder.
-        direction_encoding: Direction encoder.
-        base_mlp_num_layers: Number of layers for base MLP.
-        base_mlp_layer_width: Width of base MLP layers.
-        head_mlp_num_layers: Number of layer for output head MLP.
-        head_mlp_layer_width: Width of output head MLP layers.
-        skip_connections: Where to add skip connection in base MLP.
-        use_integrated_encoding: Used integrated samples as encoding input.
-        spatial_distortion: Spatial distortion.
-    """
-
-    def __init__(
-        self,
-        position_encoding: Encoding = Identity(in_dim=3),
-        direction_encoding: Encoding = Identity(in_dim=3),
-        base_mlp_num_layers: int = 8,
-        base_mlp_layer_width: int = 256,
-        head_mlp_num_layers: int = 2,
-        head_mlp_layer_width: int = 128,
-        skip_connections: Tuple[int] = (4,),
-        field_heads: Optional[Tuple[Type[FieldHead]]] = (RGBFieldHead,),
-        use_integrated_encoding: bool = False,
-        spatial_distortion: Optional[SpatialDistortion] = None,
-    ) -> None:
-        super().__init__()
-        self.position_encoding = position_encoding
-        self.direction_encoding = direction_encoding
-        self.use_integrated_encoding = use_integrated_encoding
-        self.spatial_distortion = spatial_distortion
-
-        self.mlp_base = MLP(
-            in_dim=self.position_encoding.get_out_dim(),
-            num_layers=base_mlp_num_layers,
-            layer_width=base_mlp_layer_width,
-            skip_connections=skip_connections,
-            out_activation=nn.ReLU(),
-        )
-        self.field_output_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
-
-        if field_heads:
-            self.mlp_head = MLP(
-                in_dim=self.mlp_base.get_out_dim() + self.direction_encoding.get_out_dim(),
-                num_layers=head_mlp_num_layers,
-                layer_width=head_mlp_layer_width,
-                out_activation=nn.ReLU(),
-            )
-        self.field_heads = nn.ModuleList([field_head() for field_head in field_heads] if field_heads else [])  # type: ignore
-        for field_head in self.field_heads:
-            field_head.set_in_dim(self.mlp_head.get_out_dim())  # type: ignore
-
-    def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
-        if self.use_integrated_encoding:
-            gaussian_samples = ray_samples.frustums.get_gaussian_blob()
-            if self.spatial_distortion is not None:
-                gaussian_samples = self.spatial_distortion(gaussian_samples)
-            encoded_xyz = self.position_encoding(gaussian_samples.mean, covs=gaussian_samples.cov)
-        else:
-            positions = ray_samples.frustums.get_positions()
-            if self.spatial_distortion is not None:
-                positions = self.spatial_distortion(positions)
-            encoded_xyz = self.position_encoding(positions)
-        base_mlp_out = self.mlp_base(encoded_xyz)
-        density = self.field_output_density(base_mlp_out)
-        return density, base_mlp_out
-
-    def get_outputs(
-        self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None
-    ) -> Dict[FieldHeadNames, Tensor]:
-        outputs = {}
-        for field_head in self.field_heads:
-            encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
-            mlp_out = self.mlp_head(torch.cat([encoded_dir, density_embedding], dim=-1))  # type: ignore
-            outputs[field_head.field_head_name] = field_head(mlp_out)
-        return outputs
diff --git a/nerfstudio/model_components/cnns.py b/nerfstudio/model_components/cnns.py
new file mode 100644
index 00000000..678f4c46
--- /dev/null
+++ b/nerfstudio/model_components/cnns.py
@@ -0,0 +1,66 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from torch import nn
+
+
+class ResidualBlock(nn.Module):
+    """Abstract Residual Block class."""
+
+    def __init__(self, in_dim: int, dim: int) -> None:
+        super().__init__()
+        if in_dim != dim:
+            self.res_branch = nn.Conv2d(in_dim, dim, kernel_size=1)
+        else:
+            self.res_branch = nn.Identity()
+        self.main_branch = nn.Identity()
+        self.final_activation = nn.ReLU(inplace=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.final_activation(self.res_branch(x) + self.main_branch(x))
+
+
+class BasicBlock(ResidualBlock):
+    """Basic residual block."""
+
+    def __init__(self, in_dim: int, dim: int, kernel_size: int, padding: int, use_bn: bool = False):
+        super().__init__(in_dim, dim)
+        self.main_branch = nn.Sequential(
+            nn.Conv2d(in_dim, dim, kernel_size=kernel_size, padding=padding),
+            nn.BatchNorm2d(dim) if use_bn else nn.Identity(),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(dim, dim, kernel_size=kernel_size, padding=padding),
+            nn.BatchNorm2d(dim) if use_bn else nn.Identity(),
+        )
+
+
+class BottleneckBlock(ResidualBlock):
+    """Simple residual bottleneck block."""
+
+    def __init__(
+        self, in_dim: int, dim: int, kernel_size: int, padding: int, channel_multiplier: int = 1, use_bn: bool = False
+    ):
+        super().__init__(in_dim, dim)
+        mid_dim = channel_multiplier * dim
+        self.main_branch = nn.Sequential(
+            nn.Conv2d(in_dim, mid_dim, kernel_size=1),
+            nn.BatchNorm2d(mid_dim) if use_bn else nn.Identity(),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(mid_dim, mid_dim, kernel_size=kernel_size, padding=padding),
+            nn.BatchNorm2d(mid_dim) if use_bn else nn.Identity(),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(mid_dim, dim, kernel_size=1),
+            nn.BatchNorm2d(mid_dim) if use_bn else nn.Identity(),
+        )
diff --git a/nerfstudio/model_components/dynamic_actors.py b/nerfstudio/model_components/dynamic_actors.py
new file mode 100644
index 00000000..3fbb4352
--- /dev/null
+++ b/nerfstudio/model_components/dynamic_actors.py
@@ -0,0 +1,188 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Type
+
+import torch
+from torch import Tensor, nn
+
+from nerfstudio.cameras.camera_utils import matrix_to_rotation_6d, rotation_6d_to_matrix
+from nerfstudio.configs.base_config import InstantiateConfig
+from nerfstudio.utils import poses as pose_utils
+from nerfstudio.utils.poses import interpolate_trajectories_6d
+from nerfstudio.viewer.server.viewer_elements import ViewerSlider
+
+
+@dataclass
+class DynamicActorsConfig(InstantiateConfig):
+    """Configuration of dynamic actors."""
+
+    _target: Type = field(default_factory=lambda: DynamicActors)
+
+    optimize_trajectories: bool = True
+    """Whether to optimize the trajectories or not."""
+    actor_bbox_padding: Tuple[float, float, float] = (0.25, 0.25, 0.1)
+    """Padding to add to the bounding boxes of the actors (wlh order, in meters)."""
+
+
+class DynamicActors(nn.Module):
+    config: DynamicActorsConfig
+
+    def __init__(self, config: DynamicActorsConfig, trajectories: List[dict]):
+        super().__init__()
+        self.config = config
+
+        self._populate_actors(trajectories)
+        self.requires_grad_(self.config.optimize_trajectories)
+
+        self.actor_editing = {
+            "lateral": 0.0,
+            "longitudinal": 0.0,
+            "rotation": 0.0,
+        }
+        self.actor_lateral_shift = ViewerSlider(
+            name="Actor lateral shift (m)",
+            default_value=self.actor_editing["lateral"],
+            min_value=-3.0,
+            max_value=3.0,
+            step=0.1,
+            cb_hook=lambda obj: self.actor_editing.update({"lateral": obj.value}),
+        )
+
+        self.actor_longitudinal_shift = ViewerSlider(
+            name="Actor longitudinal shift (m)",
+            default_value=self.actor_editing["longitudinal"],
+            min_value=-3.0,
+            max_value=3.0,
+            step=0.1,
+            cb_hook=lambda obj: self.actor_editing.update({"longitudinal": obj.value}),
+        )
+
+        self.actor_rotation_shift = ViewerSlider(
+            name="Actor rotation shift (m)",
+            default_value=self.actor_editing["rotation"],
+            min_value=-3.14,
+            max_value=3.14,
+            step=0.1,
+            cb_hook=lambda obj: self.actor_editing.update({"rotation": obj.value}),
+        )
+
+    def actor_bounds(self):
+        return self.actor_sizes / 2 + self.actor_padding
+
+    def _populate_actors(self, trajectories: List[dict]) -> None:
+        unique_timestamps = torch.tensor(sorted(list({t.item() for traj in trajectories for t in traj["timestamps"]})))
+        self.n_actors = len(trajectories)
+        self.n_times = len(unique_timestamps)
+
+        actor_poses_at_time = (
+            torch.eye(4, dtype=torch.float32).view(1, 1, 4, 4).repeat(self.n_times, self.n_actors, 1, 1)
+        )
+        actor_present_at_time = torch.zeros((self.n_times, self.n_actors), dtype=torch.bool)
+        actor_sizes = torch.zeros((self.n_actors, 3), dtype=torch.float32)
+        actor_symmetric = torch.zeros((self.n_actors,), dtype=torch.bool)
+        actor_deformable = torch.zeros((self.n_actors,), dtype=torch.bool)
+
+        for actor_index, traj in enumerate(trajectories):
+            actor_sizes[actor_index] = traj["dims"]
+            actor_symmetric[actor_index] = traj["symmetric"]
+            actor_deformable[actor_index] = traj["deformable"]
+
+            for time_index, t in enumerate(unique_timestamps):
+                time_diff = (traj["timestamps"] - t).abs()
+                traj_time_index = time_diff.argmin(dim=0)
+                if time_diff[traj_time_index] < 1e-4:
+                    actor_present_at_time[time_index, actor_index] = True
+                    actor_poses_at_time[time_index, actor_index] = traj["poses"][traj_time_index]
+                else:
+                    # TODO(carlinds): Is this hack needed anymore? Don't we already do the interpolation for all
+                    # timesteps in the dataparser?
+                    # TODO: This is a hack to make sure that all timestamps are present. This is needed because the
+                    #  trajectory optimizer assumes that all timestamps are present.
+                    # we duplicate the closest timestamp
+                    actor_poses_at_time[time_index, actor_index] = traj["poses"][traj_time_index]
+            assert actor_present_at_time[:, actor_index].sum() == len(
+                traj["timestamps"].unique()
+            ), "Failed to populate all timestamps for actor!"
+
+        self.register_buffer("unique_timestamps", unique_timestamps)
+        self.register_buffer("actor_poses_at_time", actor_poses_at_time)
+        self.register_buffer("actor_present_at_time", actor_present_at_time)
+        self.register_buffer("actor_sizes", actor_sizes)  # wlh
+        self.register_buffer("actor_symmetric", actor_symmetric)
+        self.register_buffer("actor_deformable", actor_deformable)
+        self.register_buffer("actor_padding", torch.tensor(self.config.actor_bbox_padding))
+        # this seems silly but allows us to duplicate actors during rendering
+        self.register_buffer("actor_to_id", torch.arange(self.n_actors, dtype=torch.int64))
+
+        # optimizable parameters
+        self.actor_positions = nn.Parameter(self.actor_poses_at_time[..., :3, 3])
+        self.actor_rotations_6d = nn.Parameter(matrix_to_rotation_6d(self.actor_poses_at_time[..., :3, :3]))
+        self.register_buffer("initial_positions", self.actor_positions.clone())
+        self.register_buffer("initial_rotations_6d", self.actor_rotations_6d.clone())
+
+    def get_poses_3x4(self):
+        rotations = rotation_6d_to_matrix(self.actor_rotations_6d)
+        return torch.cat([rotations, self.actor_positions.unsqueeze(-1)], dim=-1)
+
+    def get_world2boxes(self, query_times: Tensor, flatten: bool = True):
+        boxes2world, *extra = self.get_boxes2world(query_times, flatten)
+        world2boxes = pose_utils.inverse(boxes2world)
+        return world2boxes, *extra
+
+    def edit_boxes2world(self, boxes2world: Tensor):
+        with torch.no_grad():
+            if abs(self.actor_editing["longitudinal"]) > 0.0 or abs(self.actor_editing["lateral"]) > 0.0:
+                boxes2world[..., 3] = boxes2world @ torch.tensor(
+                    [self.actor_editing["lateral"], self.actor_editing["longitudinal"], 0.0, 1.0],
+                    device=boxes2world.device,
+                )
+
+            if abs(self.actor_editing["rotation"]) > 0.0:
+                rotation_yaw = torch.tensor(
+                    [
+                        [math.cos(self.actor_editing["rotation"]), -math.sin(self.actor_editing["rotation"]), 0.0],
+                        [math.sin(self.actor_editing["rotation"]), math.cos(self.actor_editing["rotation"]), 0.0],
+                        [0.0, 0.0, 1.0],
+                    ],
+                    device=boxes2world.device,
+                )
+                boxes2world[..., :3, :3] = rotation_yaw @ boxes2world[..., :3, :3]
+        return boxes2world
+
+    def get_boxes2world(self, query_times: Tensor, flatten: bool = True):
+        poses, *extra = interpolate_trajectories_6d(
+            torch.cat([self.actor_rotations_6d, self.actor_positions], dim=-1),
+            self.unique_timestamps,
+            query_times,
+            self.actor_present_at_time,
+            flatten=flatten,
+        )
+        boxes2world = torch.cat([rotation_6d_to_matrix(poses[..., :6]), poses[..., 6:].unsqueeze(-1)], dim=-1)
+
+        if not self.training:
+            boxes2world = self.edit_boxes2world(boxes2world)
+
+        boxes2world = pose_utils.to4x4(boxes2world)
+        return boxes2world, *extra
+
+    def requires_grad_(self, requires: bool) -> None:
+        self.actor_positions.requires_grad_(requires)
+        self.actor_rotations_6d.requires_grad_(requires)
+
+    def get_param_groups(self, param_groups: Dict):
+        if self.config.optimize_trajectories:
+            param_groups["trajectory_opt"] = param_groups.get("trajectory_opt", []) + list(self.parameters())
diff --git a/nerfstudio/model_components/losses.py b/nerfstudio/model_components/losses.py
index d4646698..4d986fdd 100644
--- a/nerfstudio/model_components/losses.py
+++ b/nerfstudio/model_components/losses.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,10 +16,12 @@
 """
 Collection of Losses.
 """
+
 from enum import Enum
 from typing import Dict, Literal, Optional, Tuple, cast
 
 import torch
+import torchvision
 from jaxtyping import Bool, Float
 from torch import Tensor, nn
 
@@ -252,6 +255,8 @@ def urban_radiance_field_depth_loss(
     predicted_depth: Float[Tensor, "*batch 1"],
     steps: Float[Tensor, "*batch num_samples 1"],
     sigma: Float[Tensor, "0"],
+    bin_sizes: Float[Tensor, "*batch_num_samples 1"],
+    scaling_factor: Float[Tensor, "0"],
 ) -> Float[Tensor, "*batch 1"]:
     """Lidar losses from Urban Radiance Fields (Rematas et al., 2022).
 
@@ -273,12 +278,14 @@ def urban_radiance_field_depth_loss(
     target_distribution = torch.distributions.normal.Normal(0.0, sigma / URF_SIGMA_SCALE_FACTOR)
     termination_depth = termination_depth[:, None]
     line_of_sight_loss_near_mask = torch.logical_and(
-        steps <= termination_depth + sigma, steps >= termination_depth - sigma
+        steps <= (termination_depth + sigma), steps >= (termination_depth - sigma)
     )
-    line_of_sight_loss_near = (weights - torch.exp(target_distribution.log_prob(steps - termination_depth))) ** 2
+    line_of_sight_loss_near = (
+        (weights / (bin_sizes) - torch.exp(target_distribution.log_prob(steps - termination_depth))) ** 2
+    ) * (bin_sizes)
     line_of_sight_loss_near = (line_of_sight_loss_near_mask * line_of_sight_loss_near).sum(-2)
-    line_of_sight_loss_empty_mask = steps < termination_depth - sigma
-    line_of_sight_loss_empty = (line_of_sight_loss_empty_mask * weights**2).sum(-2)
+    line_of_sight_loss_empty_mask = steps < (termination_depth - sigma)
+    line_of_sight_loss_empty = (line_of_sight_loss_empty_mask * weights**2 / bin_sizes).sum(-2)
     line_of_sight_loss = line_of_sight_loss_near + line_of_sight_loss_empty
 
     loss = (expected_depth_loss + line_of_sight_loss) * depth_mask
@@ -294,6 +301,7 @@ def depth_loss(
     directions_norm: Float[Tensor, "*batch 1"],
     is_euclidean: bool,
     depth_loss_type: DepthLossType,
+    scaling_factor: Float[Tensor, "0"] = 1.0,
 ) -> Float[Tensor, "0"]:
     """Implementation of depth losses.
 
@@ -319,7 +327,10 @@ def depth_loss(
         return ds_nerf_depth_loss(weights, termination_depth, steps, lengths, sigma)
 
     if depth_loss_type == DepthLossType.URF:
-        return urban_radiance_field_depth_loss(weights, termination_depth, predicted_depth, steps, sigma)
+        bin_sizes = ray_samples.frustums.ends - ray_samples.frustums.starts
+        return urban_radiance_field_depth_loss(
+            weights, termination_depth, predicted_depth, steps, sigma, bin_sizes, scaling_factor
+        )
 
     raise NotImplementedError("Provided depth loss type not implemented.")
 
@@ -568,6 +579,52 @@ def scale_gradients_by_distance_squared(
     return out
 
 
+class VGGPerceptualLossPix2Pix(nn.Module):
+    """From https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py"""
+
+    def __init__(self, weights=None):
+        super().__init__()
+        self.vgg = Vgg19().eval()
+        self.vgg.requires_grad_(False)
+        self.criterion = nn.L1Loss()
+        if weights is None:
+            self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0]
+        else:
+            assert len(weights) == 5, "Expected 5 weights for VGGPerceptualLossPix2Pix"
+            self.weights = weights
+
+    def forward(self, x, y):
+        # Assume input is in NHWC format
+        x, y = x.permute(0, 3, 1, 2), y.permute(0, 3, 1, 2)
+        vgg_out = self.vgg(torch.cat([x, y], dim=0))
+        # x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+        loss = 0
+        for i in range(len(vgg_out)):
+            x_vgg, y_vgg = vgg_out[i].chunk(2, dim=0)
+            loss += self.weights[i] * self.criterion(x_vgg, y_vgg.detach())
+        return loss
+
+
+class Vgg19(torch.nn.Module):
+    def __init__(self):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential(vgg_pretrained_features[:2])
+        self.slice2 = torch.nn.Sequential(vgg_pretrained_features[2:7])
+        self.slice3 = torch.nn.Sequential(vgg_pretrained_features[7:12])
+        self.slice4 = torch.nn.Sequential(vgg_pretrained_features[12:21])
+        self.slice5 = torch.nn.Sequential(vgg_pretrained_features[21:30])
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
 def depth_ranking_loss(rendered_depth, gt_depth):
     """
     Depth ranking loss as described in the SparseNeRF paper
@@ -583,3 +640,66 @@ def depth_ranking_loss(rendered_depth, gt_depth):
     out_diff = rendered_depth[::2, :] - rendered_depth[1::2, :] + m
     differing_signs = torch.sign(dpt_diff) != torch.sign(out_diff)
     return torch.nanmean((out_diff[differing_signs] * torch.sign(out_diff[differing_signs])))
+
+
+def _blur_stepfun(x: torch.Tensor, y: torch.Tensor, r: float) -> Tuple[torch.Tensor, torch.Tensor]:
+    xr, xr_idx = torch.sort(torch.cat([x - r, x + r], dim=-1))
+    y1 = (
+        torch.cat([y, torch.zeros_like(y[..., :1])], dim=-1) - torch.cat([torch.zeros_like(y[..., :1]), y], dim=-1)
+    ) / (2 * r)
+    y2 = torch.cat([y1, -y1], dim=-1).take_along_dim(xr_idx[..., :-1], dim=-1)
+    yr = torch.cumsum((xr[..., 1:] - xr[..., :-1]) * torch.cumsum(y2, dim=-1), dim=-1).clamp_min(0)
+    yr = torch.cat([torch.zeros_like(yr[..., :1]), yr], dim=-1)
+    return xr, yr
+
+
+def _sorted_interp_quad(x, xp, fpdf, fcdf):
+    right_idx = torch.searchsorted(xp, x)
+    left_idx = (right_idx - 1).clamp_min(0)
+    right_idx = right_idx.clamp_max(xp.shape[-1] - 1)
+
+    xp0 = xp.take_along_dim(left_idx, dim=-1)
+    xp1 = xp.take_along_dim(right_idx, dim=-1)
+    fpdf0 = fpdf.take_along_dim(left_idx, dim=-1)
+    fpdf1 = fpdf.take_along_dim(right_idx, dim=-1)
+    fcdf0 = fcdf.take_along_dim(left_idx, dim=-1)
+
+    offset = torch.clip(torch.nan_to_num((x - xp0) / (xp1 - xp0), 0), 0, 1)
+    return fcdf0 + (x - xp0) * (fpdf0 + fpdf1 * offset + fpdf0 * (1 - offset)) * 0.5
+
+
+def zipnerf_interlevel_loss(weights_list, ray_samples_list):
+    """Anti-aliased interlevel loss proposed in ZipNeRF paper."""
+    # ground truth s and w (real nerf samples)
+    # This implementation matches ZipNeRF up to the scale.
+    # In the paper the loss is computed as the sum over the ray samples.
+    # Here we take the mean and the multiplier for this loss should be changed accordingly.
+    pulse_widths = [0.03, 0.003]
+    c = ray_samples_to_sdist(ray_samples_list[-1]).detach()
+    w = weights_list[-1][..., 0].detach()
+    accum_w = torch.sum(w, dim=-1, keepdim=True)
+    w = torch.cat([w[..., :-1], w[..., -1:] + (1 - accum_w)], dim=-1)
+
+    w_norm = w / (c[..., 1:] - c[..., :-1])
+    loss = 0
+    for i, (ray_samples, weights) in enumerate(zip(ray_samples_list[:-1], weights_list[:-1])):
+        cp = ray_samples_to_sdist(ray_samples)
+        wp = weights[..., 0]  # (num_rays, num_samples)
+        c_, w_ = _blur_stepfun(c, w_norm, pulse_widths[i])
+
+        # piecewise linear pdf to piecewise quadratic cdf
+        area = 0.5 * (w_[..., 1:] + w_[..., :-1]) * (c_[..., 1:] - c_[..., :-1])
+        cdf = torch.cat([torch.zeros_like(area[..., :1]), torch.cumsum(area, dim=-1)], dim=-1)
+
+        # prepend 0 weight and append 1 weight
+        c_ = torch.cat([torch.zeros_like(c_[..., :1]), c_, torch.ones_like(c_[..., :1])], dim=-1)
+        w_ = torch.cat([torch.zeros_like(w_[..., :1]), w_, torch.zeros_like(w_[..., :1])], dim=-1)
+        cdf = torch.cat([torch.zeros_like(cdf[..., :1]), cdf, torch.ones_like(cdf[..., :1])], dim=-1)
+
+        # query piecewise quadratic interpolation
+        cdf_interp = _sorted_interp_quad(cp, c_, w_, cdf)
+
+        # difference between adjacent interpolated values
+        w_s = torch.diff(cdf_interp, dim=-1)
+        loss += ((w_s - wp).clamp_min(0) ** 2 / (wp + 1e-5)).sum(dim=-1).mean()
+    return loss
diff --git a/nerfstudio/model_components/ray_generators.py b/nerfstudio/model_components/ray_generators.py
index fab9e39b..f2cda976 100644
--- a/nerfstudio/model_components/ray_generators.py
+++ b/nerfstudio/model_components/ray_generators.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,10 +16,11 @@
 """
 Ray generator.
 """
-from jaxtyping import Int
+from jaxtyping import Float, Int
 from torch import Tensor, nn
 
 from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.cameras.lidars import Lidars
 from nerfstudio.cameras.rays import RayBundle
 
 
@@ -53,3 +55,39 @@ def forward(self, ray_indices: Int[Tensor, "num_rays 3"]) -> RayBundle:
             coords=coords,
         )
         return ray_bundle
+
+
+class LidarRayGenerator(nn.Module):
+    """torch.nn Module for generating rays for Lidars.
+    This class is the interface between the scene's lidars/lidar optimizer and the ray sampler.
+
+    Args:
+        lidars: Lidar objects containing lidar info.
+        pose_optimizer: pose optimization module, for optimizing noisy lidar intrinsics/extrinsics.
+    """
+
+    def __init__(self, lidars: Lidars) -> None:
+        super().__init__()
+        self.lidars = lidars
+
+    def forward(self, ray_indices: Int[Tensor, "num_rays 2"], points: Float[Tensor, "num_rays 3"] = None) -> RayBundle:
+        """Index into the lidars to generate the rays.
+
+        Args:
+            ray_indices: Contains (lidar, point) indices for target rays.
+        """
+        c = ray_indices[:, 0]  # lidar (scan) indices
+        n = ray_indices[:, 1]  # point indices
+
+        if points is not None:
+            ray_bundle = self.lidars.generate_rays(
+                lidar_indices=c.unsqueeze(-1),
+                points=points,
+            )
+        else:
+            ray_bundle = self.lidars.generate_rays(
+                lidar_indices=c.unsqueeze(-1),
+                coords=n,
+            )
+
+        return ray_bundle
diff --git a/nerfstudio/model_components/ray_samplers.py b/nerfstudio/model_components/ray_samplers.py
index 7a2052b6..3ddb31b3 100644
--- a/nerfstudio/model_components/ray_samplers.py
+++ b/nerfstudio/model_components/ray_samplers.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,6 +26,7 @@
 from torch import Tensor, nn
 
 from nerfstudio.cameras.rays import Frustums, RayBundle, RaySamples
+from nerfstudio.utils.math import inv_power_fn, power_fn
 
 
 class Sampler(nn.Module):
@@ -97,7 +99,9 @@ def generate_ray_samples(
         assert num_samples is not None
         num_rays = ray_bundle.origins.shape[0]
 
-        bins = torch.linspace(0.0, 1.0, num_samples + 1).to(ray_bundle.origins.device)[None, ...]  # [1, num_samples+1]
+        bins = torch.linspace(0.0, 1.0, num_samples + 1, device=ray_bundle.origins.device)[
+            None, ...
+        ]  # [1, num_samples+1]
 
         # TODO More complicated than it needs to be.
         if self.train_stratified and self.training:
@@ -317,7 +321,7 @@ def generate_ray_samples(
         if self.train_stratified and self.training:
             # Stratified samples between 0 and 1
             u = torch.linspace(0.0, 1.0 - (1.0 / num_bins), steps=num_bins, device=cdf.device)
-            u = u.expand(size=(*cdf.shape[:-1], num_bins))
+            u = u.expand(size=(*cdf.shape[:-1], num_bins)).clone()  # TODO(atonderski) why clone?
             if self.single_jitter:
                 rand = torch.rand((*cdf.shape[:-1], 1), device=cdf.device) / num_bins
             else:
@@ -327,7 +331,7 @@ def generate_ray_samples(
             # Uniform samples between 0 and 1
             u = torch.linspace(0.0, 1.0 - (1.0 / num_bins), steps=num_bins, device=cdf.device)
             u = u + 1.0 / (2 * num_bins)
-            u = u.expand(size=(*cdf.shape[:-1], num_bins))
+            u = u.expand(size=(*cdf.shape[:-1], num_bins)).clone()  # TODO(atonderski) why clone?
         u = u.contiguous()
 
         assert (
@@ -383,6 +387,17 @@ def __call__(
         ...
 
 
+class AlphaFn(Protocol):
+    """
+    Function that evaluates alpha at a given point.
+    """
+
+    def __call__(
+        self, positions: Float[Tensor, "*batch 3"], times: Optional[Float[Tensor, "*batch 1"]] = None
+    ) -> Float[Tensor, "*batch 1"]:
+        ...
+
+
 class VolumetricSampler(Sampler):
     """Sampler inspired by the one proposed in the Instant-NGP paper.
     Generates samples along a ray by sampling the occupancy field.
@@ -398,12 +413,16 @@ def __init__(
         self,
         occupancy_grid: OccGridEstimator,
         density_fn: Optional[DensityFn] = None,
+        alpha_fn: Optional[AlphaFn] = None,
     ):
         super().__init__()
         assert occupancy_grid is not None
         self.density_fn = density_fn
+        self.alpha_fn = alpha_fn
         self.occupancy_grid = occupancy_grid
 
+        assert not (alpha_fn is not None and density_fn is not None), "density_fn and alpha_fn cannot be both set"
+
     def get_sigma_fn(self, origins, directions, times=None) -> Optional[Callable]:
         """Returns a function that returns the density of a point.
 
@@ -430,6 +449,32 @@ def sigma_fn(t_starts, t_ends, ray_indices):
 
         return sigma_fn
 
+    def get_alpha_fn(self, origins, directions, times=None) -> Optional[Callable]:
+        """Returns a function that returns the density of a point.
+
+        Args:
+            origins: Origins of rays
+            directions: Directions of rays
+            times: Times at which rays are sampled
+        Returns:
+            Function that returns the density of a point or None if a density function is not provided.
+        """
+
+        if self.alpha_fn is None or not self.training:
+            return None
+
+        alpha_fn = self.alpha_fn
+
+        def alpha_fn_out(t_starts, t_ends, ray_indices):
+            t_origins = origins[ray_indices]
+            t_dirs = directions[ray_indices]
+            positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
+            if times is None:
+                return alpha_fn(positions).squeeze(-1)
+            return alpha_fn(positions, times[ray_indices]).squeeze(-1)
+
+        return alpha_fn_out
+
     def generate_ray_samples(self) -> RaySamples:
         raise RuntimeError(
             "The VolumetricSampler fuses sample generation and density check together. Please call forward() directly."
@@ -485,6 +530,7 @@ def forward(
             t_min=t_min,
             t_max=t_max,
             sigma_fn=self.get_sigma_fn(rays_o, rays_d, times),
+            alpha_fn=self.get_alpha_fn(rays_o, rays_d, times),
             render_step_size=render_step_size,
             near_plane=near_plane,
             far_plane=far_plane,
@@ -511,7 +557,7 @@ def forward(
                 directions=dirs,
                 starts=starts[..., None],
                 ends=ends[..., None],
-                pixel_area=ray_bundle[ray_indices].pixel_area,
+                pixel_area=ray_bundle.pixel_area[ray_indices],
             ),
             camera_indices=camera_indices,
         )
@@ -578,10 +624,12 @@ def generate_ray_samples(
         self,
         ray_bundle: Optional[RayBundle] = None,
         density_fns: Optional[List[Callable]] = None,
+        pass_ray_samples: bool = False,
     ) -> Tuple[RaySamples, List, List]:
         assert ray_bundle is not None
         assert density_fns is not None
-
+        if not pass_ray_samples:
+            density_fns = [lambda rs: f(rs.frustums.get_positions()) for f in density_fns]
         weights_list = []
         ray_samples_list = []
 
@@ -604,10 +652,10 @@ def generate_ray_samples(
             if is_prop:
                 if updated:
                     # always update on the first step or the inf check in grad scaling crashes
-                    density = density_fns[i_level](ray_samples.frustums.get_positions())
+                    density = density_fns[i_level](ray_samples)
                 else:
                     with torch.no_grad():
-                        density = density_fns[i_level](ray_samples.frustums.get_positions())
+                        density = density_fns[i_level](ray_samples)
                 weights = ray_samples.get_weights(density)
                 weights_list.append(weights)  # (num_rays, num_samples)
                 ray_samples_list.append(ray_samples)
@@ -785,3 +833,20 @@ def merge_ray_samples(ray_bundle: RayBundle, ray_samples_1: RaySamples, ray_samp
         )
 
         return ray_samples, sorted_index
+
+
+class PowerSampler(SpacedSampler):
+    """Sampler according to the ZipNeRF's power function
+    Args:
+        num_samples: Number of samples per ray
+        train_stratified: Use stratified sampling during training. Defaults to True
+        lambda_: Parameter of power transformation
+    """
+
+    def __init__(self, num_samples: Optional[int] = None, lambda_=-1.5, scaling=2.0, **kwargs) -> None:
+        super().__init__(
+            num_samples=num_samples,
+            spacing_fn=lambda x: power_fn(x * scaling, lambda_),
+            spacing_fn_inv=lambda x: inv_power_fn(x, lambda_) / scaling,
+            **kwargs,
+        )
diff --git a/nerfstudio/model_components/renderers.py b/nerfstudio/model_components/renderers.py
index 1fde0d69..181f8d02 100644
--- a/nerfstudio/model_components/renderers.py
+++ b/nerfstudio/model_components/renderers.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -55,6 +56,40 @@ def background_color_override_context(mode: Float[Tensor, "3"]) -> Generator[Non
         BACKGROUND_COLOR_OVERRIDE = old_background_color
 
 
+class FeatureRenderer(nn.Module):
+    """Standard volumetric rendering for arbitrary features."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(
+        self,
+        features: Float[Tensor, "*bs num_samples num_features"],
+        weights: Float[Tensor, "*bs num_samples 1"],
+        ray_indices: Optional[Int[Tensor, "num_samples"]] = None,
+        num_rays: Optional[int] = None,
+    ) -> Float[Tensor, "*bs num_features"]:
+        """Composite samples along ray and render color image
+
+        Args:
+            features: Features for each sample
+            weights: Weights for each sample
+            ray_indices: Ray index for each sample, used when samples are packed.
+            num_rays: Number of rays, used when samples are packed.
+
+        Returns:
+            Outputs
+        """
+        if ray_indices is None or num_rays is None:
+            # Unpacked
+            return torch.sum(features * weights, dim=-2)
+        else:
+            # Packed
+            return nerfacc.accumulate_along_rays(
+                weights[..., 0], values=features, ray_indices=ray_indices, n_rays=num_rays
+            )
+
+
 class RGBRenderer(nn.Module):
     """Standard volumetric rendering.
 
@@ -433,6 +468,8 @@ def forward(
         normals: Float[Tensor, "*bs num_samples 3"],
         weights: Float[Tensor, "*bs num_samples 1"],
         normalize: bool = True,
+        ray_indices: Optional[Int[Tensor, "num_samples"]] = None,
+        num_rays: Optional[int] = None,
     ) -> Float[Tensor, "*bs 3"]:
         """Calculate normals along the ray.
 
@@ -440,8 +477,13 @@ def forward(
             normals: Normals for each sample.
             weights: Weights of each sample.
             normalize: Normalize normals.
+            ray_indices: Ray index for each sample, used when samples are packed.
+            num_rays: Number of rays, used when samples are packed.
         """
         n = torch.sum(weights * normals, dim=-2)
+        if ray_indices is not None and num_rays is not None:
+            # Necessary for packed samples from volumetric ray sampler
+            n = nerfacc.accumulate_along_rays(weights[..., 0], values=normals, ray_indices=ray_indices, n_rays=num_rays)
         if normalize:
             n = safe_normalize(n)
         return n
diff --git a/nerfstudio/model_components/utils.py b/nerfstudio/model_components/utils.py
new file mode 100644
index 00000000..a8c3b3fa
--- /dev/null
+++ b/nerfstudio/model_components/utils.py
@@ -0,0 +1,41 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Union
+
+import torch
+from torch import Tensor, nn
+
+
+class SigmoidDensity(nn.Module):
+    """Learnable sigmoid density"""
+
+    def __init__(self, init_val, beta_min=0.0001, learnable_beta=False):
+        super().__init__()
+        self.register_buffer("beta_min", torch.tensor(beta_min))
+        self.register_parameter("beta", nn.Parameter(init_val * torch.ones(1), requires_grad=learnable_beta))
+
+    def forward(self, sdf: Tensor, beta: Union[Tensor, None] = None) -> Tensor:
+        """convert sdf value to density value with beta, if beta is missing, then use learable beta"""
+
+        if beta is None:
+            beta = self.get_beta()
+
+        # negtive sdf will have large density
+        return torch.sigmoid(-sdf * beta)
+
+    def get_beta(self):
+        """return current beta value"""
+        beta = self.beta.abs() + self.beta_min
+        return beta
diff --git a/nerfstudio/models/ad_model.py b/nerfstudio/models/ad_model.py
new file mode 100644
index 00000000..c8de17bb
--- /dev/null
+++ b/nerfstudio/models/ad_model.py
@@ -0,0 +1,121 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Type, Union
+
+import torch
+
+from nerfstudio.cameras.camera_optimizers import CameraOptimizer, CameraOptimizerConfig
+from nerfstudio.cameras.lidars import Lidars
+from nerfstudio.cameras.rays import RayBundle
+from nerfstudio.model_components.dynamic_actors import DynamicActors, DynamicActorsConfig
+from nerfstudio.models.base_model import Model, ModelConfig
+from nerfstudio.utils.poses import inverse as pose_inverse
+
+
+@dataclass
+class ADModelConfig(ModelConfig):
+    """Base model config for all AD models."""
+
+    _target: Type = field(default_factory=lambda: ADModel)
+
+    rgb_upsample_factor: int = 1
+    """Upsample factor for RGB images. For vanilla NeRFs this is 1 (1 pixel per ray)."""
+
+    dynamic_actors: DynamicActorsConfig = field(default_factory=DynamicActorsConfig)
+    """Dynamic actors configuration."""
+
+    camera_optimizer: CameraOptimizerConfig = field(
+        default_factory=lambda: CameraOptimizerConfig(mode="off", trans_l2_penalty=0.1)
+    )
+    """Config of the camera optimizer to use"""
+
+
+class ADModel(Model):
+    """Base model for all AD models."""
+
+    config: ADModelConfig
+
+    def forward(
+        self, ray_bundle: RayBundle, patch_size: Optional[Tuple[int, int]] = None
+    ) -> Dict[str, Union[torch.Tensor, List]]:
+        """Forward pass of the model."""
+        outputs = super().forward(ray_bundle)
+        if patch_size is not None and "rgb" in outputs:
+            if isinstance(outputs["rgb"], torch.Tensor):
+                # if patch size is given, we must reshape the output to respect it
+                outputs["rgb"] = outputs["rgb"].reshape(-1, *patch_size, 3)
+        return outputs
+
+    def populate_modules(self):
+        """Set the fields and modules."""
+        super().populate_modules()
+        trajectories = self.kwargs["metadata"].get("trajectories")
+        self.dynamic_actors: DynamicActors = self.config.dynamic_actors.setup(trajectories=trajectories)
+        self.camera_optimizer: CameraOptimizer = self.config.camera_optimizer.setup(
+            num_cameras=self.num_train_data, device="cpu"
+        )
+
+    def get_param_groups(self) -> Dict[str, List[torch.nn.Parameter]]:
+        """Get the parameter groups for the optimizer."""
+        param_groups = defaultdict(list)
+        self.camera_optimizer.get_param_groups(param_groups)
+        self.dynamic_actors.get_param_groups(param_groups)
+        return param_groups
+
+    def disable_ray_drop(self):
+        """Disables ray drop for the model."""
+        pass
+
+    @torch.no_grad()
+    def get_outputs_for_lidar(
+        self, lidar: Lidars, batch: Dict[str, torch.Tensor]
+    ) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
+        """Takes in a lidar, generates the raybundle, and computes the output of the model.
+        Assumes a ray-based model.
+
+        Args:
+            camera: generates raybundle
+        """
+        points = batch["lidar"]
+        lidar_indices = torch.zeros_like(points[:, 0:1]).long()
+        ray_bundle = lidar.generate_rays(lidar_indices=lidar_indices, points=points, keep_shape=True)
+        # TODO: Can we avoid needing to pass this from the raybundle to the batch?
+        batch["is_lidar"] = ray_bundle.metadata["is_lidar"]
+        batch["distance"] = ray_bundle.metadata["directions_norm"]
+        batch["did_return"] = ray_bundle.metadata["did_return"]
+
+        outputs = self.get_outputs_for_camera_ray_bundle(ray_bundle)
+
+        # add points in local coords to model outputs
+        l2w = lidar.lidar_to_worlds[0].to(self.device)
+        w2l = pose_inverse(l2w)
+        points = ray_bundle.origins + ray_bundle.directions * outputs["depth"]
+        outputs["points"] = (w2l @ torch.cat([points, torch.ones_like(points[..., :1])], dim=-1).unsqueeze(-1)).squeeze(
+            -1
+        )
+        return outputs, batch
+
+    @torch.no_grad()
+    def get_outputs_for_camera_ray_bundle(self, camera_ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:
+        """Takes in camera parameters and computes the output of the model.
+
+        Args:
+            camera_ray_bundle: ray bundle to calculate outputs over
+        """
+        if len(camera_ray_bundle.shape) == 1:  # lidar
+            return self.forward(ray_bundle=camera_ray_bundle)  # type: ignore
+        else:  # camera
+            return super().get_outputs_for_camera_ray_bundle(camera_ray_bundle)
diff --git a/nerfstudio/models/base_surface_model.py b/nerfstudio/models/base_surface_model.py
deleted file mode 100644
index 99296832..00000000
--- a/nerfstudio/models/base_surface_model.py
+++ /dev/null
@@ -1,405 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Implementation of Base surface model.
-"""
-
-from __future__ import annotations
-
-from abc import abstractmethod
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Literal, Tuple, Type, cast
-
-import torch
-import torch.nn.functional as F
-from torch.nn import Parameter
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.field_components.encodings import NeRFEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.field_components.spatial_distortions import SceneContraction
-from nerfstudio.fields.nerfacto_field import NerfactoField
-from nerfstudio.fields.sdf_field import SDFFieldConfig
-from nerfstudio.fields.vanilla_nerf_field import NeRFField
-from nerfstudio.model_components.losses import L1Loss, MSELoss, ScaleAndShiftInvariantLoss, monosdf_normal_loss
-from nerfstudio.model_components.ray_samplers import LinearDisparitySampler
-from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer, SemanticRenderer
-from nerfstudio.model_components.scene_colliders import AABBBoxCollider, NearFarCollider
-from nerfstudio.models.base_model import Model, ModelConfig
-from nerfstudio.utils import colormaps
-from nerfstudio.utils.colors import get_color
-from nerfstudio.utils.math import normalized_depth_scale_and_shift
-
-
-@dataclass
-class SurfaceModelConfig(ModelConfig):
-    """Surface Model Config"""
-
-    _target: Type = field(default_factory=lambda: SurfaceModel)
-    near_plane: float = 0.05
-    """How far along the ray to start sampling."""
-    far_plane: float = 4.0
-    """How far along the ray to stop sampling."""
-    far_plane_bg: float = 1000.0
-    """How far along the ray to stop sampling of the background model."""
-    background_color: Literal["random", "last_sample", "white", "black"] = "black"
-    """Whether to randomize the background color."""
-    use_average_appearance_embedding: bool = False
-    """Whether to use average appearance embedding or zeros for inference."""
-    eikonal_loss_mult: float = 0.1
-    """Monocular normal consistency loss multiplier."""
-    fg_mask_loss_mult: float = 0.01
-    """Foreground mask loss multiplier."""
-    mono_normal_loss_mult: float = 0.0
-    """Monocular normal consistency loss multiplier."""
-    mono_depth_loss_mult: float = 0.0
-    """Monocular depth consistency loss multiplier."""
-    sdf_field: SDFFieldConfig = field(default_factory=SDFFieldConfig)
-    """Config for SDF Field"""
-    background_model: Literal["grid", "mlp", "none"] = "mlp"
-    """background models"""
-    num_samples_outside: int = 32
-    """Number of samples outside the bounding sphere for background"""
-    periodic_tvl_mult: float = 0.0
-    """Total variational loss multiplier"""
-    overwrite_near_far_plane: bool = False
-    """whether to use near and far collider from command line"""
-
-
-class SurfaceModel(Model):
-    """Base surface model
-
-    Args:
-        config: Base surface model configuration to instantiate model
-    """
-
-    config: SurfaceModelConfig
-
-    def populate_modules(self):
-        """Set the fields and modules."""
-        super().populate_modules()
-
-        self.scene_contraction = SceneContraction(order=float("inf"))
-
-        # Can we also use contraction for sdf?
-        # Fields
-        self.field = self.config.sdf_field.setup(
-            aabb=self.scene_box.aabb,
-            spatial_distortion=self.scene_contraction,
-            num_images=self.num_train_data,
-            use_average_appearance_embedding=self.config.use_average_appearance_embedding,
-        )
-
-        # Collider
-        self.collider = AABBBoxCollider(self.scene_box, near_plane=0.05)
-
-        # command line near and far has highest priority
-        if self.config.overwrite_near_far_plane:
-            self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
-
-        # background model
-        if self.config.background_model == "grid":
-            self.field_background = NerfactoField(
-                self.scene_box.aabb,
-                spatial_distortion=self.scene_contraction,
-                num_images=self.num_train_data,
-                use_average_appearance_embedding=self.config.use_average_appearance_embedding,
-            )
-        elif self.config.background_model == "mlp":
-            position_encoding = NeRFEncoding(
-                in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=9.0, include_input=True
-            )
-            direction_encoding = NeRFEncoding(
-                in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=3.0, include_input=True
-            )
-
-            self.field_background = NeRFField(
-                position_encoding=position_encoding,
-                direction_encoding=direction_encoding,
-                spatial_distortion=self.scene_contraction,
-            )
-        else:
-            # dummy background model
-            self.field_background = Parameter(torch.ones(1), requires_grad=False)
-
-        self.sampler_bg = LinearDisparitySampler(num_samples=self.config.num_samples_outside)
-
-        # renderers
-        background_color = (
-            get_color(self.config.background_color)
-            if self.config.background_color in set(["white", "black"])
-            else self.config.background_color
-        )
-        self.renderer_rgb = RGBRenderer(background_color=background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer(method="expected")
-        self.renderer_normal = SemanticRenderer()
-
-        # losses
-        self.rgb_loss = L1Loss()
-        self.eikonal_loss = MSELoss()
-        self.depth_loss = ScaleAndShiftInvariantLoss(alpha=0.5, scales=1)
-
-        # metrics
-        from torchmetrics.functional import structural_similarity_index_measure
-        from torchmetrics.image import PeakSignalNoiseRatio
-        from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-
-        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
-        self.ssim = structural_similarity_index_measure
-        self.lpips = LearnedPerceptualImagePatchSimilarity()
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-        param_groups["fields"] = list(self.field.parameters())
-        param_groups["field_background"] = (
-            [self.field_background]
-            if isinstance(self.field_background, Parameter)
-            else list(self.field_background.parameters())
-        )
-        return param_groups
-
-    @abstractmethod
-    def sample_and_forward_field(self, ray_bundle: RayBundle) -> Dict[str, Any]:
-        """Takes in a Ray Bundle and returns a dictionary of samples and field output.
-
-        Args:
-            ray_bundle: Input bundle of rays. This raybundle should have all the
-            needed information to compute the outputs.
-
-        Returns:
-            Outputs of model. (ie. rendered colors)
-        """
-
-    def get_outputs(self, ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:
-        """Takes in a Ray Bundle and returns a dictionary of outputs.
-
-        Args:
-            ray_bundle: Input bundle of rays. This raybundle should have all the
-            needed information to compute the outputs.
-
-        Returns:
-            Outputs of model. (ie. rendered colors)
-        """
-        assert (
-            ray_bundle.metadata is not None and "directions_norm" in ray_bundle.metadata
-        ), "directions_norm is required in ray_bundle.metadata"
-
-        samples_and_field_outputs = self.sample_and_forward_field(ray_bundle=ray_bundle)
-
-        # shortcuts
-        field_outputs: Dict[FieldHeadNames, torch.Tensor] = cast(
-            Dict[FieldHeadNames, torch.Tensor], samples_and_field_outputs["field_outputs"]
-        )
-        ray_samples = samples_and_field_outputs["ray_samples"]
-        weights = samples_and_field_outputs["weights"]
-        bg_transmittance = samples_and_field_outputs["bg_transmittance"]
-
-        rgb = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.RGB], weights=weights)
-        depth = self.renderer_depth(weights=weights, ray_samples=ray_samples)
-        # the rendered depth is point-to-point distance and we should convert to depth
-        depth = depth / ray_bundle.metadata["directions_norm"]
-
-        normal = self.renderer_normal(semantics=field_outputs[FieldHeadNames.NORMALS], weights=weights)
-        accumulation = self.renderer_accumulation(weights=weights)
-
-        # background model
-        if self.config.background_model != "none":
-            assert isinstance(self.field_background, torch.nn.Module), "field_background should be a module"
-            assert ray_bundle.fars is not None, "fars is required in ray_bundle"
-            # sample inversely from far to 1000 and points and forward the bg model
-            ray_bundle.nears = ray_bundle.fars
-            assert ray_bundle.fars is not None
-            ray_bundle.fars = torch.ones_like(ray_bundle.fars) * self.config.far_plane_bg
-
-            ray_samples_bg = self.sampler_bg(ray_bundle)
-            # use the same background model for both density field and occupancy field
-            assert not isinstance(self.field_background, Parameter)
-            field_outputs_bg = self.field_background(ray_samples_bg)
-            weights_bg = ray_samples_bg.get_weights(field_outputs_bg[FieldHeadNames.DENSITY])
-
-            rgb_bg = self.renderer_rgb(rgb=field_outputs_bg[FieldHeadNames.RGB], weights=weights_bg)
-            depth_bg = self.renderer_depth(weights=weights_bg, ray_samples=ray_samples_bg)
-            accumulation_bg = self.renderer_accumulation(weights=weights_bg)
-
-            # merge background color to foregound color
-            rgb = rgb + bg_transmittance * rgb_bg
-
-            bg_outputs = {
-                "bg_rgb": rgb_bg,
-                "bg_accumulation": accumulation_bg,
-                "bg_depth": depth_bg,
-                "bg_weights": weights_bg,
-            }
-        else:
-            bg_outputs = {}
-
-        outputs = {
-            "rgb": rgb,
-            "accumulation": accumulation,
-            "depth": depth,
-            "normal": normal,
-            "weights": weights,
-            # used to scale z_vals for free space and sdf loss
-            "directions_norm": ray_bundle.metadata["directions_norm"],
-        }
-        outputs.update(bg_outputs)
-
-        if self.training:
-            grad_points = field_outputs[FieldHeadNames.GRADIENT]
-            outputs.update({"eik_grad": grad_points})
-            outputs.update(samples_and_field_outputs)
-
-        if "weights_list" in samples_and_field_outputs:
-            weights_list = cast(List[torch.Tensor], samples_and_field_outputs["weights_list"])
-            ray_samples_list = cast(List[torch.Tensor], samples_and_field_outputs["ray_samples_list"])
-
-            for i in range(len(weights_list) - 1):
-                outputs[f"prop_depth_{i}"] = self.renderer_depth(
-                    weights=weights_list[i], ray_samples=ray_samples_list[i]
-                )
-        # this is used only in viewer
-        outputs["normal_vis"] = (outputs["normal"] + 1.0) / 2.0
-        return outputs
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Tensor]:
-        """Computes and returns the losses dict.
-
-        Args:
-            outputs: the output to compute loss dict to
-            batch: ground truth batch corresponding to outputs
-            metrics_dict: dictionary of metrics, some of which we can use for loss
-        """
-        loss_dict = {}
-        image = batch["image"].to(self.device)
-        pred_image, image = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb"],
-            pred_accumulation=outputs["accumulation"],
-            gt_image=image,
-        )
-        loss_dict["rgb_loss"] = self.rgb_loss(image, pred_image)
-        if self.training:
-            # eikonal loss
-            grad_theta = outputs["eik_grad"]
-            loss_dict["eikonal_loss"] = ((grad_theta.norm(2, dim=-1) - 1) ** 2).mean() * self.config.eikonal_loss_mult
-
-            # foreground mask loss
-            if "fg_mask" in batch and self.config.fg_mask_loss_mult > 0.0:
-                fg_label = batch["fg_mask"].float().to(self.device)
-                weights_sum = outputs["weights"].sum(dim=1).clip(1e-3, 1.0 - 1e-3)
-                loss_dict["fg_mask_loss"] = (
-                    F.binary_cross_entropy(weights_sum, fg_label) * self.config.fg_mask_loss_mult
-                )
-
-            # monocular normal loss
-            if "normal" in batch and self.config.mono_normal_loss_mult > 0.0:
-                normal_gt = batch["normal"].to(self.device)
-                normal_pred = outputs["normal"]
-                loss_dict["normal_loss"] = (
-                    monosdf_normal_loss(normal_pred, normal_gt) * self.config.mono_normal_loss_mult
-                )
-
-            # monocular depth loss
-            if "depth" in batch and self.config.mono_depth_loss_mult > 0.0:
-                depth_gt = batch["depth"].to(self.device)[..., None]
-                depth_pred = outputs["depth"]
-
-                mask = torch.ones_like(depth_gt).reshape(1, 32, -1).bool()
-                loss_dict["depth_loss"] = (
-                    self.depth_loss(depth_pred.reshape(1, 32, -1), (depth_gt * 50 + 0.5).reshape(1, 32, -1), mask)
-                    * self.config.mono_depth_loss_mult
-                )
-
-        return loss_dict
-
-    def get_metrics_dict(self, outputs, batch) -> Dict[str, torch.Tensor]:
-        """Compute and returns metrics.
-
-        Args:
-            outputs: the output to compute loss dict to
-            batch: ground truth batch corresponding to outputs
-        """
-        metrics_dict = {}
-        image = batch["image"].to(self.device)
-        image = self.renderer_rgb.blend_background(image)
-        metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
-        return metrics_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        """Writes the test image outputs.
-        Args:
-            outputs: Outputs of the model.
-            batch: Batch of data.
-
-        Returns:
-            A dictionary of metrics.
-        """
-        image = batch["image"].to(self.device)
-        image = self.renderer_rgb.blend_background(image)
-        rgb = outputs["rgb"]
-        acc = colormaps.apply_colormap(outputs["accumulation"])
-
-        normal = outputs["normal"]
-        normal = (normal + 1.0) / 2.0
-
-        combined_rgb = torch.cat([image, rgb], dim=1)
-        combined_acc = torch.cat([acc], dim=1)
-        if "depth" in batch:
-            depth_gt = batch["depth"].to(self.device)
-            depth_pred = outputs["depth"]
-
-            # align to predicted depth and normalize
-            scale, shift = normalized_depth_scale_and_shift(
-                depth_pred[None, ..., 0], depth_gt[None, ...], depth_gt[None, ...] > 0.0
-            )
-            depth_pred = depth_pred * scale + shift
-
-            combined_depth = torch.cat([depth_gt[..., None], depth_pred], dim=1)
-            combined_depth = colormaps.apply_depth_colormap(combined_depth)
-        else:
-            depth = colormaps.apply_depth_colormap(
-                outputs["depth"],
-                accumulation=outputs["accumulation"],
-            )
-            combined_depth = torch.cat([depth], dim=1)
-
-        if "normal" in batch:
-            normal_gt = (batch["normal"].to(self.device) + 1.0) / 2.0
-            combined_normal = torch.cat([normal_gt, normal], dim=1)
-        else:
-            combined_normal = torch.cat([normal], dim=1)
-
-        images_dict = {
-            "img": combined_rgb,
-            "accumulation": combined_acc,
-            "depth": combined_depth,
-            "normal": combined_normal,
-        }
-
-        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
-        image = torch.moveaxis(image, -1, 0)[None, ...]
-        rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
-
-        psnr = self.psnr(image, rgb)
-        ssim = self.ssim(image, rgb)
-        lpips = self.lpips(image, rgb)
-
-        # all of these metrics will be logged as scalars
-        metrics_dict = {"psnr": float(psnr.item()), "ssim": float(ssim)}  # type: ignore
-        metrics_dict["lpips"] = float(lpips)
-
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/depth_nerfacto.py b/nerfstudio/models/depth_nerfacto.py
deleted file mode 100644
index 1a47ab16..00000000
--- a/nerfstudio/models/depth_nerfacto.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Nerfacto augmented with depth supervision.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, Tuple, Type
-
-import numpy as np
-import torch
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.model_components import losses
-from nerfstudio.model_components.losses import DepthLossType, depth_loss, depth_ranking_loss
-from nerfstudio.models.nerfacto import NerfactoModel, NerfactoModelConfig
-from nerfstudio.utils import colormaps
-
-
-@dataclass
-class DepthNerfactoModelConfig(NerfactoModelConfig):
-    """Additional parameters for depth supervision."""
-
-    _target: Type = field(default_factory=lambda: DepthNerfactoModel)
-    depth_loss_mult: float = 1e-3
-    """Lambda of the depth loss."""
-    is_euclidean_depth: bool = False
-    """Whether input depth maps are Euclidean distances (or z-distances)."""
-    depth_sigma: float = 0.01
-    """Uncertainty around depth values in meters (defaults to 1cm)."""
-    should_decay_sigma: bool = False
-    """Whether to exponentially decay sigma."""
-    starting_depth_sigma: float = 0.2
-    """Starting uncertainty around depth values in meters (defaults to 0.2m)."""
-    sigma_decay_rate: float = 0.99985
-    """Rate of exponential decay."""
-    depth_loss_type: DepthLossType = DepthLossType.DS_NERF
-    """Depth loss type."""
-
-
-class DepthNerfactoModel(NerfactoModel):
-    """Depth loss augmented nerfacto model.
-
-    Args:
-        config: Nerfacto configuration to instantiate model
-    """
-
-    config: DepthNerfactoModelConfig
-
-    def populate_modules(self):
-        """Set the fields and modules."""
-        super().populate_modules()
-
-        if self.config.should_decay_sigma:
-            self.depth_sigma = torch.tensor([self.config.starting_depth_sigma])
-        else:
-            self.depth_sigma = torch.tensor([self.config.depth_sigma])
-
-    def get_outputs(self, ray_bundle: RayBundle):
-        outputs = super().get_outputs(ray_bundle)
-        if ray_bundle.metadata is not None and "directions_norm" in ray_bundle.metadata:
-            outputs["directions_norm"] = ray_bundle.metadata["directions_norm"]
-        return outputs
-
-    def get_metrics_dict(self, outputs, batch):
-        metrics_dict = super().get_metrics_dict(outputs, batch)
-        if self.training:
-            if (
-                losses.FORCE_PSEUDODEPTH_LOSS
-                and self.config.depth_loss_type not in losses.PSEUDODEPTH_COMPATIBLE_LOSSES
-            ):
-                raise ValueError(
-                    f"Forcing pseudodepth loss, but depth loss type ({self.config.depth_loss_type}) must be one of {losses.PSEUDODEPTH_COMPATIBLE_LOSSES}"
-                )
-            if self.config.depth_loss_type in (DepthLossType.DS_NERF, DepthLossType.URF):
-                metrics_dict["depth_loss"] = 0.0
-                sigma = self._get_sigma().to(self.device)
-                termination_depth = batch["depth_image"].to(self.device)
-                for i in range(len(outputs["weights_list"])):
-                    metrics_dict["depth_loss"] += depth_loss(
-                        weights=outputs["weights_list"][i],
-                        ray_samples=outputs["ray_samples_list"][i],
-                        termination_depth=termination_depth,
-                        predicted_depth=outputs["expected_depth"],
-                        sigma=sigma,
-                        directions_norm=outputs["directions_norm"],
-                        is_euclidean=self.config.is_euclidean_depth,
-                        depth_loss_type=self.config.depth_loss_type,
-                    ) / len(outputs["weights_list"])
-            elif self.config.depth_loss_type in (DepthLossType.SPARSENERF_RANKING,):
-                metrics_dict["depth_ranking"] = depth_ranking_loss(
-                    outputs["expected_depth"], batch["depth_image"].to(self.device)
-                )
-            else:
-                raise NotImplementedError(f"Unknown depth loss type {self.config.depth_loss_type}")
-
-        return metrics_dict
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None):
-        loss_dict = super().get_loss_dict(outputs, batch, metrics_dict)
-        if self.training:
-            assert metrics_dict is not None and ("depth_loss" in metrics_dict or "depth_ranking" in metrics_dict)
-            if "depth_ranking" in metrics_dict:
-                loss_dict["depth_ranking"] = (
-                    self.config.depth_loss_mult
-                    * np.interp(self.step, [0, 2000], [0, 0.2])
-                    * metrics_dict["depth_ranking"]
-                )
-            if "depth_loss" in metrics_dict:
-                loss_dict["depth_loss"] = self.config.depth_loss_mult * metrics_dict["depth_loss"]
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        """Appends ground truth depth to the depth image."""
-        metrics, images = super().get_image_metrics_and_images(outputs, batch)
-        ground_truth_depth = batch["depth_image"].to(self.device)
-        if not self.config.is_euclidean_depth:
-            ground_truth_depth = ground_truth_depth * outputs["directions_norm"]
-
-        ground_truth_depth_colormap = colormaps.apply_depth_colormap(ground_truth_depth)
-        predicted_depth_colormap = colormaps.apply_depth_colormap(
-            outputs["depth"],
-            accumulation=outputs["accumulation"],
-            near_plane=float(torch.min(ground_truth_depth).cpu()),
-            far_plane=float(torch.max(ground_truth_depth).cpu()),
-        )
-        images["depth"] = torch.cat([ground_truth_depth_colormap, predicted_depth_colormap], dim=1)
-        depth_mask = ground_truth_depth > 0
-        metrics["depth_mse"] = float(
-            torch.nn.functional.mse_loss(outputs["depth"][depth_mask], ground_truth_depth[depth_mask]).cpu()
-        )
-        return metrics, images
-
-    def _get_sigma(self):
-        if not self.config.should_decay_sigma:
-            return self.depth_sigma
-
-        self.depth_sigma = torch.maximum(
-            self.config.sigma_decay_rate * self.depth_sigma, torch.tensor([self.config.depth_sigma])
-        )
-        return self.depth_sigma
diff --git a/nerfstudio/models/generfacto.py b/nerfstudio/models/generfacto.py
deleted file mode 100644
index 5c1316ab..00000000
--- a/nerfstudio/models/generfacto.py
+++ /dev/null
@@ -1,520 +0,0 @@
-# Copyright 2022 The Nerfstudio Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Nerfstudio's Text to 3D model.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from torch.nn import Parameter
-from typing_extensions import Literal
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.fields.density_fields import HashMLPDensityField
-from nerfstudio.fields.generfacto_field import GenerfactoField
-from nerfstudio.generative.deepfloyd import DeepFloyd
-from nerfstudio.generative.positional_text_embeddings import PositionalTextEmbeddings
-from nerfstudio.generative.stable_diffusion import StableDiffusion
-from nerfstudio.model_components.losses import MSELoss, distortion_loss, interlevel_loss, orientation_loss
-from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler, UniformSampler
-from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, NormalsRenderer, RGBRenderer
-from nerfstudio.model_components.scene_colliders import AABBBoxCollider, SphereCollider
-from nerfstudio.model_components.shaders import LambertianShader, NormalsShader
-from nerfstudio.models.base_model import Model, ModelConfig
-from nerfstudio.utils import colormaps, math, misc
-
-
-@dataclass
-class GenerfactoModelConfig(ModelConfig):
-    """Generfacto model config"""
-
-    _target: Type = field(default_factory=lambda: GenerfactoModel)
-    """target class to instantiate"""
-    prompt: str = "a high quality photo of a ripe pineapple"
-    """prompt for stable dreamfusion"""
-    background_color: Literal["random", "last_sample", "black", "white"] = "white"
-    """Whether to randomize the background color."""
-    orientation_loss_mult: Tuple[float, float] = (0.001, 10.0)
-    """Orientation loss multipier on computed normals."""
-    orientation_loss_mult_range: Tuple[int, int] = (0, 15000)
-    """number of iterations to reach last orientation_loss_mult value"""
-    random_light_source: bool = True
-    """Randomizes light source per output."""
-    initialize_density: bool = True
-    """Initialize density in center of scene."""
-    taper_range: Tuple[int, int] = (0, 1000)
-    """Range of step values for the density tapering"""
-    taper_strength: Tuple[float, float] = (1.0, 0.0)
-    """Strength schedule of center density"""
-    sphere_collider: bool = True
-    """Use spherical collider instead of box"""
-    random_background: bool = True
-    """Randomly choose between using background mlp and random color for background"""
-
-    target_transmittance_start: float = 0.4
-    """target transmittance for opacity penalty. This is the percent of the scene that is
-    background when rendered at the start of training"""
-    target_transmittance_end: float = 0.7
-    """target transmittance for opacity penalty. This is the percent of the scene that is
-    background when rendered at the end of training"""
-    transmittance_end_schedule: int = 1500
-    """number of iterations to reach target_transmittance_end"""
-
-    num_proposal_samples_per_ray: Tuple[int, ...] = (256, 96)
-    """Number of samples per ray for each proposal network."""
-    num_nerf_samples_per_ray: int = 48
-    """Number of samples per ray for the nerf network."""
-    proposal_update_every: int = 5
-    """Sample every n steps after the warmup"""
-    proposal_warmup: int = 5000
-    """Scales n from 1 to proposal_update_every over this many steps"""
-    num_proposal_iterations: int = 2
-    """Number of proposal network iterations."""
-    use_same_proposal_network: bool = False
-    """Use the same proposal network. Otherwise use different ones."""
-    proposal_net_args_list: List[Dict] = field(
-        default_factory=lambda: [
-            {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 128},
-            {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 256},
-        ]
-    )
-    """Arguments for the proposal density fields."""
-    proposal_weights_anneal_slope: float = 10.0
-    """Slope of the annealing function for the proposal weights."""
-    proposal_weights_anneal_max_num_iters: int = 500
-    """Max num iterations for the annealing function."""
-    use_single_jitter: bool = True
-    """Whether use single jitter or not for the proposal networks."""
-    interlevel_loss_mult: float = 1.0
-    """Proposal loss multiplier."""
-    distortion_loss_mult: float = 1.0
-    """Distortion loss multiplier."""
-    start_normals_training: int = 1000
-    """Start training normals after this many iterations"""
-    start_lambertian_training: int = 1000
-    """start training with lambertian shading after this many iterations"""
-    opacity_penalty: bool = True
-    """enables penalty to encourage sparse weights (penalizing for uniform density along ray)"""
-    opacity_loss_mult: float = 1
-    """scale for opacity penalty"""
-    max_res: int = 256
-    """Maximum resolution of the density field."""
-
-    location_based_prompting: bool = True
-    """enables location based prompting"""
-    interpolated_prompting: bool = False
-    """enables interpolated location prompting"""
-    positional_prompting: Literal["discrete", "interpolated", "off"] = "discrete"
-    """ how to incorporate position into prompt"""
-    top_prompt: str = ", overhead view"
-    """appended to prompt for overhead view"""
-    side_prompt: str = ", side view"
-    """appended to prompt for side view"""
-    front_prompt: str = ", front view"
-    """appended to prompt for front view"""
-    back_prompt: str = ", back view"
-    """appended to prompt for back view"""
-    guidance_scale: float = 20
-    """guidance scale for sds loss"""
-    diffusion_device: Optional[str] = None
-    """device for diffusion model"""
-    diffusion_model: Literal["stablediffusion", "deepfloyd"] = "deepfloyd"
-    """diffusion model for SDS loss"""
-    sd_version: str = "1-5"
-    """model version when using stable diffusion"""
-    implementation: Literal["tcnn", "torch"] = "tcnn"
-    """Which implementation to use for the model."""
-
-
-class GenerfactoModel(Model):
-    """Generfacto Model
-
-    Args:
-        config: Generfacto configuration to instantiate model
-    """
-
-    config: GenerfactoModelConfig
-
-    def __init__(
-        self,
-        config: GenerfactoModelConfig,
-        **kwargs,
-    ) -> None:
-        self.prompt = config.prompt
-        self.cur_prompt = config.prompt
-        self.sd_version = config.sd_version
-        self.initialize_density = config.initialize_density
-        self.train_normals = False
-        self.train_shaded = False
-        self.random_background = config.random_background
-        self.density_strength = 1.0
-        self.target_transmittance = config.target_transmittance_start
-        self.grad_scaler = kwargs["grad_scaler"]
-
-        self.guidance_scale = config.guidance_scale
-        self.top_prompt = config.top_prompt
-        self.side_prompt = config.side_prompt
-        self.back_prompt = config.back_prompt
-        self.front_prompt = config.front_prompt
-
-        self.diffusion_device = (
-            torch.device(kwargs["device"]) if config.diffusion_device is None else torch.device(config.diffusion_device)
-        )
-
-        super().__init__(config=config, **kwargs)
-
-    def populate_modules(self):
-        """Set the fields and modules"""
-        super().populate_modules()
-
-        if self.config.diffusion_model == "stablediffusion":
-            self._diffusion_model = StableDiffusion(self.diffusion_device, version=self.sd_version)
-        elif self.config.diffusion_model == "deepfloyd":
-            self._diffusion_model = DeepFloyd(self.diffusion_device)
-
-        self.text_embeddings = PositionalTextEmbeddings(
-            base_prompt=self.cur_prompt,
-            top_prompt=self.cur_prompt + self.top_prompt,
-            side_prompt=self.cur_prompt + self.side_prompt,
-            back_prompt=self.cur_prompt + self.back_prompt,
-            front_prompt=self.cur_prompt + self.front_prompt,
-            diffusion_model=self._diffusion_model,
-            positional_prompting=self.config.positional_prompting,
-        )
-
-        # setting up fields
-        self.field = GenerfactoField(self.scene_box.aabb, max_res=self.config.max_res)
-
-        # samplers
-        self.density_fns = []
-        num_prop_nets = self.config.num_proposal_iterations
-        # Build the proposal network(s)
-        self.proposal_networks = torch.nn.ModuleList()
-
-        for i in range(num_prop_nets):
-            prop_net_args = self.config.proposal_net_args_list[min(i, len(self.config.proposal_net_args_list) - 1)]
-            network = HashMLPDensityField(
-                self.scene_box.aabb, **prop_net_args, implementation=self.config.implementation
-            )
-            self.proposal_networks.append(network)
-        self.density_fns.extend([network.density_fn for network in self.proposal_networks])
-
-        def update_schedule(step):
-            return np.clip(
-                np.interp(step, [0, self.config.proposal_warmup], [0, self.config.proposal_update_every]),
-                1,
-                self.config.proposal_update_every,
-            )
-
-        self.proposal_sampler = ProposalNetworkSampler(
-            num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
-            num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
-            num_proposal_network_iterations=self.config.num_proposal_iterations,
-            single_jitter=self.config.use_single_jitter,
-            update_sched=update_schedule,
-            initial_sampler=UniformSampler(single_jitter=self.config.use_single_jitter),
-        )
-
-        # renderers
-        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer()
-        self.renderer_normals = NormalsRenderer()
-
-        # shaders
-        self.shader_lambertian = LambertianShader()
-        self.shader_normals = NormalsShader()
-
-        # losses
-        self.rgb_loss = MSELoss()
-
-        # colliders
-        if self.config.sphere_collider:
-            self.collider = SphereCollider(torch.Tensor([0, 0, 0]), 1.0)
-        else:
-            self.collider = AABBBoxCollider(scene_box=self.scene_box)
-
-    def get_training_callbacks(
-        self, training_callback_attributes: TrainingCallbackAttributes
-    ) -> List[TrainingCallback]:
-        # the callback that we want to run every X iterations after the training iteration
-        def taper_density(
-            self,
-            training_callback_attributes: TrainingCallbackAttributes,
-            step: int,  # pylint: disable=unused-argument
-        ):
-            self.density_strength = np.interp(step, self.config.taper_range, self.config.taper_strength)
-
-        def start_training_normals(
-            self,
-            training_callback_attributes: TrainingCallbackAttributes,
-            step: int,  # pylint: disable=unused-argument
-        ):
-            self.train_normals = True
-
-        def start_shaded_training(
-            self,
-            training_callback_attributes: TrainingCallbackAttributes,
-            step: int,  # pylint: disable=unused-argument
-        ):
-            self.train_shaded = True
-
-        def update_orientation_loss_mult(
-            self,
-            training_callback_attributes: TrainingCallbackAttributes,
-            step: int,  # pylint: disable=unused-argument
-        ):
-            if step <= self.config.start_normals_training:
-                self.orientation_loss_mult = 0
-            else:
-                self.orientation_loss_mult = np.interp(
-                    step,
-                    self.config.orientation_loss_mult_range,
-                    self.config.orientation_loss_mult,
-                )
-
-        # anneal the weights of the proposal network before doing PDF sampling
-        def set_anneal(step):
-            # https://arxiv.org/pdf/2111.12077.pdf eq. 18
-            train_frac = np.clip(step / self.config.proposal_weights_anneal_max_num_iters, 0, 1)
-
-            def bias(x, b):
-                return b * x / ((b - 1) * x + 1)
-
-            anneal = bias(train_frac, self.config.proposal_weights_anneal_slope)
-            self.proposal_sampler.set_anneal(anneal)
-
-        callbacks = [
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                func=taper_density,
-                update_every_num_iters=1,
-                args=[self, training_callback_attributes],
-            ),
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                iters=(self.config.start_normals_training,),
-                func=start_training_normals,
-                args=[self, training_callback_attributes],
-            ),
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                iters=(self.config.start_lambertian_training,),
-                func=start_shaded_training,
-                args=[self, training_callback_attributes],
-            ),
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                update_every_num_iters=1,
-                func=update_orientation_loss_mult,
-                args=[self, training_callback_attributes],
-            ),
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                update_every_num_iters=1,
-                func=self.proposal_sampler.step_cb,
-            ),
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
-                update_every_num_iters=1,
-                func=set_anneal,
-            ),
-        ]
-        return callbacks
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-        param_groups["proposal_networks"] = list(self.proposal_networks.parameters())
-        param_groups["fields"] = list(self.field.parameters())
-        return param_groups
-
-    def get_outputs(self, ray_bundle: RayBundle):  # pylint: disable=too-many-statements
-        # uniform sampling
-        background_rgb = self.field.get_background_rgb(ray_bundle)
-        ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
-        field_outputs = self.field(ray_samples, compute_normals=True)
-        density = field_outputs[FieldHeadNames.DENSITY]
-
-        if self.initialize_density:
-            pos = ray_samples.frustums.get_positions()
-            density_blob = self.density_strength * (-torch.exp(torch.norm(pos, dim=-1) / 0.4) + 2)[..., None]
-            density = torch.max(density + density_blob, torch.tensor([0.0], device=self.device))
-
-        weights = ray_samples.get_weights(density)
-        weights_list.append(weights)
-        ray_samples_list.append(ray_samples)
-
-        accumulation = self.renderer_accumulation(weights)
-        depth = self.renderer_depth(weights, ray_samples)
-        rgb = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.RGB], weights=weights)
-
-        accum_mask = torch.clamp((torch.nan_to_num(accumulation, nan=0.0)), min=0.0, max=1.0)
-        accum_mask_inv = 1.0 - accum_mask
-
-        background = accum_mask_inv * background_rgb
-
-        outputs = {
-            "rgb_only": rgb,
-            "background_rgb": background_rgb,
-            "background": background,
-            "accumulation": accum_mask,
-            "depth": depth,
-        }
-
-        # These use a lot of GPU memory, so we avoid storing them for eval.
-        if self.training:
-            outputs["weights_list"] = weights_list
-            outputs["ray_samples_list"] = ray_samples_list
-
-        for i in range(self.config.num_proposal_iterations):
-            outputs[f"prop_depth_{i}"] = self.renderer_depth(weights=weights_list[i], ray_samples=ray_samples_list[i])
-
-        normals = self.renderer_normals(normals=field_outputs[FieldHeadNames.NORMALS], weights=weights)
-
-        # lambertian shading
-        if self.config.random_light_source:  # and self.training:
-            light_d = ray_bundle.origins[0] + torch.randn(3, dtype=torch.float).to(normals)
-        else:
-            light_d = ray_bundle.origins[0]
-        light_d = math.safe_normalize(light_d)
-
-        if (self.train_shaded and np.random.random_sample() > 0.75) or not self.training:
-            shading_weight = 0.9
-        else:
-            shading_weight = 0.0
-
-        shaded, shaded_albedo = self.shader_lambertian(
-            rgb=rgb, normals=normals, light_direction=light_d, shading_weight=shading_weight, detach_normals=False
-        )
-
-        shaded, shaded_albedo = accum_mask * shaded, accum_mask * shaded_albedo
-
-        outputs["normals"] = self.shader_normals(normals, weights=accum_mask)
-        outputs["shaded"] = shaded
-        outputs["other_train_output"] = shaded_albedo + background
-        outputs["shaded_albedo"] = shaded_albedo
-        outputs["rgb"] = accum_mask * rgb + background
-
-        # while training 50% of the time use a random background
-        if np.random.random_sample() < 0.5 and self.random_background and self.training:
-            background = torch.ones_like(background) * torch.rand(3, device=self.device) * accum_mask_inv
-
-        if shading_weight > 0:
-            samp = np.random.random_sample()
-            if samp > 0.5:
-                outputs["train_output"] = outputs["shaded"]
-            else:
-                outputs["train_output"] = shaded_albedo + background
-        else:
-            outputs["train_output"] = accum_mask * rgb + background
-
-        outputs["rendered_orientation_loss"] = orientation_loss(
-            weights.detach(), field_outputs[FieldHeadNames.NORMALS], ray_bundle.directions
-        )
-
-        assert weights.shape[-1] == 1
-        if self.config.opacity_penalty:
-            outputs["opacity_loss"] = torch.sqrt(torch.sum(weights, dim=-2) ** 2 + 0.01) * self.config.opacity_loss_mult
-
-        return outputs
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Tensor]:
-        # Scaling metrics by coefficients to create the losses.
-
-        loss_dict = {}
-        loss_dict = misc.scale_dict(loss_dict, self.config.loss_coefficients)
-        if self.train_normals:
-            # orientation loss for computed normals
-            loss_dict["orientation_loss"] = self.orientation_loss_mult * torch.mean(
-                outputs["rendered_orientation_loss"]
-            )
-        else:
-            loss_dict["orientation_loss"] = 0
-
-        if self.config.opacity_penalty:
-            loss_dict["opacity_loss"] = self.config.opacity_loss_mult * outputs["opacity_loss"].mean()
-
-        if self.prompt != self.cur_prompt:
-            self.cur_prompt = self.prompt
-            self.text_embeddings.update_prompt(
-                base_prompt=self.cur_prompt,
-                top_prompt=self.cur_prompt + self.top_prompt,
-                side_prompt=self.cur_prompt + self.side_prompt,
-                back_prompt=self.cur_prompt + self.back_prompt,
-                front_prompt=self.cur_prompt + self.front_prompt,
-            )
-
-        text_embedding = self.text_embeddings.get_text_embedding(
-            vertical_angle=batch["vertical"], horizontal_angle=batch["central"]
-        )
-
-        train_output = (
-            outputs["train_output"]
-            .view(1, int(outputs["train_output"].shape[0] ** 0.5), int(outputs["train_output"].shape[0] ** 0.5), 3)
-            .permute(0, 3, 1, 2)
-        )
-
-        sds_loss = self._diffusion_model.sds_loss(
-            text_embedding.to(self.diffusion_device),
-            train_output.to(self.diffusion_device),
-            guidance_scale=int(self.guidance_scale),
-            grad_scaler=self.grad_scaler,
-        )
-
-        loss_dict["sds_loss"] = sds_loss.to(self.device)
-
-        if self.training:
-            loss_dict["distortion_loss"] = self.config.distortion_loss_mult * distortion_loss(
-                outputs["weights_list"], outputs["ray_samples_list"]
-            )
-            loss_dict["interlevel_loss"] = self.config.interlevel_loss_mult * interlevel_loss(
-                outputs["weights_list"], outputs["ray_samples_list"]
-            )
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        acc = colormaps.apply_colormap(outputs["accumulation"])
-        depth = colormaps.apply_depth_colormap(
-            outputs["depth"],
-            accumulation=outputs["accumulation"],
-        )
-        prop_depth_0 = colormaps.apply_depth_colormap(
-            outputs["prop_depth_0"],
-            accumulation=outputs["accumulation"],
-        )
-        prop_depth_1 = colormaps.apply_depth_colormap(
-            outputs["prop_depth_1"],
-            accumulation=outputs["accumulation"],
-        )
-
-        metrics_dict = {}
-        images_dict = {
-            "img": outputs["rgb"],
-            "accumulation": acc,
-            "depth": depth,
-            "prop_depth_0": prop_depth_0,
-            "prop_depth_1": prop_depth_1,
-            "normals": outputs["normals"],
-        }
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/instant_ngp.py b/nerfstudio/models/instant_ngp.py
deleted file mode 100644
index 2503e7e2..00000000
--- a/nerfstudio/models/instant_ngp.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Implementation of Instant NGP.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Literal, Optional, Tuple, Type
-
-import nerfacc
-import torch
-from torch.nn import Parameter
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.field_components.spatial_distortions import SceneContraction
-from nerfstudio.fields.nerfacto_field import NerfactoField
-from nerfstudio.model_components.losses import MSELoss, scale_gradients_by_distance_squared
-from nerfstudio.model_components.ray_samplers import VolumetricSampler
-from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
-from nerfstudio.models.base_model import Model, ModelConfig
-from nerfstudio.utils import colormaps
-
-
-@dataclass
-class InstantNGPModelConfig(ModelConfig):
-    """Instant NGP Model Config"""
-
-    _target: Type = field(
-        default_factory=lambda: NGPModel
-    )  # We can't write `NGPModel` directly, because `NGPModel` doesn't exist yet
-    """target class to instantiate"""
-    enable_collider: bool = False
-    """Whether to create a scene collider to filter rays."""
-    collider_params: Optional[Dict[str, float]] = None
-    """Instant NGP doesn't use a collider."""
-    grid_resolution: int = 128
-    """Resolution of the grid used for the field."""
-    grid_levels: int = 4
-    """Levels of the grid used for the field."""
-    max_res: int = 2048
-    """Maximum resolution of the hashmap for the base mlp."""
-    log2_hashmap_size: int = 19
-    """Size of the hashmap for the base mlp"""
-    alpha_thre: float = 0.01
-    """Threshold for opacity skipping."""
-    cone_angle: float = 0.004
-    """Should be set to 0.0 for blender scenes but 1./256 for real scenes."""
-    render_step_size: Optional[float] = None
-    """Minimum step size for rendering."""
-    near_plane: float = 0.05
-    """How far along ray to start sampling."""
-    far_plane: float = 1e3
-    """How far along ray to stop sampling."""
-    use_gradient_scaling: bool = False
-    """Use gradient scaler where the gradients are lower for points closer to the camera."""
-    use_appearance_embedding: bool = False
-    """Whether to use an appearance embedding."""
-    background_color: Literal["random", "black", "white"] = "random"
-    """The color that is given to untrained areas."""
-    disable_scene_contraction: bool = False
-    """Whether to disable scene contraction or not."""
-
-
-class NGPModel(Model):
-    """Instant NGP model
-
-    Args:
-        config: instant NGP configuration to instantiate model
-    """
-
-    config: InstantNGPModelConfig
-    field: NerfactoField
-
-    def __init__(self, config: InstantNGPModelConfig, **kwargs) -> None:
-        super().__init__(config=config, **kwargs)
-
-    def populate_modules(self):
-        """Set the fields and modules."""
-        super().populate_modules()
-
-        if self.config.disable_scene_contraction:
-            scene_contraction = None
-        else:
-            scene_contraction = SceneContraction(order=float("inf"))
-
-        self.field = NerfactoField(
-            aabb=self.scene_box.aabb,
-            appearance_embedding_dim=0 if self.config.use_appearance_embedding else 32,
-            num_images=self.num_train_data,
-            log2_hashmap_size=self.config.log2_hashmap_size,
-            max_res=self.config.max_res,
-            spatial_distortion=scene_contraction,
-        )
-
-        self.scene_aabb = Parameter(self.scene_box.aabb.flatten(), requires_grad=False)
-
-        if self.config.render_step_size is None:
-            # auto step size: ~1000 samples in the base level grid
-            self.config.render_step_size = ((self.scene_aabb[3:] - self.scene_aabb[:3]) ** 2).sum().sqrt().item() / 1000
-        # Occupancy Grid.
-        self.occupancy_grid = nerfacc.OccGridEstimator(
-            roi_aabb=self.scene_aabb,
-            resolution=self.config.grid_resolution,
-            levels=self.config.grid_levels,
-        )
-
-        # Sampler
-        self.sampler = VolumetricSampler(
-            occupancy_grid=self.occupancy_grid,
-            density_fn=self.field.density_fn,
-        )
-
-        # renderers
-        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer(method="expected")
-
-        # losses
-        self.rgb_loss = MSELoss()
-
-        # metrics
-        from torchmetrics.functional import structural_similarity_index_measure
-        from torchmetrics.image import PeakSignalNoiseRatio
-        from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-
-        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
-        self.ssim = structural_similarity_index_measure
-        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
-
-    def get_training_callbacks(
-        self, training_callback_attributes: TrainingCallbackAttributes
-    ) -> List[TrainingCallback]:
-        def update_occupancy_grid(step: int):
-            self.occupancy_grid.update_every_n_steps(
-                step=step,
-                occ_eval_fn=lambda x: self.field.density_fn(x) * self.config.render_step_size,
-            )
-
-        return [
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
-                update_every_num_iters=1,
-                func=update_occupancy_grid,
-            ),
-        ]
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-        if self.field is None:
-            raise ValueError("populate_fields() must be called before get_param_groups")
-        param_groups["fields"] = list(self.field.parameters())
-        return param_groups
-
-    def get_outputs(self, ray_bundle: RayBundle):
-        assert self.field is not None
-        num_rays = len(ray_bundle)
-
-        with torch.no_grad():
-            ray_samples, ray_indices = self.sampler(
-                ray_bundle=ray_bundle,
-                near_plane=self.config.near_plane,
-                far_plane=self.config.far_plane,
-                render_step_size=self.config.render_step_size,
-                alpha_thre=self.config.alpha_thre,
-                cone_angle=self.config.cone_angle,
-            )
-
-        field_outputs = self.field(ray_samples)
-        if self.config.use_gradient_scaling:
-            field_outputs = scale_gradients_by_distance_squared(field_outputs, ray_samples)
-
-        # accumulation
-        packed_info = nerfacc.pack_info(ray_indices, num_rays)
-        weights = nerfacc.render_weight_from_density(
-            t_starts=ray_samples.frustums.starts[..., 0],
-            t_ends=ray_samples.frustums.ends[..., 0],
-            sigmas=field_outputs[FieldHeadNames.DENSITY][..., 0],
-            packed_info=packed_info,
-        )[0]
-        weights = weights[..., None]
-
-        rgb = self.renderer_rgb(
-            rgb=field_outputs[FieldHeadNames.RGB],
-            weights=weights,
-            ray_indices=ray_indices,
-            num_rays=num_rays,
-        )
-        depth = self.renderer_depth(
-            weights=weights, ray_samples=ray_samples, ray_indices=ray_indices, num_rays=num_rays
-        )
-        accumulation = self.renderer_accumulation(weights=weights, ray_indices=ray_indices, num_rays=num_rays)
-
-        outputs = {
-            "rgb": rgb,
-            "accumulation": accumulation,
-            "depth": depth,
-            "num_samples_per_ray": packed_info[:, 1],
-        }
-        return outputs
-
-    def get_metrics_dict(self, outputs, batch):
-        image = batch["image"].to(self.device)
-        image = self.renderer_rgb.blend_background(image)
-        metrics_dict = {}
-        metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
-        metrics_dict["num_samples_per_batch"] = outputs["num_samples_per_ray"].sum()
-        return metrics_dict
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None):
-        image = batch["image"].to(self.device)
-        pred_rgb, image = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb"],
-            pred_accumulation=outputs["accumulation"],
-            gt_image=image,
-        )
-        rgb_loss = self.rgb_loss(image, pred_rgb)
-        loss_dict = {"rgb_loss": rgb_loss}
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        image = batch["image"].to(self.device)
-        image = self.renderer_rgb.blend_background(image)
-        rgb = outputs["rgb"]
-        acc = colormaps.apply_colormap(outputs["accumulation"])
-        depth = colormaps.apply_depth_colormap(
-            outputs["depth"],
-            accumulation=outputs["accumulation"],
-        )
-
-        combined_rgb = torch.cat([image, rgb], dim=1)
-        combined_acc = torch.cat([acc], dim=1)
-        combined_depth = torch.cat([depth], dim=1)
-
-        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
-        image = torch.moveaxis(image, -1, 0)[None, ...]
-        rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
-
-        psnr = self.psnr(image, rgb)
-        ssim = self.ssim(image, rgb)
-        lpips = self.lpips(image, rgb)
-
-        # all of these metrics will be logged as scalars
-        metrics_dict = {"psnr": float(psnr.item()), "ssim": float(ssim), "lpips": float(lpips)}  # type: ignore
-        # TODO(ethan): return an image dictionary
-
-        images_dict = {
-            "img": combined_rgb,
-            "accumulation": combined_acc,
-            "depth": combined_depth,
-        }
-
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/lidar_nerfacto.py b/nerfstudio/models/lidar_nerfacto.py
new file mode 100644
index 00000000..e059fe8b
--- /dev/null
+++ b/nerfstudio/models/lidar_nerfacto.py
@@ -0,0 +1,137 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+# Copyright 2022 The Nerfstudio Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Nerfacto augmented with depth supervision.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Dict, Tuple, Type
+
+import torch
+
+from nerfstudio.cameras.rays import RayBundle
+from nerfstudio.model_components.losses import DepthLossType, depth_loss
+from nerfstudio.model_components.renderers import DepthRenderer
+from nerfstudio.models.ad_model import ADModel, ADModelConfig
+from nerfstudio.models.nerfacto import NerfactoModel, NerfactoModelConfig
+
+
+@dataclass
+class LidarNerfactoModelConfig(NerfactoModelConfig, ADModelConfig):
+    """Additional parameters for depth supervision."""
+
+    _target: Type = field(default_factory=lambda: LidarNerfactoModel)
+    depth_loss_mult: float = 1e-3
+    """Lambda of the depth loss."""
+    is_euclidean_depth: bool = True
+    """Whether input depth maps are Euclidean distances (or z-distances)."""
+    depth_sigma: float = 0.01
+    """Uncertainty around depth values in meters (defaults to 1cm)."""
+    should_decay_sigma: bool = True
+    """Whether to exponentially decay sigma."""
+    starting_depth_sigma: float = 0.2
+    """Starting uncertainty around depth values in meters (defaults to 0.2m)."""
+    sigma_decay_rate: float = 0.99985
+    """Rate of exponential decay."""
+    depth_loss_type: DepthLossType = DepthLossType.DS_NERF
+    """Depth loss type."""
+    use_gradient_scaling: bool = True
+    """Use gradient scaler where the gradients are lower for points closer to the camera."""
+
+
+class LidarNerfactoModel(NerfactoModel, ADModel):
+    """Depth loss augmented nerfacto model.
+
+    Args:
+        config: Nerfacto configuration to instantiate model
+    """
+
+    config: LidarNerfactoModelConfig
+
+    def populate_modules(self):
+        """Set the fields and modules."""
+        super().populate_modules()
+
+        if self.config.should_decay_sigma:
+            self.depth_sigma = torch.tensor([self.config.starting_depth_sigma])
+        else:
+            self.depth_sigma = torch.tensor([self.config.depth_sigma])
+
+        self.renderer_depth = DepthRenderer(method="expected")
+
+        self.median_l2 = lambda pred, gt: torch.median((pred - gt) ** 2)
+
+    def get_outputs(self, ray_bundle: RayBundle):
+        outputs = super().get_outputs(ray_bundle)
+        if ray_bundle.metadata is not None and "directions_norm" in ray_bundle.metadata:
+            outputs["directions_norm"] = ray_bundle.metadata["directions_norm"]
+        if "is_lidar" in ray_bundle.metadata:
+            outputs["rgb"] = outputs["rgb"][~ray_bundle.metadata["is_lidar"][:, 0]]
+        return outputs
+
+    def get_metrics_dict(self, outputs, batch):
+        metrics_dict = super().get_metrics_dict(outputs, batch)
+        if self.training and "lidar" in batch:
+            metrics_dict["depth_loss"] = 0.0
+            is_lidar = batch["is_lidar"][:, 0]
+            sigma = self._get_sigma().to(self.device)
+            pred_depths = [outputs[f"prop_depth_{i}"] for i in range(len(outputs["weights_list"]) - 1)]
+            pred_depths.append(outputs["depth"])
+            lidar_scale = batch.get("lidar_scale", [1.0])[0]
+            for i in range(len(outputs["weights_list"])):
+                metrics_dict["depth_loss"] += depth_loss(
+                    weights=outputs["weights_list"][i][is_lidar],
+                    ray_samples=outputs["ray_samples_list"][i][is_lidar],
+                    termination_depth=batch["distance"],
+                    predicted_depth=pred_depths[i][is_lidar],
+                    sigma=sigma * lidar_scale,
+                    directions_norm=outputs["directions_norm"][is_lidar],
+                    is_euclidean=self.config.is_euclidean_depth,
+                    depth_loss_type=self.config.depth_loss_type,
+                    scaling_factor=lidar_scale,
+                ) / len(outputs["weights_list"])
+
+        return metrics_dict
+
+    def get_loss_dict(self, outputs, batch, metrics_dict=None):
+        loss_dict = super().get_loss_dict(outputs, batch, metrics_dict)
+        if self.training:
+            assert metrics_dict
+            if "depth_loss" in metrics_dict:
+                loss_dict["depth_loss"] = self.config.depth_loss_mult * metrics_dict["depth_loss"]
+
+        return loss_dict
+
+    def get_image_metrics_and_images(
+        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
+    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
+        metrics, images = {}, {}
+        if "image" in batch:
+            metrics, images = super().get_image_metrics_and_images(outputs, batch)
+        if "lidar" in batch:
+            metrics["depth_median_l2"] = float(self.median_l2(outputs["depth"], batch["distance"]))
+        return metrics, images
+
+    def _get_sigma(self):
+        if not self.config.should_decay_sigma:
+            return self.depth_sigma
+
+        self.depth_sigma = torch.maximum(  # pylint: disable=attribute-defined-outside-init
+            self.config.sigma_decay_rate * self.depth_sigma, torch.tensor([self.config.depth_sigma])
+        )
+        return self.depth_sigma
diff --git a/nerfstudio/models/mipnerf.py b/nerfstudio/models/mipnerf.py
deleted file mode 100644
index e033681f..00000000
--- a/nerfstudio/models/mipnerf.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Implementation of mip-NeRF.
-"""
-from __future__ import annotations
-
-from typing import Dict, List, Tuple
-
-import torch
-from torch.nn import Parameter
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.field_components.encodings import NeRFEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.fields.vanilla_nerf_field import NeRFField
-from nerfstudio.model_components.losses import MSELoss, scale_gradients_by_distance_squared
-from nerfstudio.model_components.ray_samplers import PDFSampler, UniformSampler
-from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
-from nerfstudio.models.base_model import Model
-from nerfstudio.models.vanilla_nerf import VanillaModelConfig
-from nerfstudio.utils import colormaps, misc
-
-
-class MipNerfModel(Model):
-    """mip-NeRF model
-
-    Args:
-        config: MipNerf configuration to instantiate model
-    """
-
-    config: VanillaModelConfig
-
-    def __init__(
-        self,
-        config: VanillaModelConfig,
-        **kwargs,
-    ) -> None:
-        self.field = None
-        assert config.collider_params is not None, "MipNeRF model requires bounding box collider parameters."
-        super().__init__(config=config, **kwargs)
-        assert self.config.collider_params is not None, "mip-NeRF requires collider parameters to be set."
-
-    def populate_modules(self):
-        """Set the fields and modules"""
-        super().populate_modules()
-
-        # setting up fields
-        position_encoding = NeRFEncoding(
-            in_dim=3, num_frequencies=16, min_freq_exp=0.0, max_freq_exp=16.0, include_input=True
-        )
-        direction_encoding = NeRFEncoding(
-            in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True
-        )
-
-        self.field = NeRFField(
-            position_encoding=position_encoding, direction_encoding=direction_encoding, use_integrated_encoding=True
-        )
-
-        # samplers
-        self.sampler_uniform = UniformSampler(num_samples=self.config.num_coarse_samples)
-        self.sampler_pdf = PDFSampler(num_samples=self.config.num_importance_samples, include_original=False)
-
-        # renderers
-        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer()
-
-        # losses
-        self.rgb_loss = MSELoss()
-
-        # metrics
-        from torchmetrics.functional import structural_similarity_index_measure
-        from torchmetrics.image import PeakSignalNoiseRatio
-        from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-
-        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
-        self.ssim = structural_similarity_index_measure
-        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-        if self.field is None:
-            raise ValueError("populate_fields() must be called before get_param_groups")
-        param_groups["fields"] = list(self.field.parameters())
-        return param_groups
-
-    def get_outputs(self, ray_bundle: RayBundle):
-        if self.field is None:
-            raise ValueError("populate_fields() must be called before get_outputs")
-
-        # uniform sampling
-        ray_samples_uniform = self.sampler_uniform(ray_bundle)
-
-        # First pass:
-        field_outputs_coarse = self.field.forward(ray_samples_uniform)
-        if self.config.use_gradient_scaling:
-            field_outputs_coarse = scale_gradients_by_distance_squared(field_outputs_coarse, ray_samples_uniform)
-        weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse[FieldHeadNames.DENSITY])
-        rgb_coarse = self.renderer_rgb(
-            rgb=field_outputs_coarse[FieldHeadNames.RGB],
-            weights=weights_coarse,
-        )
-        accumulation_coarse = self.renderer_accumulation(weights_coarse)
-        depth_coarse = self.renderer_depth(weights_coarse, ray_samples_uniform)
-
-        # pdf sampling
-        ray_samples_pdf = self.sampler_pdf(ray_bundle, ray_samples_uniform, weights_coarse)
-
-        # Second pass:
-        field_outputs_fine = self.field.forward(ray_samples_pdf)
-        if self.config.use_gradient_scaling:
-            field_outputs_fine = scale_gradients_by_distance_squared(field_outputs_fine, ray_samples_pdf)
-        weights_fine = ray_samples_pdf.get_weights(field_outputs_fine[FieldHeadNames.DENSITY])
-        rgb_fine = self.renderer_rgb(
-            rgb=field_outputs_fine[FieldHeadNames.RGB],
-            weights=weights_fine,
-        )
-        accumulation_fine = self.renderer_accumulation(weights_fine)
-        depth_fine = self.renderer_depth(weights_fine, ray_samples_pdf)
-
-        outputs = {
-            "rgb_coarse": rgb_coarse,
-            "rgb_fine": rgb_fine,
-            "accumulation_coarse": accumulation_coarse,
-            "accumulation_fine": accumulation_fine,
-            "depth_coarse": depth_coarse,
-            "depth_fine": depth_fine,
-        }
-        return outputs
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None):
-        image = batch["image"].to(self.device)
-        pred_coarse, image_coarse = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb_coarse"],
-            pred_accumulation=outputs["accumulation_coarse"],
-            gt_image=image,
-        )
-        pred_fine, image_fine = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb_fine"],
-            pred_accumulation=outputs["accumulation_fine"],
-            gt_image=image,
-        )
-        rgb_loss_coarse = self.rgb_loss(image_coarse, pred_coarse)
-        rgb_loss_fine = self.rgb_loss(image_fine, pred_fine)
-        loss_dict = {"rgb_loss_coarse": rgb_loss_coarse, "rgb_loss_fine": rgb_loss_fine}
-        loss_dict = misc.scale_dict(loss_dict, self.config.loss_coefficients)
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        assert self.config.collider_params is not None, "mip-NeRF requires collider parameters to be set."
-        image = batch["image"].to(outputs["rgb_coarse"].device)
-        image = self.renderer_rgb.blend_background(image)
-        rgb_coarse = outputs["rgb_coarse"]
-        rgb_fine = outputs["rgb_fine"]
-        acc_coarse = colormaps.apply_colormap(outputs["accumulation_coarse"])
-        acc_fine = colormaps.apply_colormap(outputs["accumulation_fine"])
-
-        assert self.config.collider_params is not None
-        depth_coarse = colormaps.apply_depth_colormap(
-            outputs["depth_coarse"],
-            accumulation=outputs["accumulation_coarse"],
-            near_plane=self.config.collider_params["near_plane"],
-            far_plane=self.config.collider_params["far_plane"],
-        )
-        depth_fine = colormaps.apply_depth_colormap(
-            outputs["depth_fine"],
-            accumulation=outputs["accumulation_fine"],
-            near_plane=self.config.collider_params["near_plane"],
-            far_plane=self.config.collider_params["far_plane"],
-        )
-
-        combined_rgb = torch.cat([image, rgb_coarse, rgb_fine], dim=1)
-        combined_acc = torch.cat([acc_coarse, acc_fine], dim=1)
-        combined_depth = torch.cat([depth_coarse, depth_fine], dim=1)
-
-        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
-        image = torch.moveaxis(image, -1, 0)[None, ...]
-        rgb_coarse = torch.moveaxis(rgb_coarse, -1, 0)[None, ...]
-        rgb_fine = torch.moveaxis(rgb_fine, -1, 0)[None, ...]
-        rgb_coarse = torch.clip(rgb_coarse, min=0, max=1)
-        rgb_fine = torch.clip(rgb_fine, min=0, max=1)
-
-        coarse_psnr = self.psnr(image, rgb_coarse)
-        fine_psnr = self.psnr(image, rgb_fine)
-        fine_ssim = self.ssim(image, rgb_fine)
-        fine_lpips = self.lpips(image, rgb_fine)
-
-        assert isinstance(fine_ssim, torch.Tensor)
-        metrics_dict = {
-            "psnr": float(fine_psnr.item()),
-            "coarse_psnr": float(coarse_psnr.item()),
-            "fine_psnr": float(fine_psnr.item()),
-            "fine_ssim": float(fine_ssim.item()),
-            "fine_lpips": float(fine_lpips.item()),
-        }
-        images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/neurad.py b/nerfstudio/models/neurad.py
new file mode 100644
index 00000000..acd9b983
--- /dev/null
+++ b/nerfstudio/models/neurad.py
@@ -0,0 +1,723 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+NeuRAD (Neural Rendering for Autonomous Driving) model.
+"""
+
+from __future__ import annotations
+
+import math
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Dict, List, Literal, Optional, Tuple, Type, Union
+
+import nerfacc
+import torch
+from jaxtyping import Float, Int
+from torch import Tensor
+from torch.nn import BCEWithLogitsLoss, Parameter
+from torchmetrics.functional import structural_similarity_index_measure
+from torchmetrics.image import PeakSignalNoiseRatio
+from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+
+from nerfstudio.cameras.rays import RayBundle, RaySamples
+from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackLocation
+from nerfstudio.field_components.field_heads import FieldHeadNames
+from nerfstudio.field_components.mlp import MLP
+from nerfstudio.fields.neurad_field import (
+    NeuRADField,
+    NeuRADFieldConfig,
+    NeuRADProposalField,
+    NeuRADProposalFieldConfig,
+)
+from nerfstudio.model_components.cnns import BasicBlock
+from nerfstudio.model_components.losses import (
+    L1Loss,
+    MSELoss,
+    VGGPerceptualLossPix2Pix,
+    distortion_loss,
+    zipnerf_interlevel_loss,
+)
+from nerfstudio.model_components.ray_samplers import PowerSampler, ProposalNetworkSampler
+from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, FeatureRenderer, NormalsRenderer
+from nerfstudio.models.ad_model import ADModel, ADModelConfig
+from nerfstudio.utils import colormaps
+from nerfstudio.utils.math import chamfer_distance
+from nerfstudio.viewer.server.viewer_elements import ViewerSlider
+
+EPS = 1e-7
+
+
+@dataclass
+class LossSettings:
+    """Configuration of all the losses."""
+
+    vgg_mult: float = 0.05
+    """Multipier for VGG perceptual loss."""
+    rgb_mult: float = 5.0
+    """Multipier for RGB loss."""
+    depth_mult: float = 0.01
+    """Multipier for lidar loss."""
+    intensity_mult: float = 0.1
+    """Multipier for lidar intensity loss."""
+    carving_mult: float = 0.01
+    """Multiplier for the lidar carving loss."""
+    carving_epsilon: float = 0.1
+    """Epsilon for regularization losses (both carving and eikonal), in meters."""
+    quantile_threshold: float = 0.95
+    """Quantile threshold for lidar and regularization losses."""
+    interlevel_loss_mult: float = 0.001
+    """Multiplier for the interlevel loss."""
+    distortion_loss_mult: float = 0.002
+    """Multiplier for the distortion loss."""
+    non_return_lidar_distance: float = 150.0
+    """Distance threshold for the non-return carving loss (in meters)."""
+    non_return_loss_mult: float = 0.1
+    """Multiplier for the non-return lidar losses (on top of existing multiplier)."""
+    ray_drop_loss_mult: float = 0.01
+    """Multiplier for the ray drop loss."""
+    prop_lidar_loss_mult: float = 0.1
+    """Multiplier for the proposal depth loss (on top of existing multiplier)."""
+
+
+@dataclass
+class SamplingSettings:
+    """Configuration of NeuRADs proposal sampling."""
+
+    single_jitter: bool = True
+    """Use the same random jitter for all samples in the same ray (in training)."""
+
+    proposal_field_1: NeuRADProposalFieldConfig = field(default_factory=NeuRADProposalFieldConfig)
+    """First proposal field configuration."""
+    proposal_field_2: NeuRADProposalFieldConfig = field(default_factory=NeuRADProposalFieldConfig)
+    """Second proposal field configuration."""
+    num_proposal_samples: Tuple[int, ...] = (128, 64)
+    """Number of proposal samples per ray."""
+    num_nerf_samples: int = 32
+    """Number of nerf samples per ray."""
+    power_lambda: float = -1.0
+    """Lambda for the distance power function."""
+    power_scaling: float = 0.1
+    """Scaling for the distance power function."""
+    sky_distance: float = 20000.0
+    """Distance to use for the sky field."""
+
+
+@dataclass
+class NeuRADModelConfig(ADModelConfig):
+    """Overall configuration of NeuRAD."""
+
+    _target: Type = field(default_factory=lambda: NeuRADModel)
+    """Target class to instantiate."""
+
+    loss: LossSettings = field(default_factory=LossSettings)
+    """All the loss parameters."""
+    sampling: SamplingSettings = field(default_factory=SamplingSettings)
+    """All the sampling parameters."""
+    field: NeuRADFieldConfig = field(default_factory=NeuRADFieldConfig)
+    """Main field configuration."""
+
+    appearance_dim: int = 16
+    """Dimensionality of the appearance embedding."""
+    use_temporal_appearance: bool = True
+    """Whether to use temporal appearance or not."""
+    temporal_appearance_freq: float = 1.0
+    """Frequency of temporal appearance."""
+
+    rgb_upsample_factor: int = 3
+    """Upsampling factor for the rgb decoder."""
+    rgb_hidden_dim: int = 32
+    """Dimensionality of the hidden layers in the CNN."""
+
+    implementation: Literal["tcnn", "torch"] = "tcnn"
+    """Which implementation to use for the model."""
+
+    compensate_upsampling_when_rendering: bool = True
+    """Compensate for upsampling when asked to render an image of some given resolution."""
+    normalize_depth: bool = False
+    """Whether to normalize depth by dividing by accumulation."""
+    verbose: bool = False
+    """Whether to log additional images and out the model configuration."""
+
+    @property
+    def num_proposal_rounds(self):
+        return len(self.sampling.num_proposal_samples)
+
+    @property
+    def fields(self) -> list[Union[NeuRADFieldConfig, NeuRADProposalFieldConfig]]:
+        return [self.field, self.sampling.proposal_field_1, self.sampling.proposal_field_2]
+
+
+class NeuRADModel(ADModel):
+    """NeuRAD model.
+
+    Args:
+        config: NeuRAD configuration to instantiate model
+    """
+
+    config: NeuRADModelConfig
+
+    def populate_modules(self):
+        """Set the fields and modules."""
+        super().populate_modules()
+        self.field = self.config.field.setup(
+            actors=self.dynamic_actors,
+            static_scale=self.scene_box.aabb.max(),
+            implementation=self.config.implementation,
+        )
+
+        # Appearance embedding settings
+        dataset_metadata = self.kwargs["metadata"]
+        self._duration = dataset_metadata["duration"]
+        num_sensors = len(dataset_metadata["sensor_idx_to_name"])
+        if self.config.use_temporal_appearance:
+            self._num_embeds_per_sensor = math.ceil(self._duration * self.config.temporal_appearance_freq)
+            num_embeds = num_sensors * self._num_embeds_per_sensor
+        else:
+            num_embeds = num_sensors
+        self.appearance_embedding = torch.nn.Embedding(num_embeds, self.config.appearance_dim)
+        self.fallback_sensor_idx = ViewerSlider("fallback sensor idx", 0, 0, num_sensors - 1, step=1)
+
+        # Modality decoders
+        hidden_dim = self.config.rgb_hidden_dim
+        in_dim = self.config.field.nff_out_dim + self.config.appearance_dim
+        self.rgb_decoder = torch.nn.Sequential(
+            torch.nn.Conv2d(in_dim, hidden_dim, kernel_size=1, padding=0),
+            torch.nn.ReLU(inplace=True),
+            BasicBlock(hidden_dim, hidden_dim, kernel_size=7, padding=3, use_bn=True),
+            BasicBlock(hidden_dim, hidden_dim, kernel_size=7, padding=3, use_bn=True),
+            torch.nn.ConvTranspose2d(
+                hidden_dim,
+                hidden_dim,
+                kernel_size=self.config.rgb_upsample_factor,
+                stride=self.config.rgb_upsample_factor,
+            ),
+            BasicBlock(hidden_dim, hidden_dim, kernel_size=7, padding=3, use_bn=True),
+            BasicBlock(hidden_dim, hidden_dim, kernel_size=7, padding=3, use_bn=True),
+            torch.nn.Conv2d(hidden_dim, 3, kernel_size=1, padding=0),
+            torch.nn.Sigmoid(),
+        )
+        self.lidar_decoder = MLP(
+            in_dim=self.config.field.nff_out_dim + self.config.appearance_dim,
+            layer_width=32,
+            out_dim=2,
+            num_layers=3,
+            implementation=self.config.implementation,
+            out_activation=None,
+        )
+
+        # Sampler
+        self.sampler = ProposalNetworkSampler(
+            num_proposal_samples_per_ray=self.config.sampling.num_proposal_samples,
+            num_nerf_samples_per_ray=self.config.sampling.num_nerf_samples,
+            num_proposal_network_iterations=self.config.num_proposal_rounds,
+            single_jitter=self.config.sampling.single_jitter,
+            initial_sampler=PowerSampler(
+                lambda_=self.config.sampling.power_lambda,
+                scaling=self.config.sampling.power_scaling,
+            ),
+            update_sched=lambda x: 0,
+        )
+        self.proposal_fields = torch.nn.ModuleList(
+            [
+                conf.setup(
+                    actors=self.dynamic_actors,
+                    static_scale=self.scene_box.aabb.max(),
+                    implementation=self.config.implementation,
+                )
+                for conf in (self.config.sampling.proposal_field_1, self.config.sampling.proposal_field_2)
+            ]
+        )
+        self.density_fns = [lambda x: prop_field.get_density(x)[0] for prop_field in self.proposal_fields]
+
+        # renderers
+        self.renderer_feat = FeatureRenderer()
+        self.renderer_accumulation = AccumulationRenderer()
+        self.renderer_depth = DepthRenderer(method="expected") if self.config.normalize_depth else render_depth_simple
+        self.renderer_normals = NormalsRenderer()
+
+        # losses
+        self.rgb_loss = MSELoss()
+        self.depth_loss = L1Loss(reduction="none")
+        self.intensity_loss = MSELoss(reduction="none")
+        self.vgg_loss = VGGPerceptualLossPix2Pix()
+        self.ray_drop_loss = BCEWithLogitsLoss()
+        self.interlevel_loss = zipnerf_interlevel_loss
+
+        # metrics
+        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
+        self.ssim = structural_similarity_index_measure
+        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
+        self.median_l2 = lambda pred, gt: torch.median((pred - gt) ** 2)
+        self.mean_rel_l2 = lambda pred, gt: torch.mean(((pred - gt) / gt) ** 2)
+        self.rmse = lambda pred, gt: torch.sqrt(torch.mean((pred - gt) ** 2))
+        self.chamfer_distance = lambda pred, gt: chamfer_distance(pred, gt, 1_000, True)
+
+    @property
+    def fields(self) -> list[Union[NeuRADField, NeuRADProposalField]]:
+        """Convenience accessor for all fields."""
+        return [self.field, *self.proposal_fields]  # type: ignore
+
+    def disable_ray_drop(self):
+        self.config.loss.ray_drop_loss_mult = 0.0
+
+    def get_param_groups(self) -> Dict[str, List[Parameter]]:
+        """Get the parameter groups for the optimizer."""
+        param_groups = super().get_param_groups()
+        for field_ in self.fields:
+            field_.get_param_groups(param_groups)
+        param_groups["fields"] += list(self.lidar_decoder.parameters())
+        param_groups["fields"] += list(self.appearance_embedding.parameters())
+        param_groups["cnn"] += self.rgb_decoder.parameters()
+        return param_groups
+
+    def get_training_callbacks(self, _) -> List[TrainingCallback]:
+        callbacks = []
+        callbacks.append(
+            TrainingCallback(
+                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
+                update_every_num_iters=1,
+                func=self.sampler.step_cb,
+            )
+        )
+        return callbacks
+
+    def forward(
+        self,
+        ray_bundle: RayBundle,
+        patch_size: Tuple[int, int] = (1, 1),
+        intensity_for_cam: bool = False,
+        calc_lidar_losses: bool = True,
+    ):
+        return self.get_outputs(ray_bundle, patch_size, intensity_for_cam, calc_lidar_losses)
+
+    def get_outputs(
+        self,
+        ray_bundle: RayBundle,
+        patch_size: Tuple[int, int],
+        intensity_for_cam: bool = False,
+        calc_lidar_losses: bool = True,
+    ):
+        if self.training:
+            self.camera_optimizer.apply_to_raybundle(ray_bundle)
+        nff_outputs = self.get_nff_outputs(ray_bundle, calc_lidar_losses)
+        rgb, intensity, ray_drop_logits = self.decode_features(
+            features=nff_outputs["features"],
+            patch_size=patch_size,
+            is_lidar=ray_bundle.metadata.get("is_lidar"),
+            intensity_for_cam=intensity_for_cam,
+        )
+        # Drop features because it's too trippy (and big)
+        nff_outputs.pop("features", None)
+        if rgb is not None:
+            nff_outputs["rgb"] = rgb
+        if intensity is not None:
+            nff_outputs["intensity"] = intensity
+        if ray_drop_logits is not None:
+            nff_outputs["ray_drop_logits"] = ray_drop_logits
+        return nff_outputs
+
+    def decode_features(
+        self,
+        features: Tensor,
+        patch_size: Tuple[int, int],
+        is_lidar: Optional[Tensor] = None,
+        intensity_for_cam: bool = False,
+    ) -> Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor]]:
+        """Take the neural feature field feature, and render them to rgb and/or intensity."""
+        if is_lidar is None:
+            lidar_features, cam_features = torch.empty(0), features
+        else:
+            lidar_features, cam_features = features[is_lidar[..., 0]], features[~is_lidar[..., 0]]
+
+        # Decode lidar features
+        if intensity_for_cam:  # Useful for visualization
+            intensity, ray_drop_logit = self.lidar_decoder(features).float().split(1, dim=-1)  # TODO: hmm
+        elif lidar_features.numel() > 0:
+            intensity, ray_drop_logit = self.lidar_decoder(lidar_features).split(1, dim=-1)
+        else:
+            intensity, ray_drop_logit = None, None
+        intensity = intensity.sigmoid() if intensity is not None else None
+
+        if cam_features.numel() == 0:
+            rgb = None
+        # need to reshape the ray features to apply the cnn decoder
+        cam_feature_patches = cam_features.view(-1, *patch_size, cam_features.shape[-1])  # B x D x D x C
+        cam_feature_patches = cam_feature_patches.permute(0, 3, 1, 2)  # B x C x D x D
+        rgb = self.rgb_decoder(cam_feature_patches)  # B x 3 x upsample x upsample
+        rgb = rgb.permute(0, 2, 3, 1)  # B x upsample x upsample x 3
+        return rgb, intensity, ray_drop_logit
+
+    def get_nff_outputs(self, ray_bundle: RayBundle, calc_lidar_losses: bool = False) -> Dict[str, Tensor]:
+        """Run the neural feature field, and return the rendered outputs."""
+        self._scale_pixel_area(ray_bundle)
+        ray_samples, proposal_ray_samples, proposal_weights = self._get_ray_samples(ray_bundle)
+
+        outputs = self.field(ray_samples)
+
+        # Render rays
+        nff_outputs = {}
+        weights = self._render_weights(outputs, ray_samples)
+        accumulation = self.renderer_accumulation(weights=weights[..., None])
+
+        # Put remaining accumulation on last (sky) sample and render features
+        weights = torch.cat((weights[..., :-1], weights[..., -1:] + 1 - accumulation), dim=-1).unsqueeze(-1)
+        features = self.renderer_feat(features=outputs[FieldHeadNames.FEATURE], weights=weights)
+        if self.config.appearance_dim > 0:  # add appearance embedding
+            appearance = self._get_appearance_embedding(ray_bundle, features)
+            features = torch.cat([features, appearance], dim=-1)
+
+        # Discard sky sample for all remaining purposes
+        weights, ray_samples = weights[..., :-1, :], ray_samples[..., :-1]
+        outputs = {k: v[..., :-1, :] for k, v in outputs.items()}
+        depth = self.renderer_depth(weights=weights, ray_samples=ray_samples)
+        nff_outputs = {
+            "features": features,
+            "depth": depth,
+            "accumulation": accumulation,
+        }
+        if not self.training and FieldHeadNames.NORMALS in outputs:
+            nff_outputs["normals"] = self.renderer_normals(normals=outputs[FieldHeadNames.NORMALS], weights=weights)
+
+        # Handle proposal outputs
+        for i, (prop_w, prop_rs) in enumerate(zip(proposal_weights, proposal_ray_samples)):
+            nff_outputs[f"prop_depth_{i}"] = self.renderer_depth(prop_w, prop_rs)
+            if self.training and calc_lidar_losses:
+                weights_mask = (~prop_rs.metadata["is_close_to_lidar"]) & prop_rs.metadata["is_lidar"]
+                nff_outputs[f"prop_weights_loss_{i}"] = ((prop_w * weights_mask) ** 2).sum()
+        if self.training:
+            # Need to render the static samples separately for proposal supervision
+            nff_outputs["weights_list"] = proposal_weights + [weights]
+            nff_outputs["ray_samples_list"] = proposal_ray_samples + [ray_samples]
+
+        if self.training and calc_lidar_losses:
+            assert (metadata := ray_samples.metadata) is not None
+            weights_mask = ((~metadata["is_close_to_lidar"]) & metadata["is_lidar"]).squeeze(-1)
+            weights_idx = weights_mask.nonzero(as_tuple=True)
+            nff_outputs["non_nearby_weights"] = weights[weights_idx]
+            # TODO: get rid of this
+            ray_indices = torch.arange(weights.shape[0], device=weights.device).unsqueeze(-1)
+            ray_indices = ray_indices.repeat(1, *weights.shape[1:-1])
+            lidar_start_ray = ray_bundle.metadata["is_lidar"].int().argmax()  # argmax gives first True
+            nff_outputs["non_nearby_lidar_ray_indices"] = ray_indices[weights_idx] - lidar_start_ray
+
+        return nff_outputs
+
+    def _get_appearance_embedding(self, ray_bundle, features):
+        sensor_idx = ray_bundle.metadata.get("sensor_idxs")
+        if sensor_idx is None:
+            assert not self.training, "Sensor sensor_idx must be present in metadata during training"
+            sensor_idx = torch.full_like(features[..., :1], self.fallback_sensor_idx.value, dtype=torch.long)
+
+        if self.config.use_temporal_appearance:
+            time_idx = ray_bundle.times / self._duration * (embd_per_sensor := self._num_embeds_per_sensor)
+            before_idx = time_idx.floor().clamp(0, embd_per_sensor - 1)
+            after_idx = (before_idx + 1).clamp(0, embd_per_sensor - 1)
+            ratio = time_idx - before_idx
+            # unwrap to true embedding indices, which also account for the sensor index, not just the time index
+            before_idx, after_idx = (x + sensor_idx * embd_per_sensor for x in (before_idx, after_idx))
+            before_embed = self.appearance_embedding(before_idx.squeeze(-1).long())
+            after_embed = self.appearance_embedding(after_idx.squeeze(-1).long())
+            embed = before_embed * (1 - ratio) + after_embed * ratio
+        else:
+            embed = self.appearance_embedding(sensor_idx.squeeze(-1))
+        return embed
+
+    def _get_ray_samples(self, ray_bundle: RayBundle) -> tuple[RaySamples, List[RaySamples], List[Tensor]]:
+        # Sampling
+        if ray_bundle.fars is not None:
+            ray_bundle.fars.clamp_max_(sky_distance := self.config.sampling.sky_distance)
+        else:
+            ray_bundle.fars = torch.full_like(ray_bundle.pixel_area, sky_distance := self.config.sampling.sky_distance)
+        ray_bundle.nears = ray_bundle.nears if ray_bundle.nears is not None else torch.zeros_like(ray_bundle.fars)
+        ray_samples, prop_weights, prop_ray_samples = self.sampler(ray_bundle, self.density_fns, pass_ray_samples=True)
+        # Efficient "sky-field" by setting last sample to end extremely far away
+        dist_to_sky = sky_distance - ray_samples.frustums.ends[..., -1, 0]
+        ray_samples.frustums.ends[..., -1, 0] += dist_to_sky
+        ray_samples.deltas[..., -1, 0] += dist_to_sky
+        ray_samples.spacing_ends[..., -1, 0] = 1 - EPS  # Hacky, but sky is ish at infinity
+
+        if self.training and "is_lidar" in ray_bundle.metadata:
+            self._compute_is_close_to_lidar(ray_samples, *prop_ray_samples)
+        return ray_samples, prop_ray_samples, prop_weights
+
+    def get_metrics_dict(self, outputs, batch):
+        metrics_dict = {}
+        if "image" in batch:
+            image, rgb = batch["image"].to(self.device), outputs["rgb"]
+            metrics_dict["psnr"] = self.psnr(rgb.detach(), image)
+        if "lidar" in batch:
+            is_lidar = batch["is_lidar"][:, 0].to(self.device)
+            n_lidar_rays = is_lidar.sum()
+            did_return = batch["did_return"][batch["is_lidar"].squeeze(-1)].squeeze(-1).to(self.device)
+            points_intensities = batch["lidar"][..., 3:4].to(self.device)
+            termination_depth = batch["distance"].to(self.device)
+
+            pred_depth = outputs["depth"][is_lidar]
+            ray_drop_logits = outputs["ray_drop_logits"]
+            pred_intensity = outputs["intensity"]
+
+            # eval metrics
+            metrics_dict["depth_median_l2"] = self.median_l2(pred_depth[did_return], termination_depth[did_return])
+            metrics_dict["depth_mean_rel_l2"] = self.mean_rel_l2(pred_depth[did_return], termination_depth[did_return])
+            metrics_dict["intensity_rmse"] = self.rmse(pred_intensity[did_return], points_intensities[did_return])
+            metrics_dict["ray_drop_accuracy"] = (
+                ((ray_drop_logits.sigmoid() > 0.5).squeeze(-1) == ~did_return).float().mean()
+            )
+
+            # train metrics / losses
+            if self.training:
+                # Adjust target depth for non-returning rays
+                nonret_lid_dist = torch.tensor(
+                    self.config.loss.non_return_lidar_distance, device=termination_depth.device
+                )
+                target_depth = termination_depth.clone()
+                target_depth[~did_return] = pred_depth.detach()[~did_return].maximum(nonret_lid_dist)
+                unreduced_depth_loss = self.depth_loss(target_depth, pred_depth)
+                unreduced_depth_loss[~did_return] *= self.config.loss.non_return_loss_mult
+                # TODO: get rid of quantile mask
+                quantile = torch.quantile(unreduced_depth_loss, self.config.loss.quantile_threshold)
+                quantile_mask = (unreduced_depth_loss < quantile).squeeze(-1)
+
+                metrics_dict["depth_loss"] = torch.mean(unreduced_depth_loss[quantile_mask])
+
+                quant_and_return = quantile_mask & did_return
+                metrics_dict["intensity_loss"] = self.intensity_loss(
+                    points_intensities[quant_and_return], outputs["intensity"][quant_and_return]
+                ).mean()
+
+                metrics_dict["ray_drop_loss"] = self.ray_drop_loss(
+                    ray_drop_logits, (~did_return).unsqueeze(-1).to(ray_drop_logits)
+                )
+                # quantile_weights_mask = quantile_mask[outputs["non_nearby_lidar_ray_indices"]].squeeze(-1)
+                weights_loss = (outputs["non_nearby_weights"] ** 2).sum()
+                metrics_dict["carving_loss"] = weights_loss / n_lidar_rays  # avg per ray
+
+                # Lidar proposal losses
+                for prop_i in range(self.config.num_proposal_rounds):
+                    pred_depth = outputs[f"prop_depth_{prop_i}"][is_lidar]
+                    target_depth = termination_depth.clone()
+                    target_depth[~did_return] = pred_depth.detach()[~did_return].maximum(nonret_lid_dist)
+                    unreduced_depth_loss = self.depth_loss(target_depth, pred_depth)
+                    unreduced_depth_loss[~did_return] *= self.config.loss.non_return_loss_mult
+                    metrics_dict[f"depth_loss_{prop_i}"] = torch.mean(unreduced_depth_loss)
+                    metrics_dict[f"carving_loss_{prop_i}"] = outputs[f"prop_weights_loss_{prop_i}"] / n_lidar_rays
+
+        if self.training and "weights_list" in outputs:
+            metrics_dict["distortion"] = distortion_loss(outputs["weights_list"], outputs["ray_samples_list"])
+
+        if self.config.field.use_sdf:
+            metrics_dict["sdf_to_density"] = float(self.field.sdf_to_density.beta)
+        self.camera_optimizer.get_metrics_dict(metrics_dict)
+        return metrics_dict
+
+    def get_loss_dict(self, outputs, batch, metrics_dict=None):
+        conf = self.config.loss
+        loss_dict = {}
+        if "image" in batch:
+            image, rgb = batch["image"].to(self.device), outputs["rgb"]
+            loss_dict["rgb_loss"] = self.rgb_loss(image, rgb) * conf.rgb_mult
+            if conf.vgg_mult > 0.0:
+                loss_dict["vgg_loss"] = self.vgg_loss(rgb, image) * conf.vgg_mult
+        if self.training:
+            if "weights_list" in outputs:
+                loss_dict["interlevel_loss"] = self.config.loss.interlevel_loss_mult * self.interlevel_loss(
+                    outputs["weights_list"], outputs["ray_samples_list"]
+                )
+                assert metrics_dict is not None and "distortion" in metrics_dict
+                loss_dict["distortion_loss"] = self.config.loss.distortion_loss_mult * metrics_dict["distortion"]
+                prop_depth_mult = conf.prop_lidar_loss_mult * conf.depth_mult
+                prop_carv_mult = conf.prop_lidar_loss_mult * conf.carving_mult
+                for i_prop in range(self.config.num_proposal_rounds):
+                    loss_dict[f"depth_loss_{i_prop}"] = prop_depth_mult * metrics_dict[f"depth_loss_{i_prop}"]
+                    loss_dict[f"carving_loss_{i_prop}"] = prop_carv_mult * metrics_dict[f"carving_loss_{i_prop}"]
+            assert metrics_dict
+            if "depth_loss" in metrics_dict:
+                loss_dict["depth_loss"] = conf.depth_mult * metrics_dict["depth_loss"]
+            if "intensity_loss" in metrics_dict:
+                loss_dict["intensity_loss"] = conf.intensity_mult * metrics_dict["intensity_loss"]
+            if "carving_loss" in metrics_dict:
+                loss_dict["carving_loss"] = conf.carving_mult * metrics_dict["carving_loss"]
+            if "ray_drop_loss" in metrics_dict:
+                loss_dict["ray_drop_loss"] = conf.ray_drop_loss_mult * metrics_dict["ray_drop_loss"]
+            self.camera_optimizer.get_loss_dict(loss_dict)
+        return loss_dict
+
+    def get_image_metrics_and_images(
+        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
+    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
+        metrics_dict = {}
+        images_dict = {}
+        if "image" in batch:
+            image, rgb = batch["image"].to(self.device), outputs["rgb"]
+            images_dict["img"] = torch.cat([image, rgb], dim=1)
+            images_dict["depth"] = colormaps.apply_depth_colormap(outputs["depth"])
+            if self.config.verbose:
+                images_dict["accumulation"] = colormaps.apply_colormap(outputs["accumulation"])
+                # Add proposal depthmaps
+                for i in range(self.config.num_proposal_rounds):
+                    key = f"prop_depth_{i}"
+                    prop_depth_i = colormaps.apply_depth_colormap(outputs[key])
+                    images_dict[key] = prop_depth_i
+
+            # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
+            image = torch.moveaxis(image, -1, 0)[None, ...]
+            rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
+
+            # all of these metrics will be logged as scalars
+            metrics_dict["psnr"] = float(self.psnr(image, rgb))
+            metrics_dict["ssim"] = float(self.ssim(image, rgb))  # type: ignore
+            metrics_dict["lpips"] = float(self.lpips(image, rgb))
+
+        if "lidar" in batch:
+            points = batch["lidar"].to(self.device)
+            if "is_lidar" not in batch:
+                batch["is_lidar"] = torch.ones(*batch["lidar"].shape[:-1], 1, dtype=torch.bool, device=self.device)
+            if "did_return" not in batch:
+                batch["did_return"] = torch.ones(*batch["lidar"].shape[:-1], 1, dtype=torch.bool, device=self.device)
+
+            ray_drop_logits = outputs["ray_drop_logits"]
+            pred_depth = outputs["depth"]
+            did_return = batch["did_return"][:, 0].to(self.device)
+            is_lidar = batch["is_lidar"][:, 0].to(self.device)
+            metrics_dict["depth_median_l2"] = float(
+                self.median_l2(pred_depth[is_lidar][did_return], batch["distance"][did_return])
+            )
+            metrics_dict["depth_mean_rel_l2"] = float(
+                self.mean_rel_l2(pred_depth[is_lidar][did_return], batch["distance"][did_return])
+            )
+            metrics_dict["intensity_rmse"] = float(self.rmse(outputs["intensity"][did_return], points[did_return, 3:4]))
+            metrics_dict["ray_drop_accuracy"] = float(
+                ((ray_drop_logits.sigmoid() > 0.5).squeeze(-1) == ~did_return).float().mean()
+            )
+            if self.config.loss.ray_drop_loss_mult > 0.0:
+                pred_points_did_return = (ray_drop_logits.sigmoid() < 0.5).squeeze(-1)
+            else:
+                pred_points_did_return = (pred_depth < self.config.loss.non_return_lidar_distance).squeeze(-1)
+            if pred_points_did_return.any() and points.shape[0] > 0 and did_return.any():
+                pred_points = outputs["points"][is_lidar][pred_points_did_return]
+                metrics_dict["chamfer_distance"] = float(
+                    self.chamfer_distance(pred_points[..., :3], points[did_return, :3])
+                )
+            else:
+                metrics_dict["chamfer_distance"] = points[did_return, :3].norm(dim=-1).mean()
+        return metrics_dict, images_dict
+
+    @torch.no_grad()
+    def get_outputs_for_camera_ray_bundle(self, camera_ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:
+        """Takes in camera parameters and computes the output of the model.
+
+        Args:
+            camera_ray_bundle: ray bundle to calculate outputs over
+        """
+        if len(camera_ray_bundle.shape) == 1:  # lidar
+            output_size, patch_size = (camera_ray_bundle.shape[0],), (1, 1)
+            is_lidar = torch.ones_like(camera_ray_bundle.pixel_area, dtype=torch.bool)
+        else:  # camera
+            assert len(camera_ray_bundle.shape) == 2, "Raybundle should be 2d (an image/patch)"
+            if self.config.compensate_upsampling_when_rendering:
+                # shoot rays at a lower resolution and upsample the output to the target resolution
+                step = self.config.rgb_upsample_factor
+                camera_ray_bundle = camera_ray_bundle[step // 2 :: step, step // 2 :: step]
+            output_size = patch_size = (camera_ray_bundle.shape[0], camera_ray_bundle.shape[1])
+            camera_ray_bundle = camera_ray_bundle.reshape((-1,))
+            is_lidar = None
+
+        # Run chunked forward pass through NFF only
+        num_rays_per_chunk = self.config.eval_num_rays_per_chunk
+        num_rays = len(camera_ray_bundle)
+        outputs_lists = defaultdict(list)
+        for i in range(0, num_rays, num_rays_per_chunk):
+            start_idx = i
+            end_idx = i + num_rays_per_chunk
+            ray_bundle = camera_ray_bundle.get_row_major_sliced_ray_bundle(start_idx, end_idx)
+            outputs = self.get_nff_outputs(ray_bundle, calc_lidar_losses=False)
+            for output_name, output in outputs.items():  # type: ignore
+                outputs_lists[output_name].append(output)
+        outputs = {}
+        for output_name, outputs_list in outputs_lists.items():
+            outputs[output_name] = torch.cat(outputs_list).view(*output_size, -1)  # type: ignore
+
+        features = outputs["features"].view(-1, outputs["features"].shape[-1])
+        rgb, intensity, ray_drop_logit = self.decode_features(
+            features, patch_size=patch_size, is_lidar=is_lidar, intensity_for_cam=True
+        )
+        if rgb is not None:
+            outputs["rgb"] = rgb.squeeze(0)
+        if intensity is not None:
+            outputs["intensity"] = intensity.view(*output_size, -1)
+        if ray_drop_logit is not None:
+            outputs["ray_drop_logits"] = ray_drop_logit.view(*output_size, -1)
+            outputs["ray_drop_prob"] = ray_drop_logit.view(*output_size, -1).sigmoid()
+        return outputs
+
+    def _compute_is_close_to_lidar(self, *all_ray_samples: RaySamples) -> None:
+        """Compute which rays are close to the lidar."""
+        for ray_samples in all_ray_samples:
+            if ray_samples is None:
+                continue
+            assert ray_samples.metadata is not None
+            metadata, frustums = ray_samples.metadata, ray_samples.frustums
+            sample_distance = (frustums.starts + frustums.ends) * 0.5
+            mask = metadata["is_lidar"].clone()  # handle that is_lidar is expanded
+            idx = mask.nonzero(as_tuple=True)
+            sample_distance = sample_distance[idx]
+            # directions_norm, in case of lidar, is the distance
+            dist = metadata["directions_norm"][idx] - sample_distance
+            # same as mask[mask] since it will write to itself in place and break
+            close_to_hit = dist.abs() < self.config.loss.carving_epsilon
+            if "did_return" in metadata.keys():
+                did_return = metadata["did_return"][idx]
+                in_lidar_range = sample_distance < self.config.loss.non_return_lidar_distance
+                mask[idx] = (did_return & close_to_hit) | (
+                    (~did_return) & in_lidar_range
+                )  # TODO: should this be in_range or out_of_range?
+            else:
+                mask[idx] = close_to_hit
+            metadata["is_close_to_lidar"] = mask
+
+    def _scale_pixel_area(self, ray_bundle: RayBundle):
+        is_lidar = ray_bundle.metadata.get("is_lidar")
+        scaling = torch.ones_like(ray_bundle.pixel_area)
+        if is_lidar is not None:
+            scaling[~is_lidar] = self.config.rgb_upsample_factor**2
+        else:
+            scaling = self.config.rgb_upsample_factor**2
+        ray_bundle.pixel_area = ray_bundle.pixel_area * scaling
+
+    def _render_weights(self, outputs, ray_samples):
+        value = outputs[FieldHeadNames.ALPHA if self.config.field.use_sdf else FieldHeadNames.DENSITY].squeeze(-1)
+        if self.device.type in ("cpu", "mps"):
+            # Note: for debugging on devices without cuda
+            weights = torch.zeros_like(value) + 0.5
+        elif self.config.field.use_sdf:
+            weights, _ = nerfacc.render_weight_from_alpha(value)
+        else:
+            weights, _, _ = nerfacc.render_weight_from_density(
+                t_ends=ray_samples.frustums.ends.squeeze(-1),
+                t_starts=ray_samples.frustums.starts.squeeze(-1),
+                sigmas=value,
+            )
+        return weights
+
+
+def render_depth_simple(
+    weights: Float[Tensor, "*batch num_samples 1"],
+    ray_samples: RaySamples,
+    ray_indices: Optional[Int[Tensor, "num_samples"]] = None,
+    num_rays: Optional[int] = None,
+) -> Float[Tensor, "*batch 1"]:
+    steps = (ray_samples.frustums.starts + ray_samples.frustums.ends) / 2
+    return nerfacc.accumulate_along_rays(weights[..., 0], values=steps, ray_indices=ray_indices, n_rays=num_rays)
diff --git a/nerfstudio/models/neus.py b/nerfstudio/models/neus.py
deleted file mode 100644
index d754c63b..00000000
--- a/nerfstudio/models/neus.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Implementation of NeuS.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Type
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.model_components.ray_samplers import NeuSSampler
-from nerfstudio.models.base_surface_model import SurfaceModel, SurfaceModelConfig
-
-
-@dataclass
-class NeuSModelConfig(SurfaceModelConfig):
-    """NeuS Model Config"""
-
-    _target: Type = field(default_factory=lambda: NeuSModel)
-    num_samples: int = 64
-    """Number of uniform samples"""
-    num_samples_importance: int = 64
-    """Number of importance samples"""
-    num_up_sample_steps: int = 4
-    """number of up sample step, 1 for simple coarse-to-fine sampling"""
-    base_variance: float = 64
-    """fixed base variance in NeuS sampler, the inv_s will be base * 2 ** iter during upsample"""
-    perturb: bool = True
-    """use to use perturb for the sampled points"""
-
-
-class NeuSModel(SurfaceModel):
-    """NeuS model
-
-    Args:
-        config: NeuS configuration to instantiate model
-    """
-
-    config: NeuSModelConfig
-
-    def populate_modules(self):
-        """Set the fields and modules."""
-        super().populate_modules()
-
-        self.sampler = NeuSSampler(
-            num_samples=self.config.num_samples,
-            num_samples_importance=self.config.num_samples_importance,
-            num_samples_outside=self.config.num_samples_outside,
-            num_upsample_steps=self.config.num_up_sample_steps,
-            base_variance=self.config.base_variance,
-        )
-
-        self.anneal_end = 50000
-
-    def get_training_callbacks(
-        self, training_callback_attributes: TrainingCallbackAttributes
-    ) -> List[TrainingCallback]:
-        callbacks = []
-        # anneal for cos in NeuS
-        if self.anneal_end > 0:
-
-            def set_anneal(step):
-                anneal = min([1.0, step / self.anneal_end])
-                self.field.set_cos_anneal_ratio(anneal)
-
-            callbacks.append(
-                TrainingCallback(
-                    where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
-                    update_every_num_iters=1,
-                    func=set_anneal,
-                )
-            )
-
-        return callbacks
-
-    def sample_and_forward_field(self, ray_bundle: RayBundle) -> Dict:
-        ray_samples = self.sampler(ray_bundle, sdf_fn=self.field.get_sdf)
-        field_outputs = self.field(ray_samples, return_alphas=True)
-        weights, transmittance = ray_samples.get_weights_and_transmittance_from_alphas(
-            field_outputs[FieldHeadNames.ALPHA]
-        )
-        bg_transmittance = transmittance[:, -1, :]
-
-        samples_and_field_outputs = {
-            "ray_samples": ray_samples,
-            "field_outputs": field_outputs,
-            "weights": weights,
-            "bg_transmittance": bg_transmittance,
-        }
-        return samples_and_field_outputs
-
-    def get_metrics_dict(self, outputs, batch) -> Dict:
-        metrics_dict = super().get_metrics_dict(outputs, batch)
-        if self.training:
-            # training statics
-            metrics_dict["s_val"] = self.field.deviation_network.get_variance().item()
-            metrics_dict["inv_s"] = 1.0 / self.field.deviation_network.get_variance().item()
-
-        return metrics_dict
diff --git a/nerfstudio/models/neus_facto.py b/nerfstudio/models/neus_facto.py
deleted file mode 100644
index 39e4e7fe..00000000
--- a/nerfstudio/models/neus_facto.py
+++ /dev/null
@@ -1,219 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Implementation of NeuS similar to nerfacto where proposal sampler is used.
-Based on SDFStudio https://github.com/autonomousvision/sdfstudio/
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Tuple, Type
-
-import numpy as np
-import torch
-from torch.nn import Parameter
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.fields.density_fields import HashMLPDensityField
-from nerfstudio.model_components.losses import interlevel_loss
-from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler, UniformSampler
-from nerfstudio.models.neus import NeuSModel, NeuSModelConfig
-from nerfstudio.utils import colormaps
-
-
-@dataclass
-class NeuSFactoModelConfig(NeuSModelConfig):
-    """NeusFacto Model Config"""
-
-    _target: Type = field(default_factory=lambda: NeuSFactoModel)
-    num_proposal_samples_per_ray: Tuple[int, ...] = (256, 96)
-    """Number of samples per ray for the proposal network."""
-    num_neus_samples_per_ray: int = 48
-    """Number of samples per ray for the nerf network."""
-    proposal_update_every: int = 5
-    """Sample every n steps after the warmup"""
-    proposal_warmup: int = 5000
-    """Scales n from 1 to proposal_update_every over this many steps"""
-    num_proposal_iterations: int = 2
-    """Number of proposal network iterations."""
-    use_same_proposal_network: bool = False
-    """Use the same proposal network. Otherwise use different ones."""
-    proposal_net_args_list: List[Dict] = field(
-        default_factory=lambda: [
-            {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 64},
-            {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 256},
-        ]
-    )
-    """Arguments for the proposal density fields."""
-    interlevel_loss_mult: float = 1.0
-    """Proposal loss multiplier."""
-    use_proposal_weight_anneal: bool = True
-    """Whether to use proposal weight annealing."""
-    proposal_weights_anneal_slope: float = 10.0
-    """Slope of the annealing function for the proposal weights."""
-    proposal_weights_anneal_max_num_iters: int = 1000
-    """Max num iterations for the annealing function."""
-    use_single_jitter: bool = True
-    """Whether use single jitter or not for the proposal networks."""
-
-
-class NeuSFactoModel(NeuSModel):
-    """NeuSFactoModel extends NeuSModel for a more efficient sampling strategy.
-
-    The model improves the rendering speed and quality by incorporating a learning-based
-    proposal distribution to guide the sampling process.(similar to mipnerf-360)
-
-    Args:
-        config: NeuS configuration to instantiate model
-    """
-
-    config: NeuSFactoModelConfig
-
-    def populate_modules(self):
-        """Instantiate modules and fields, including proposal networks."""
-        super().populate_modules()
-
-        self.density_fns = []
-        num_prop_nets = self.config.num_proposal_iterations
-        # Build the proposal network(s)
-        self.proposal_networks = torch.nn.ModuleList()
-        if self.config.use_same_proposal_network:
-            assert len(self.config.proposal_net_args_list) == 1, "Only one proposal network is allowed."
-            prop_net_args = self.config.proposal_net_args_list[0]
-            network = HashMLPDensityField(
-                self.scene_box.aabb, spatial_distortion=self.scene_contraction, **prop_net_args
-            )
-            self.proposal_networks.append(network)
-            self.density_fns.extend([network.density_fn for _ in range(num_prop_nets)])
-        else:
-            for i in range(num_prop_nets):
-                prop_net_args = self.config.proposal_net_args_list[min(i, len(self.config.proposal_net_args_list) - 1)]
-                network = HashMLPDensityField(
-                    self.scene_box.aabb,
-                    spatial_distortion=self.scene_contraction,
-                    **prop_net_args,
-                )
-                self.proposal_networks.append(network)
-            self.density_fns.extend([network.density_fn for network in self.proposal_networks])
-
-        # update proposal network every iterations
-        def update_schedule(_):
-            return -1
-
-        initial_sampler = UniformSampler(single_jitter=self.config.use_single_jitter)
-        self.proposal_sampler = ProposalNetworkSampler(
-            num_nerf_samples_per_ray=self.config.num_neus_samples_per_ray,
-            num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
-            num_proposal_network_iterations=self.config.num_proposal_iterations,
-            single_jitter=self.config.use_single_jitter,
-            update_sched=update_schedule,
-            initial_sampler=initial_sampler,
-        )
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        """Return a dictionary with the parameters of the proposal networks."""
-        param_groups = super().get_param_groups()
-        param_groups["proposal_networks"] = list(self.proposal_networks.parameters())
-        return param_groups
-
-    def get_training_callbacks(
-        self, training_callback_attributes: TrainingCallbackAttributes
-    ) -> List[TrainingCallback]:
-        callbacks = super().get_training_callbacks(training_callback_attributes)
-
-        if self.config.use_proposal_weight_anneal:
-            # anneal the weights of the proposal network before doing PDF sampling
-            N = self.config.proposal_weights_anneal_max_num_iters
-
-            def set_anneal(step: int):
-                # https://arxiv.org/pdf/2111.12077.pdf eq. 18
-                train_frac = np.clip(step / N, 0, 1)
-
-                def bias(x, b):
-                    return b * x / ((b - 1) * x + 1)
-
-                anneal = bias(train_frac, self.config.proposal_weights_anneal_slope)
-                self.proposal_sampler.set_anneal(anneal)
-
-            callbacks.append(
-                TrainingCallback(
-                    where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
-                    update_every_num_iters=1,
-                    func=set_anneal,
-                )
-            )
-            callbacks.append(
-                TrainingCallback(
-                    where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                    update_every_num_iters=1,
-                    func=self.proposal_sampler.step_cb,
-                )
-            )
-
-        return callbacks
-
-    def sample_and_forward_field(self, ray_bundle: RayBundle) -> Dict[str, Any]:
-        """Sample rays using proposal networks and compute the corresponding field outputs."""
-        ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
-
-        field_outputs = self.field(ray_samples, return_alphas=True)
-        weights, transmittance = ray_samples.get_weights_and_transmittance_from_alphas(
-            field_outputs[FieldHeadNames.ALPHA]
-        )
-        bg_transmittance = transmittance[:, -1, :]
-
-        weights_list.append(weights)
-        ray_samples_list.append(ray_samples)
-
-        samples_and_field_outputs = {
-            "ray_samples": ray_samples,
-            "field_outputs": field_outputs,
-            "weights": weights,
-            "bg_transmittance": bg_transmittance,
-            "weights_list": weights_list,
-            "ray_samples_list": ray_samples_list,
-        }
-        return samples_and_field_outputs
-
-    def get_loss_dict(
-        self, outputs: Dict[str, Any], batch: Dict[str, Any], metrics_dict: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
-        """Compute the loss dictionary, including interlevel loss for proposal networks."""
-        loss_dict = super().get_loss_dict(outputs, batch, metrics_dict)
-
-        if self.training:
-            loss_dict["interlevel_loss"] = self.config.interlevel_loss_mult * interlevel_loss(
-                outputs["weights_list"], outputs["ray_samples_list"]
-            )
-
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, Any], batch: Dict[str, Any]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        """Compute image metrics and images, including the proposal depth for each iteration."""
-        metrics_dict, images_dict = super().get_image_metrics_and_images(outputs, batch)
-        for i in range(self.config.num_proposal_iterations):
-            key = f"prop_depth_{i}"
-            prop_depth_i = colormaps.apply_depth_colormap(
-                outputs[key],
-                accumulation=outputs["accumulation"],
-            )
-            images_dict[key] = prop_depth_i
-
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/semantic_nerfw.py b/nerfstudio/models/semantic_nerfw.py
deleted file mode 100644
index f3dff767..00000000
--- a/nerfstudio/models/semantic_nerfw.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Semantic NeRF-W implementation which should be fast enough to view in the viewer.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Tuple, Type
-
-import numpy as np
-import torch
-from torch.nn import Parameter
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.data.dataparsers.base_dataparser import Semantics
-from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.field_components.spatial_distortions import SceneContraction
-from nerfstudio.fields.density_fields import HashMLPDensityField
-from nerfstudio.fields.nerfacto_field import NerfactoField
-from nerfstudio.model_components.losses import MSELoss, distortion_loss, interlevel_loss
-from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler
-from nerfstudio.model_components.renderers import (
-    AccumulationRenderer,
-    DepthRenderer,
-    RGBRenderer,
-    SemanticRenderer,
-    UncertaintyRenderer,
-)
-from nerfstudio.model_components.scene_colliders import NearFarCollider
-from nerfstudio.models.base_model import Model
-from nerfstudio.models.nerfacto import NerfactoModelConfig
-from nerfstudio.utils import colormaps
-
-
-@dataclass
-class SemanticNerfWModelConfig(NerfactoModelConfig):
-    """Nerfacto Model Config"""
-
-    _target: Type = field(default_factory=lambda: SemanticNerfWModel)
-    use_transient_embedding: bool = False
-    """Whether to use transient embedding."""
-    semantic_loss_weight: float = 1.0
-    pass_semantic_gradients: bool = False
-
-
-class SemanticNerfWModel(Model):
-    """Nerfacto model
-
-    Args:
-        config: Nerfacto configuration to instantiate model
-    """
-
-    config: SemanticNerfWModelConfig
-
-    def __init__(self, config: SemanticNerfWModelConfig, metadata: Dict, **kwargs) -> None:
-        assert "semantics" in metadata.keys() and isinstance(metadata["semantics"], Semantics)
-        self.semantics = metadata["semantics"]
-        super().__init__(config=config, **kwargs)
-        self.colormap = self.semantics.colors.clone().detach().to(self.device)
-
-    def populate_modules(self):
-        """Set the fields and modules."""
-        super().populate_modules()
-
-        scene_contraction = SceneContraction(order=float("inf"))
-
-        if self.config.use_transient_embedding:
-            raise ValueError("Transient embedding is not fully working for semantic nerf-w.")
-
-        # Fields
-        self.field = NerfactoField(
-            self.scene_box.aabb,
-            num_levels=self.config.num_levels,
-            max_res=self.config.max_res,
-            log2_hashmap_size=self.config.log2_hashmap_size,
-            spatial_distortion=scene_contraction,
-            num_images=self.num_train_data,
-            use_average_appearance_embedding=self.config.use_average_appearance_embedding,
-            use_transient_embedding=self.config.use_transient_embedding,
-            use_semantics=True,
-            num_semantic_classes=len(self.semantics.classes),
-            pass_semantic_gradients=self.config.pass_semantic_gradients,
-        )
-
-        # Build the proposal network(s)
-        self.proposal_networks = torch.nn.ModuleList()
-        if self.config.use_same_proposal_network:
-            network = HashMLPDensityField(self.scene_box.aabb, spatial_distortion=scene_contraction)
-            self.proposal_networks.append(network)
-            self.density_fns = [network.density_fn for _ in range(self.config.num_proposal_iterations)]
-        else:
-            for _ in range(self.config.num_proposal_iterations):
-                network = HashMLPDensityField(self.scene_box.aabb, spatial_distortion=scene_contraction)
-                self.proposal_networks.append(network)
-            self.density_fns = [network.density_fn for network in self.proposal_networks]
-
-        # Collider
-        self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
-
-        # Samplers
-        self.proposal_sampler = ProposalNetworkSampler(
-            num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
-            num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
-            num_proposal_network_iterations=self.config.num_proposal_iterations,
-            single_jitter=self.config.use_single_jitter,
-        )
-
-        # renderers
-        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer()
-        self.renderer_uncertainty = UncertaintyRenderer()
-        self.renderer_semantics = SemanticRenderer()
-
-        # losses
-        self.rgb_loss = MSELoss()
-        self.cross_entropy_loss = torch.nn.CrossEntropyLoss(reduction="mean")
-
-        # metrics
-        from torchmetrics.functional import structural_similarity_index_measure
-        from torchmetrics.image import PeakSignalNoiseRatio
-        from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-
-        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
-        self.ssim = structural_similarity_index_measure
-        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-        param_groups["proposal_networks"] = list(self.proposal_networks.parameters())
-        param_groups["fields"] = list(self.field.parameters())
-        return param_groups
-
-    def get_training_callbacks(
-        self, training_callback_attributes: TrainingCallbackAttributes
-    ) -> List[TrainingCallback]:
-        callbacks = []
-        if self.config.use_proposal_weight_anneal:
-            # anneal the weights of the proposal network before doing PDF sampling
-            N = self.config.proposal_weights_anneal_max_num_iters
-
-            def set_anneal(step):
-                # https://arxiv.org/pdf/2111.12077.pdf eq. 18
-                train_frac = np.clip(step / N, 0, 1)
-
-                def bias(x, b):
-                    return b * x / ((b - 1) * x + 1)
-
-                anneal = bias(train_frac, self.config.proposal_weights_anneal_slope)
-                self.proposal_sampler.set_anneal(anneal)
-
-            callbacks.append(
-                TrainingCallback(
-                    where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
-                    update_every_num_iters=1,
-                    func=set_anneal,
-                )
-            )
-        return callbacks
-
-    def get_outputs(self, ray_bundle: RayBundle):
-        ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
-        field_outputs = self.field(ray_samples)
-
-        if self.training and self.config.use_transient_embedding:
-            density = field_outputs[FieldHeadNames.DENSITY] + field_outputs[FieldHeadNames.TRANSIENT_DENSITY]
-            weights = ray_samples.get_weights(density)
-            weights_static = ray_samples.get_weights(field_outputs[FieldHeadNames.DENSITY])
-            rgb_static_component = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.RGB], weights=weights)
-            rgb_transient_component = self.renderer_rgb(
-                rgb=field_outputs[FieldHeadNames.TRANSIENT_RGB], weights=weights
-            )
-            rgb = rgb_static_component + rgb_transient_component
-        else:
-            weights_static = ray_samples.get_weights(field_outputs[FieldHeadNames.DENSITY])
-            weights = weights_static
-            rgb = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.RGB], weights=weights)
-        weights_list.append(weights_static)
-        ray_samples_list.append(ray_samples)
-
-        depth = self.renderer_depth(weights=weights_static, ray_samples=ray_samples)
-        accumulation = self.renderer_accumulation(weights=weights_static)
-
-        outputs = {"rgb": rgb, "accumulation": accumulation, "depth": depth}
-        outputs["weights_list"] = weights_list
-        outputs["ray_samples_list"] = ray_samples_list
-
-        for i in range(self.config.num_proposal_iterations):
-            outputs[f"prop_depth_{i}"] = self.renderer_depth(weights=weights_list[i], ray_samples=ray_samples_list[i])
-
-        # transients
-        if self.training and self.config.use_transient_embedding:
-            weights_transient = ray_samples.get_weights(field_outputs[FieldHeadNames.TRANSIENT_DENSITY])
-            uncertainty = self.renderer_uncertainty(field_outputs[FieldHeadNames.UNCERTAINTY], weights_transient)
-            outputs["uncertainty"] = uncertainty + 0.03  # NOTE(ethan): this is the uncertainty min
-            outputs["density_transient"] = field_outputs[FieldHeadNames.TRANSIENT_DENSITY]
-
-        # semantics
-        semantic_weights = weights_static
-        if not self.config.pass_semantic_gradients:
-            semantic_weights = semantic_weights.detach()
-        outputs["semantics"] = self.renderer_semantics(
-            field_outputs[FieldHeadNames.SEMANTICS], weights=semantic_weights
-        )
-
-        # semantics colormaps
-        semantic_labels = torch.argmax(torch.nn.functional.softmax(outputs["semantics"], dim=-1), dim=-1)
-        outputs["semantics_colormap"] = self.colormap.to(self.device)[semantic_labels]
-
-        return outputs
-
-    def get_metrics_dict(self, outputs, batch):
-        metrics_dict = {}
-        image = batch["image"].to(self.device)
-        image = self.renderer_rgb.blend_background(image)
-        metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
-        metrics_dict["distortion"] = distortion_loss(outputs["weights_list"], outputs["ray_samples_list"])
-        return metrics_dict
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None):
-        loss_dict = {}
-        image = batch["image"].to(self.device)
-        image = self.renderer_rgb.blend_background(image)
-        loss_dict["interlevel_loss"] = self.config.interlevel_loss_mult * interlevel_loss(
-            outputs["weights_list"], outputs["ray_samples_list"]
-        )
-        assert metrics_dict is not None and "distortion" in metrics_dict
-        loss_dict["distortion_loss"] = self.config.distortion_loss_mult * metrics_dict["distortion"]
-
-        # transient loss
-        if self.training and self.config.use_transient_embedding:
-            betas = outputs["uncertainty"]
-            loss_dict["uncertainty_loss"] = 3 + torch.log(betas).mean()
-            loss_dict["density_loss"] = 0.01 * outputs["density_transient"].mean()
-            loss_dict["rgb_loss"] = (((image - outputs["rgb"]) ** 2).sum(-1) / (betas[..., 0] ** 2)).mean()
-        else:
-            loss_dict["rgb_loss"] = self.rgb_loss(image, outputs["rgb"])
-
-        # semantic loss
-        loss_dict["semantics_loss"] = self.config.semantic_loss_weight * self.cross_entropy_loss(
-            outputs["semantics"], batch["semantics"][..., 0].long().to(self.device)
-        )
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        image = batch["image"].to(self.device)
-        rgb = outputs["rgb"]
-        rgb, image = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=rgb,
-            pred_accumulation=outputs["accumulation"],
-            gt_image=image,
-        )
-
-        rgb = torch.clamp(rgb, min=0, max=1)
-        acc = colormaps.apply_colormap(outputs["accumulation"])
-        depth = colormaps.apply_depth_colormap(
-            outputs["depth"],
-            accumulation=outputs["accumulation"],
-        )
-
-        combined_rgb = torch.cat([image, rgb], dim=1)
-        combined_acc = torch.cat([acc], dim=1)
-        combined_depth = torch.cat([depth], dim=1)
-
-        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
-        image = torch.moveaxis(image, -1, 0)[None, ...]
-        rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
-
-        psnr = self.psnr(image, rgb)
-        ssim = self.ssim(image, rgb)
-        lpips = self.lpips(image, rgb)
-
-        # all of these metrics will be logged as scalars
-        metrics_dict = {"psnr": float(psnr.item()), "ssim": float(ssim)}  # type: ignore
-        metrics_dict["lpips"] = float(lpips)
-
-        images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
-
-        for i in range(self.config.num_proposal_iterations):
-            key = f"prop_depth_{i}"
-            prop_depth_i = colormaps.apply_depth_colormap(
-                outputs[key],
-                accumulation=outputs["accumulation"],
-            )
-            images_dict[key] = prop_depth_i
-
-        # semantics
-        semantic_labels = torch.argmax(torch.nn.functional.softmax(outputs["semantics"], dim=-1), dim=-1)
-        images_dict["semantics_colormap"] = self.colormap.to(self.device)[semantic_labels]
-
-        # valid mask
-        images_dict["mask"] = batch["mask"].repeat(1, 1, 3).to(self.device)
-
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/splatfacto.py b/nerfstudio/models/splatfacto.py
index 7d7a78d2..728101ff 100644
--- a/nerfstudio/models/splatfacto.py
+++ b/nerfstudio/models/splatfacto.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # ruff: noqa: E741
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
@@ -20,6 +21,7 @@
 from __future__ import annotations
 
 import math
+import warnings
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple, Type, Union
 
@@ -120,6 +122,8 @@ class SplatfactoModelConfig(ModelConfig):
     """whether to initialize the positions uniformly randomly (not SFM points)"""
     num_random: int = 50000
     """Number of gaussians to initialize if random init is used"""
+    max_num_seed_points: int = -1
+    """Maximum number of seed points to use for initialization. -1 means all seed points are used."""
     random_scale: float = 10.0
     "Size of the cube to initialize random gaussians within"
     ssim_lambda: float = 0.2
@@ -167,6 +171,14 @@ def __init__(
         super().__init__(*args, **kwargs)
 
     def populate_modules(self):
+        if self.seed_points is not None and self.config.max_num_seed_points > 0:
+            n_seed_points = self.seed_points[0].shape[0]
+            perm_idx = torch.randperm(n_seed_points)
+            self.seed_points = (
+                self.seed_points[0][perm_idx][: self.config.max_num_seed_points],
+                self.seed_points[1][perm_idx][: self.config.max_num_seed_points],
+            )
+
         if self.seed_points is not None and not self.config.random_init:
             means = torch.nn.Parameter(self.seed_points[0])  # (Location, Color)
         else:
@@ -826,6 +838,12 @@ def get_metrics_dict(self, outputs, batch) -> Dict[str, torch.Tensor]:
         gt_rgb = self.composite_with_background(self.get_gt_img(batch["image"]), outputs["background"])
         metrics_dict = {}
         predicted_rgb = outputs["rgb"]
+        # slice gt_rgb to same shape as predicted_rgb
+        if not gt_rgb.shape[:2] == predicted_rgb.shape[:2]:
+            gt_rgb = gt_rgb[: predicted_rgb.shape[0], : predicted_rgb.shape[1], :]
+            # raise user warning
+            warnings.warn("GT image and predicted image have different shapes. Cropping GT image to match.")
+
         metrics_dict["psnr"] = self.psnr(predicted_rgb, gt_rgb)
 
         metrics_dict["gaussian_count"] = self.num_points
@@ -841,6 +859,10 @@ def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Te
         """
         gt_img = self.composite_with_background(self.get_gt_img(batch["image"]), outputs["background"])
         pred_img = outputs["rgb"]
+        if not gt_img.shape[:2] == pred_img.shape[:2]:
+            gt_img = gt_img[: pred_img.shape[0], : pred_img.shape[1], :]
+            # raise user warning
+            warnings.warn("GT image and predicted image have different shapes. Cropping GT image to match.")
 
         # Set masked part of both ground-truth and rendered image to black.
         # This is a little bit sketchy for the SSIM loss.
@@ -910,6 +932,12 @@ def get_image_metrics_and_images(
         else:
             predicted_rgb = outputs["rgb"]
 
+        # slice gt_rgb to same shape as predicted_rgb
+        if not gt_rgb.shape[:2] == predicted_rgb.shape[:2]:
+            gt_rgb = gt_rgb[: predicted_rgb.shape[0], : predicted_rgb.shape[1], :]
+            # raise user warning
+            warnings.warn("GT image and predicted image have different shapes. Cropping GT image to match.")
+
         combined_rgb = torch.cat([gt_rgb, predicted_rgb], dim=1)
 
         # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
diff --git a/nerfstudio/models/tensorf.py b/nerfstudio/models/tensorf.py
deleted file mode 100644
index 6c149b4b..00000000
--- a/nerfstudio/models/tensorf.py
+++ /dev/null
@@ -1,378 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-TensorRF implementation.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Literal, Tuple, Type, cast
-
-import numpy as np
-import torch
-from torch.nn import Parameter
-
-from nerfstudio.cameras.camera_optimizers import CameraOptimizer, CameraOptimizerConfig
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.configs.config_utils import to_immutable_dict
-from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
-from nerfstudio.field_components.encodings import NeRFEncoding, TensorCPEncoding, TensorVMEncoding, TriplaneEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.fields.tensorf_field import TensoRFField
-from nerfstudio.model_components.losses import MSELoss, scale_gradients_by_distance_squared, tv_loss
-from nerfstudio.model_components.ray_samplers import PDFSampler, UniformSampler
-from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
-from nerfstudio.model_components.scene_colliders import AABBBoxCollider
-from nerfstudio.models.base_model import Model, ModelConfig
-from nerfstudio.utils import colormaps, colors, misc
-
-
-@dataclass
-class TensoRFModelConfig(ModelConfig):
-    """TensoRF model config"""
-
-    _target: Type = field(default_factory=lambda: TensoRFModel)
-    """target class to instantiate"""
-    init_resolution: int = 128
-    """initial render resolution"""
-    final_resolution: int = 300
-    """final render resolution"""
-    upsampling_iters: Tuple[int, ...] = (2000, 3000, 4000, 5500, 7000)
-    """specifies a list of iteration step numbers to perform upsampling"""
-    loss_coefficients: Dict[str, float] = to_immutable_dict(
-        {
-            "rgb_loss": 1.0,
-            "tv_reg_density": 1e-3,
-            "tv_reg_color": 1e-4,
-            "l1_reg": 5e-4,
-        }
-    )
-    """Loss specific weights."""
-    num_samples: int = 50
-    """Number of samples in field evaluation"""
-    num_uniform_samples: int = 200
-    """Number of samples in density evaluation"""
-    num_den_components: int = 16
-    """Number of components in density encoding"""
-    num_color_components: int = 48
-    """Number of components in color encoding"""
-    appearance_dim: int = 27
-    """Number of channels for color encoding"""
-    tensorf_encoding: Literal["triplane", "vm", "cp"] = "vm"
-    regularization: Literal["none", "l1", "tv"] = "l1"
-    """Regularization method used in tensorf paper"""
-    camera_optimizer: CameraOptimizerConfig = field(default_factory=lambda: CameraOptimizerConfig(mode="SO3xR3"))
-    """Config of the camera optimizer to use"""
-    use_gradient_scaling: bool = False
-    """Use gradient scaler where the gradients are lower for points closer to the camera."""
-    background_color: Literal["random", "last_sample", "black", "white"] = "white"
-    """Whether to randomize the background color."""
-
-
-class TensoRFModel(Model):
-    """TensoRF Model
-
-    Args:
-        config: TensoRF configuration to instantiate model
-    """
-
-    config: TensoRFModelConfig
-
-    def __init__(
-        self,
-        config: TensoRFModelConfig,
-        **kwargs,
-    ) -> None:
-        self.init_resolution = config.init_resolution
-        self.upsampling_iters = config.upsampling_iters
-        self.num_den_components = config.num_den_components
-        self.num_color_components = config.num_color_components
-        self.appearance_dim = config.appearance_dim
-        self.upsampling_steps = (
-            np.round(
-                np.exp(
-                    np.linspace(
-                        np.log(config.init_resolution),
-                        np.log(config.final_resolution),
-                        len(config.upsampling_iters) + 1,
-                    )
-                )
-            )
-            .astype("int")
-            .tolist()[1:]
-        )
-        super().__init__(config=config, **kwargs)
-
-    def get_training_callbacks(
-        self, training_callback_attributes: TrainingCallbackAttributes
-    ) -> List[TrainingCallback]:
-        # the callback that we want to run every X iterations after the training iteration
-        def reinitialize_optimizer(self, training_callback_attributes: TrainingCallbackAttributes, step: int):
-            assert training_callback_attributes.optimizers is not None
-            assert training_callback_attributes.pipeline is not None
-            index = self.upsampling_iters.index(step)
-            resolution = self.upsampling_steps[index]
-
-            # upsample the position and direction grids
-            self.field.density_encoding.upsample_grid(resolution)
-            self.field.color_encoding.upsample_grid(resolution)
-
-            # reinitialize the encodings optimizer
-            optimizers_config = training_callback_attributes.optimizers.config
-            enc = training_callback_attributes.pipeline.get_param_groups()["encodings"]
-            lr_init = optimizers_config["encodings"]["optimizer"].lr
-
-            training_callback_attributes.optimizers.optimizers["encodings"] = optimizers_config["encodings"][
-                "optimizer"
-            ].setup(params=enc)
-            if optimizers_config["encodings"]["scheduler"]:
-                training_callback_attributes.optimizers.schedulers["encodings"] = (
-                    optimizers_config["encodings"]["scheduler"]
-                    .setup()
-                    .get_scheduler(
-                        optimizer=training_callback_attributes.optimizers.optimizers["encodings"], lr_init=lr_init
-                    )
-                )
-
-        callbacks = [
-            TrainingCallback(
-                where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
-                iters=self.upsampling_iters,
-                func=reinitialize_optimizer,
-                args=[self, training_callback_attributes],
-            )
-        ]
-        return callbacks
-
-    def update_to_step(self, step: int) -> None:
-        if step < self.upsampling_iters[0]:
-            return
-
-        new_iters = list(self.upsampling_iters) + [step + 1]
-        new_iters.sort()
-
-        index = new_iters.index(step + 1)
-        new_grid_resolution = self.upsampling_steps[index - 1]
-
-        self.field.density_encoding.upsample_grid(new_grid_resolution)  # type: ignore
-        self.field.color_encoding.upsample_grid(new_grid_resolution)  # type: ignore
-
-    def populate_modules(self):
-        """Set the fields and modules"""
-        super().populate_modules()
-
-        # setting up fields
-        if self.config.tensorf_encoding == "vm":
-            density_encoding = TensorVMEncoding(
-                resolution=self.init_resolution,
-                num_components=self.num_den_components,
-            )
-            color_encoding = TensorVMEncoding(
-                resolution=self.init_resolution,
-                num_components=self.num_color_components,
-            )
-        elif self.config.tensorf_encoding == "cp":
-            density_encoding = TensorCPEncoding(
-                resolution=self.init_resolution,
-                num_components=self.num_den_components,
-            )
-            color_encoding = TensorCPEncoding(
-                resolution=self.init_resolution,
-                num_components=self.num_color_components,
-            )
-        elif self.config.tensorf_encoding == "triplane":
-            density_encoding = TriplaneEncoding(
-                resolution=self.init_resolution,
-                num_components=self.num_den_components,
-            )
-            color_encoding = TriplaneEncoding(
-                resolution=self.init_resolution,
-                num_components=self.num_color_components,
-            )
-        else:
-            raise ValueError(f"Encoding {self.config.tensorf_encoding} not supported")
-
-        feature_encoding = NeRFEncoding(in_dim=self.appearance_dim, num_frequencies=2, min_freq_exp=0, max_freq_exp=2)
-        direction_encoding = NeRFEncoding(in_dim=3, num_frequencies=2, min_freq_exp=0, max_freq_exp=2)
-
-        self.field = TensoRFField(
-            self.scene_box.aabb,
-            feature_encoding=feature_encoding,
-            direction_encoding=direction_encoding,
-            density_encoding=density_encoding,
-            color_encoding=color_encoding,
-            appearance_dim=self.appearance_dim,
-            head_mlp_num_layers=2,
-            head_mlp_layer_width=128,
-            use_sh=False,
-        )
-
-        # samplers
-        self.sampler_uniform = UniformSampler(num_samples=self.config.num_uniform_samples, single_jitter=True)
-        self.sampler_pdf = PDFSampler(num_samples=self.config.num_samples, single_jitter=True, include_original=False)
-
-        # renderers
-        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer()
-
-        # losses
-        self.rgb_loss = MSELoss()
-
-        # metrics
-        from torchmetrics.functional import structural_similarity_index_measure
-        from torchmetrics.image import PeakSignalNoiseRatio
-        from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-
-        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
-        self.ssim = structural_similarity_index_measure
-        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
-
-        # colliders
-        if self.config.enable_collider:
-            self.collider = AABBBoxCollider(scene_box=self.scene_box)
-
-        # regularizations
-        if self.config.tensorf_encoding == "cp" and self.config.regularization == "tv":
-            raise RuntimeError("TV reg not supported for CP decomposition")
-
-        # (optional) camera optimizer
-        self.camera_optimizer: CameraOptimizer = self.config.camera_optimizer.setup(
-            num_cameras=self.num_train_data, device="cpu"
-        )
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-
-        param_groups["fields"] = (
-            list(self.field.mlp_head.parameters())
-            + list(self.field.B.parameters())
-            + list(self.field.field_output_rgb.parameters())
-        )
-        param_groups["encodings"] = list(self.field.color_encoding.parameters()) + list(
-            self.field.density_encoding.parameters()
-        )
-        self.camera_optimizer.get_param_groups(param_groups=param_groups)
-
-        return param_groups
-
-    def get_outputs(self, ray_bundle: RayBundle):
-        # uniform sampling
-        if self.training:
-            self.camera_optimizer.apply_to_raybundle(ray_bundle)
-        ray_samples_uniform = self.sampler_uniform(ray_bundle)
-        dens = self.field.get_density(ray_samples_uniform)
-        weights = ray_samples_uniform.get_weights(dens)
-        coarse_accumulation = self.renderer_accumulation(weights)
-        acc_mask = torch.where(coarse_accumulation < 0.0001, False, True).reshape(-1)
-
-        # pdf sampling
-        ray_samples_pdf = self.sampler_pdf(ray_bundle, ray_samples_uniform, weights)
-
-        # fine field:
-        field_outputs_fine = self.field.forward(
-            ray_samples_pdf, mask=acc_mask, bg_color=colors.WHITE.to(weights.device)
-        )
-        if self.config.use_gradient_scaling:
-            field_outputs_fine = scale_gradients_by_distance_squared(field_outputs_fine, ray_samples_pdf)
-
-        weights_fine = ray_samples_pdf.get_weights(field_outputs_fine[FieldHeadNames.DENSITY])
-
-        accumulation = self.renderer_accumulation(weights_fine)
-        depth = self.renderer_depth(weights_fine, ray_samples_pdf)
-
-        rgb = self.renderer_rgb(
-            rgb=field_outputs_fine[FieldHeadNames.RGB],
-            weights=weights_fine,
-        )
-
-        rgb = torch.where(accumulation < 0, colors.WHITE.to(rgb.device), rgb)
-        accumulation = torch.clamp(accumulation, min=0)
-
-        outputs = {"rgb": rgb, "accumulation": accumulation, "depth": depth}
-        return outputs
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Tensor]:
-        # Scaling metrics by coefficients to create the losses.
-        device = outputs["rgb"].device
-        image = batch["image"].to(device)
-        pred_image, image = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb"],
-            pred_accumulation=outputs["accumulation"],
-            gt_image=image,
-        )
-
-        rgb_loss = self.rgb_loss(image, pred_image)
-
-        loss_dict = {"rgb_loss": rgb_loss}
-
-        if self.config.regularization == "l1":
-            l1_parameters = []
-            for parameter in self.field.density_encoding.parameters():
-                l1_parameters.append(parameter.view(-1))
-            loss_dict["l1_reg"] = torch.abs(torch.cat(l1_parameters)).mean()
-        elif self.config.regularization == "tv":
-            density_plane_coef = self.field.density_encoding.plane_coef
-            color_plane_coef = self.field.color_encoding.plane_coef
-            assert isinstance(color_plane_coef, torch.Tensor) and isinstance(
-                density_plane_coef, torch.Tensor
-            ), "TV reg only supported for TensoRF encoding types with plane_coef attribute"
-            loss_dict["tv_reg_density"] = tv_loss(density_plane_coef)
-            loss_dict["tv_reg_color"] = tv_loss(color_plane_coef)
-        elif self.config.regularization == "none":
-            pass
-        else:
-            raise ValueError(f"Regularization {self.config.regularization} not supported")
-
-        self.camera_optimizer.get_loss_dict(loss_dict)
-
-        loss_dict = misc.scale_dict(loss_dict, self.config.loss_coefficients)
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        image = batch["image"].to(outputs["rgb"].device)
-        image = self.renderer_rgb.blend_background(image)
-        rgb = outputs["rgb"]
-        acc = colormaps.apply_colormap(outputs["accumulation"])
-        assert self.config.collider_params is not None
-        depth = colormaps.apply_depth_colormap(
-            outputs["depth"],
-            accumulation=outputs["accumulation"],
-            near_plane=self.config.collider_params["near_plane"],
-            far_plane=self.config.collider_params["far_plane"],
-        )
-
-        combined_rgb = torch.cat([image, rgb], dim=1)
-
-        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
-        image = torch.moveaxis(image, -1, 0)[None, ...]
-        rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
-
-        psnr = self.psnr(image, rgb)
-        ssim = cast(torch.Tensor, self.ssim(image, rgb))
-        lpips = self.lpips(image, rgb)
-
-        metrics_dict = {
-            "psnr": float(psnr.item()),
-            "ssim": float(ssim.item()),
-            "lpips": float(lpips.item()),
-        }
-        self.camera_optimizer.get_metrics_dict(metrics_dict)
-
-        images_dict = {"img": combined_rgb, "accumulation": acc, "depth": depth}
-        return metrics_dict, images_dict
diff --git a/nerfstudio/models/vanilla_nerf.py b/nerfstudio/models/vanilla_nerf.py
deleted file mode 100644
index dd856510..00000000
--- a/nerfstudio/models/vanilla_nerf.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Implementation of vanilla nerf.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Literal, Tuple, Type
-
-import torch
-from torch.nn import Parameter
-
-from nerfstudio.cameras.rays import RayBundle
-from nerfstudio.configs.config_utils import to_immutable_dict
-from nerfstudio.field_components.encodings import NeRFEncoding
-from nerfstudio.field_components.field_heads import FieldHeadNames
-from nerfstudio.field_components.temporal_distortions import TemporalDistortionKind
-from nerfstudio.fields.vanilla_nerf_field import NeRFField
-from nerfstudio.model_components.losses import MSELoss, scale_gradients_by_distance_squared
-from nerfstudio.model_components.ray_samplers import PDFSampler, UniformSampler
-from nerfstudio.model_components.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
-from nerfstudio.models.base_model import Model, ModelConfig
-from nerfstudio.utils import colormaps, misc
-
-
-@dataclass
-class VanillaModelConfig(ModelConfig):
-    """Vanilla Model Config"""
-
-    _target: Type = field(default_factory=lambda: NeRFModel)
-    num_coarse_samples: int = 64
-    """Number of samples in coarse field evaluation"""
-    num_importance_samples: int = 128
-    """Number of samples in fine field evaluation"""
-
-    enable_temporal_distortion: bool = False
-    """Specifies whether or not to include ray warping based on time."""
-    temporal_distortion_params: Dict[str, Any] = to_immutable_dict({"kind": TemporalDistortionKind.DNERF})
-    """Parameters to instantiate temporal distortion with"""
-    use_gradient_scaling: bool = False
-    """Use gradient scaler where the gradients are lower for points closer to the camera."""
-    background_color: Literal["random", "last_sample", "black", "white"] = "white"
-    """Whether to randomize the background color."""
-
-
-class NeRFModel(Model):
-    """Vanilla NeRF model
-
-    Args:
-        config: Basic NeRF configuration to instantiate model
-    """
-
-    config: VanillaModelConfig
-
-    def __init__(
-        self,
-        config: VanillaModelConfig,
-        **kwargs,
-    ) -> None:
-        self.field_coarse = None
-        self.field_fine = None
-        self.temporal_distortion = None
-
-        super().__init__(
-            config=config,
-            **kwargs,
-        )
-
-    def populate_modules(self):
-        """Set the fields and modules"""
-        super().populate_modules()
-
-        # fields
-        position_encoding = NeRFEncoding(
-            in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True
-        )
-        direction_encoding = NeRFEncoding(
-            in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True
-        )
-
-        self.field_coarse = NeRFField(
-            position_encoding=position_encoding,
-            direction_encoding=direction_encoding,
-        )
-
-        self.field_fine = NeRFField(
-            position_encoding=position_encoding,
-            direction_encoding=direction_encoding,
-        )
-
-        # samplers
-        self.sampler_uniform = UniformSampler(num_samples=self.config.num_coarse_samples)
-        self.sampler_pdf = PDFSampler(num_samples=self.config.num_importance_samples)
-
-        # renderers
-        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
-        self.renderer_accumulation = AccumulationRenderer()
-        self.renderer_depth = DepthRenderer()
-
-        # losses
-        self.rgb_loss = MSELoss()
-
-        # metrics
-        from torchmetrics.functional import structural_similarity_index_measure
-        from torchmetrics.image import PeakSignalNoiseRatio
-        from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-
-        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
-        self.ssim = structural_similarity_index_measure
-        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
-
-        if getattr(self.config, "enable_temporal_distortion", False):
-            params = self.config.temporal_distortion_params
-            kind = params.pop("kind")
-            self.temporal_distortion = kind.to_temporal_distortion(params)
-
-    def get_param_groups(self) -> Dict[str, List[Parameter]]:
-        param_groups = {}
-        if self.field_coarse is None or self.field_fine is None:
-            raise ValueError("populate_fields() must be called before get_param_groups")
-        param_groups["fields"] = list(self.field_coarse.parameters()) + list(self.field_fine.parameters())
-        if self.temporal_distortion is not None:
-            param_groups["temporal_distortion"] = list(self.temporal_distortion.parameters())
-        return param_groups
-
-    def get_outputs(self, ray_bundle: RayBundle):
-        if self.field_coarse is None or self.field_fine is None:
-            raise ValueError("populate_fields() must be called before get_outputs")
-
-        # uniform sampling
-        ray_samples_uniform = self.sampler_uniform(ray_bundle)
-        if self.temporal_distortion is not None:
-            offsets = None
-            if ray_samples_uniform.times is not None:
-                offsets = self.temporal_distortion(
-                    ray_samples_uniform.frustums.get_positions(), ray_samples_uniform.times
-                )
-            ray_samples_uniform.frustums.set_offsets(offsets)
-
-        # coarse field:
-        field_outputs_coarse = self.field_coarse.forward(ray_samples_uniform)
-        if self.config.use_gradient_scaling:
-            field_outputs_coarse = scale_gradients_by_distance_squared(field_outputs_coarse, ray_samples_uniform)
-        weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse[FieldHeadNames.DENSITY])
-        rgb_coarse = self.renderer_rgb(
-            rgb=field_outputs_coarse[FieldHeadNames.RGB],
-            weights=weights_coarse,
-        )
-        accumulation_coarse = self.renderer_accumulation(weights_coarse)
-        depth_coarse = self.renderer_depth(weights_coarse, ray_samples_uniform)
-
-        # pdf sampling
-        ray_samples_pdf = self.sampler_pdf(ray_bundle, ray_samples_uniform, weights_coarse)
-        if self.temporal_distortion is not None:
-            offsets = None
-            if ray_samples_pdf.times is not None:
-                offsets = self.temporal_distortion(ray_samples_pdf.frustums.get_positions(), ray_samples_pdf.times)
-            ray_samples_pdf.frustums.set_offsets(offsets)
-
-        # fine field:
-        field_outputs_fine = self.field_fine.forward(ray_samples_pdf)
-        if self.config.use_gradient_scaling:
-            field_outputs_fine = scale_gradients_by_distance_squared(field_outputs_fine, ray_samples_pdf)
-        weights_fine = ray_samples_pdf.get_weights(field_outputs_fine[FieldHeadNames.DENSITY])
-        rgb_fine = self.renderer_rgb(
-            rgb=field_outputs_fine[FieldHeadNames.RGB],
-            weights=weights_fine,
-        )
-        accumulation_fine = self.renderer_accumulation(weights_fine)
-        depth_fine = self.renderer_depth(weights_fine, ray_samples_pdf)
-
-        outputs = {
-            "rgb_coarse": rgb_coarse,
-            "rgb_fine": rgb_fine,
-            "accumulation_coarse": accumulation_coarse,
-            "accumulation_fine": accumulation_fine,
-            "depth_coarse": depth_coarse,
-            "depth_fine": depth_fine,
-        }
-        return outputs
-
-    def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Tensor]:
-        # Scaling metrics by coefficients to create the losses.
-        device = outputs["rgb_coarse"].device
-        image = batch["image"].to(device)
-        coarse_pred, coarse_image = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb_coarse"],
-            pred_accumulation=outputs["accumulation_coarse"],
-            gt_image=image,
-        )
-        fine_pred, fine_image = self.renderer_rgb.blend_background_for_loss_computation(
-            pred_image=outputs["rgb_fine"],
-            pred_accumulation=outputs["accumulation_fine"],
-            gt_image=image,
-        )
-
-        rgb_loss_coarse = self.rgb_loss(coarse_image, coarse_pred)
-        rgb_loss_fine = self.rgb_loss(fine_image, fine_pred)
-
-        loss_dict = {"rgb_loss_coarse": rgb_loss_coarse, "rgb_loss_fine": rgb_loss_fine}
-        loss_dict = misc.scale_dict(loss_dict, self.config.loss_coefficients)
-        return loss_dict
-
-    def get_image_metrics_and_images(
-        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
-        image = batch["image"].to(outputs["rgb_coarse"].device)
-        image = self.renderer_rgb.blend_background(image)
-        rgb_coarse = outputs["rgb_coarse"]
-        rgb_fine = outputs["rgb_fine"]
-        acc_coarse = colormaps.apply_colormap(outputs["accumulation_coarse"])
-        acc_fine = colormaps.apply_colormap(outputs["accumulation_fine"])
-        assert self.config.collider_params is not None
-        depth_coarse = colormaps.apply_depth_colormap(
-            outputs["depth_coarse"],
-            accumulation=outputs["accumulation_coarse"],
-            near_plane=self.config.collider_params["near_plane"],
-            far_plane=self.config.collider_params["far_plane"],
-        )
-        depth_fine = colormaps.apply_depth_colormap(
-            outputs["depth_fine"],
-            accumulation=outputs["accumulation_fine"],
-            near_plane=self.config.collider_params["near_plane"],
-            far_plane=self.config.collider_params["far_plane"],
-        )
-
-        combined_rgb = torch.cat([image, rgb_coarse, rgb_fine], dim=1)
-        combined_acc = torch.cat([acc_coarse, acc_fine], dim=1)
-        combined_depth = torch.cat([depth_coarse, depth_fine], dim=1)
-
-        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
-        image = torch.moveaxis(image, -1, 0)[None, ...]
-        rgb_coarse = torch.moveaxis(rgb_coarse, -1, 0)[None, ...]
-        rgb_fine = torch.moveaxis(rgb_fine, -1, 0)[None, ...]
-
-        coarse_psnr = self.psnr(image, rgb_coarse)
-        fine_psnr = self.psnr(image, rgb_fine)
-        fine_ssim = self.ssim(image, rgb_fine)
-        fine_lpips = self.lpips(image, rgb_fine)
-        assert isinstance(fine_ssim, torch.Tensor)
-
-        metrics_dict = {
-            "psnr": float(fine_psnr.item()),
-            "coarse_psnr": float(coarse_psnr),
-            "fine_psnr": float(fine_psnr),
-            "fine_ssim": float(fine_ssim),
-            "fine_lpips": float(fine_lpips),
-        }
-        images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
-        return metrics_dict, images_dict
diff --git a/nerfstudio/pipelines/ad_pipeline.py b/nerfstudio/pipelines/ad_pipeline.py
new file mode 100644
index 00000000..ce1c147c
--- /dev/null
+++ b/nerfstudio/pipelines/ad_pipeline.py
@@ -0,0 +1,392 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass, field
+from pathlib import Path
+from time import time
+from typing import Dict, List, Optional, Tuple, Type
+
+import torch
+from PIL import Image
+from rich.progress import BarColumn, MofNCompleteColumn, Progress, TextColumn, TimeElapsedColumn
+from torchmetrics.image.fid import FrechetInceptionDistance
+from torchvision.transforms.functional import to_pil_image, to_tensor
+
+from nerfstudio.data.datamanagers.ad_datamanager import ADDataManager, ADDataManagerConfig
+from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager
+from nerfstudio.data.datamanagers.parallel_datamanager import ParallelDataManager
+from nerfstudio.models.ad_model import ADModel, ADModelConfig
+from nerfstudio.pipelines.base_pipeline import VanillaPipeline, VanillaPipelineConfig
+from nerfstudio.utils import profiler
+
+
+@dataclass
+class ADPipelineConfig(VanillaPipelineConfig):
+    """Configuration for pipeline instantiation"""
+
+    _target: Type = field(default_factory=lambda: ADPipeline)
+    """target class to instantiate"""
+    datamanager: ADDataManagerConfig = field(default_factory=ADDataManagerConfig)
+    """specifies the datamanager config"""
+    model: ADModelConfig = field(default_factory=ADModelConfig)
+    """specifies the model config"""
+    calc_fid_steps: Tuple[int, ...] = (20000,)  # NOTE: must also be an eval step for this to work
+    """Whether to calculate FID for lane shifted images."""
+    ray_patch_size: Tuple[int, int] = (32, 32)
+    """Size of the ray patches to sample from the image during training (for camera rays only)."""
+
+    def __post_init__(self) -> None:
+        assert self.ray_patch_size[0] == self.ray_patch_size[1], "Non-square patches are not supported yet, sorry."
+        self.datamanager.image_divisible_by = self.model.rgb_upsample_factor
+
+
+class ADPipeline(VanillaPipeline):
+    """Pipeline for training AD models."""
+
+    def __init__(self, config: ADPipelineConfig, **kwargs):
+        pixel_sampler = config.datamanager.pixel_sampler
+        pixel_sampler.patch_size = config.ray_patch_size[0]
+        pixel_sampler.patch_scale = config.model.rgb_upsample_factor
+        super().__init__(config, **kwargs)
+
+        # Fix type hints
+        self.datamanager: ADDataManager = self.datamanager
+        self.model: ADModel = self.model
+        self.config: ADPipelineConfig = self.config
+
+        # Disable ray drop classification if we do not add missing points
+        if not self.datamanager.dataparser.config.add_missing_points:
+            self.model.disable_ray_drop()
+
+        self.fid = None
+
+    @profiler.time_function
+    def get_train_loss_dict(self, step: int):
+        """This function gets your training loss dict. This will be responsible for
+        getting the next batch of data from the DataManager and interfacing with the
+        Model class, feeding the data to the model's forward function.
+
+        Args:
+            step: current iteration step to update sampler if using DDP (distributed)
+        """
+        # Regular forward pass and loss calc
+        ray_bundle, batch = self.datamanager.next_train(step)
+        model_outputs = self._model(ray_bundle, patch_size=self.config.ray_patch_size)
+        metrics_dict = self.model.get_metrics_dict(model_outputs, batch)
+
+        if (actors := self.model.dynamic_actors).config.optimize_trajectories:
+            pos_norm = (actors.actor_positions - actors.initial_positions).norm(dim=-1)
+            metrics_dict["traj_opt_translation"] = pos_norm[pos_norm > 0].mean().nan_to_num()
+            metrics_dict["traj_opt_rotation"] = (
+                (actors.actor_rotations_6d - actors.initial_rotations_6d)[pos_norm > 0].norm(dim=-1).mean().nan_to_num()
+            )
+
+        loss_dict = self.model.get_loss_dict(model_outputs, batch, metrics_dict)
+
+        return model_outputs, loss_dict, metrics_dict
+
+    @profiler.time_function
+    def get_eval_loss_dict(self, step: int):
+        """This function gets your evaluation loss dict. It needs to get the data
+        from the DataManager and feed it to the model's forward function
+
+        Args:
+            step: current iteration step
+        """
+        self.eval()
+        ray_bundle, batch = self.datamanager.next_eval(step)
+        model_outputs = self.model(ray_bundle, patch_size=self.config.ray_patch_size)
+        metrics_dict = self.model.get_metrics_dict(model_outputs, batch)
+        loss_dict = self.model.get_loss_dict(model_outputs, batch, metrics_dict)
+        self.train()
+        return model_outputs, loss_dict, metrics_dict
+
+    @profiler.time_function
+    def get_eval_image_metrics_and_images(self, step: int):
+        """This function gets your evaluation loss dict. It needs to get the data
+        from the DataManager and feed it to the model's forward function
+
+        Args:
+            step: current iteration step
+        """
+        self.eval()
+        # Image eval
+        camera, batch = self.datamanager.next_eval_image(step)
+        outputs = self.model.get_outputs_for_camera(camera)
+        metrics_dict, images_dict = self.model.get_image_metrics_and_images(outputs, batch)
+        assert "num_rays" not in metrics_dict
+        metrics_dict["num_rays"] = (camera.height * camera.width * camera.size).item()
+
+        # Lidar eval
+        lidar, batch = self.datamanager.next_eval_lidar(step)
+        outputs, batch = self.model.get_outputs_for_lidar(lidar, batch=batch)
+        lidar_metrics_dict, _ = self.model.get_image_metrics_and_images(outputs, batch)
+        assert not set(lidar_metrics_dict.keys()).intersection(metrics_dict.keys())
+        metrics_dict.update(lidar_metrics_dict)
+
+        self.train()
+        return metrics_dict, images_dict
+
+    @profiler.time_function
+    def get_average_eval_image_metrics(
+        self, step: Optional[int] = None, output_path: Optional[Path] = None, get_std: bool = False
+    ):
+        """Iterate over all the images in the eval dataset and get the average.
+
+         Args:
+            step: current training step
+            output_path: optional path to save rendered images to
+            get_std: Set True if you want to return std with the mean metric.
+
+        Returns:
+            metrics_dict: dictionary of metrics
+        """
+        self.eval()
+        metrics_dict_list = []
+        assert isinstance(self.datamanager, (VanillaDataManager, ParallelDataManager))
+        with Progress(
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TimeElapsedColumn(),
+            MofNCompleteColumn(),
+            transient=True,
+        ) as progress:
+            lane_shift_fids = (
+                {i: FrechetInceptionDistance().to(self.device) for i in (0, 2, 3)}
+                if step in self.config.calc_fid_steps or step is None
+                else {}
+            )
+            vertical_shift_fids = (
+                {i: FrechetInceptionDistance().to(self.device) for i in (1,)}
+                if step in self.config.calc_fid_steps or step is None
+                else {}
+            )
+            actor_edits = {
+                "rot": [(0.5, 0), (-0.5, 0)],
+                "trans": [(0, 2.0), (0, -2.0)],
+                # "both": [(0.5, 2.0), (-0.5, 2.0), (0.5, -2.0), (-0.5, -2.0)],
+            }
+            actor_fids = (
+                {k: FrechetInceptionDistance().to(self.device) for k in actor_edits.keys()}
+                if step in self.config.calc_fid_steps or step is None
+                else {}
+            )
+            if actor_fids:
+                actor_fids["true"] = FrechetInceptionDistance().to(self.device)
+
+            num_images = len(self.datamanager.fixed_indices_eval_dataloader)
+            task = progress.add_task("[green]Evaluating all eval images...", total=num_images)
+            for camera, batch in self.datamanager.fixed_indices_eval_dataloader:
+                # time this the following line
+                inner_start = time()
+                # Generate images from the original rays
+                camera_ray_bundle = camera.generate_rays(camera_indices=0, keep_shape=True)
+                outputs = self.model.get_outputs_for_camera_ray_bundle(camera_ray_bundle)
+                # Compute metrics for the original image
+                metrics_dict, images_dict = self.model.get_image_metrics_and_images(outputs, batch)
+                if output_path is not None:
+                    camera_indices = camera_ray_bundle.camera_indices
+                    assert camera_indices is not None
+                    for key, val in images_dict.items():
+                        Image.fromarray((val * 255).byte().cpu().numpy()).save(
+                            output_path / "{0:06d}-{1}.jpg".format(int(camera_indices[0, 0, 0]), key)
+                        )
+                # Add timing stuff
+                assert "num_rays_per_sec" not in metrics_dict
+                num_rays = math.prod(camera_ray_bundle.shape)
+                metrics_dict["num_rays_per_sec"] = num_rays / (time() - inner_start)
+                fps_str = "fps"
+                assert fps_str not in metrics_dict
+                metrics_dict[fps_str] = metrics_dict["num_rays_per_sec"] / num_rays
+                metrics_dict_list.append(metrics_dict)
+                if lane_shift_fids:
+                    self._update_lane_shift_fid(lane_shift_fids, camera_ray_bundle, batch["image"], outputs["rgb"])
+                if vertical_shift_fids:
+                    self._update_vertical_shift_fid(vertical_shift_fids, camera_ray_bundle, batch["image"])
+                if actor_fids:
+                    self._update_actor_fids(actor_fids, actor_edits, camera_ray_bundle, batch["image"])
+                progress.advance(task)
+            num_lidar = len(self.datamanager.fixed_indices_eval_lidar_dataloader)
+            task = progress.add_task("[green]Evaluating all eval point clouds...", total=num_lidar)
+            for lidar, batch in self.datamanager.fixed_indices_eval_lidar_dataloader:
+                outputs, batch = self.model.get_outputs_for_lidar(lidar, batch=batch)
+                metrics_dict, _ = self.model.get_image_metrics_and_images(outputs, batch)
+                metrics_dict_list.append(metrics_dict)
+                progress.advance(task)
+
+        # average the metrics list
+        metrics_dict = {}
+        keys = {key for metrics_dict in metrics_dict_list for key in metrics_dict.keys()}
+        # remove the keys related to actor metrics as they need to be averaged differently
+        actor_keys = {key for key in keys if key.startswith("actor_")}
+        keys = keys - actor_keys
+
+        for key in keys:
+            if get_std:
+                key_std, key_mean = torch.std_mean(
+                    torch.tensor([metrics_dict[key] for metrics_dict in metrics_dict_list if key in metrics_dict])
+                )
+                metrics_dict[key] = float(key_mean)
+                metrics_dict[f"{key}_std"] = float(key_std)
+            else:
+                metrics_dict[key] = float(
+                    torch.mean(
+                        torch.tensor([metrics_dict[key] for metrics_dict in metrics_dict_list if key in metrics_dict])
+                    )
+                )
+        # average the actor metrics. Note that due to the way we compute the actor metrics,
+        # we need to weight them by how big portion of the image they cover.
+        actor_metrics_dict = [md for md in metrics_dict_list if "actor_coverage" in md]
+        if actor_metrics_dict:
+            actor_coverages = torch.tensor([md["actor_coverage"] for md in actor_metrics_dict])
+            for key in actor_keys:
+                # we dont want to average the actor coverage in this way.
+                if key == "actor_coverage":
+                    continue
+                # we should weight the actor metrics by the actor coverage
+                metrics_dict[key] = float(
+                    torch.sum(
+                        torch.tensor(
+                            [md[key] for md in actor_metrics_dict],
+                        )
+                        * actor_coverages
+                    )
+                    / actor_coverages.sum()
+                )
+
+        # Add FID metrics (if applicable)
+        for shift, fid in lane_shift_fids.items():
+            metrics_dict[f"lane_shift_{shift}_fid"] = fid.compute().item()
+
+        for shift, fid in vertical_shift_fids.items():
+            metrics_dict[f"vertical_shift_{shift}_fid"] = fid.compute().item()
+
+        if actor_fids:
+            for edit_type in actor_edits.keys():
+                metrics_dict[f"actor_shift_{edit_type}_fid"] = actor_fids[edit_type].compute().item()
+
+        self.train()
+        return metrics_dict
+
+    @staticmethod
+    def _downsample_img(
+        img: torch.Tensor,
+        out_size: Tuple[int, int] = (
+            299,
+            299,
+        ),
+    ):
+        """Converts tensor to PIL, downsamples with bicubic, and converts back to tensor."""
+        img = to_pil_image(img)
+        img = img.resize(out_size, Image.BICUBIC)
+        img = to_tensor(img)
+        return img
+
+    def _update_lane_shift_fid(self, fids: Dict[int, FrechetInceptionDistance], ray_bundle, orig_img, gen_img):
+        """Updates the FID metrics (for shifted views) for the given ray bundle and images."""
+        # Update "true" FID (with hack to only compute it once)
+        img_original = (
+            (self._downsample_img((orig_img).permute(2, 0, 1)) * 255).unsqueeze(0).to(torch.uint8).to(self.device)
+        )
+        fids_list = list(fids.values())
+        fids_list[0].update(img_original, real=True)
+        for fid in fids_list[1:]:
+            fid.real_features_sum = fids_list[0].real_features_sum
+            fid.real_features_cov_sum = fids_list[0].real_features_cov_sum
+            fid.real_features_num_samples = fids_list[0].real_features_num_samples
+
+        # Compute FID for shifted views
+        assert fids.keys() == {0, 2, 3}, "Shift amounts are hardcoded for now."
+        imgs_generated = {0: gen_img}
+
+        driving_direction = ray_bundle.metadata["velocities"][0, 0, :]
+        driving_direction = driving_direction / driving_direction.norm()
+        orth_right_direction = torch.cross(
+            driving_direction, torch.tensor([0.0, 0.0, 1.0], device=driving_direction.device)
+        )
+
+        # TODO: Do we need to take z axis into account?
+        shift_sign = self.datamanager.eval_lidar_dataset.metadata.get("lane_shift_sign", 1)
+        original_ray_origins = ray_bundle.origins.clone()
+        ray_bundle.origins[..., :2] += 2 * orth_right_direction[:2] * shift_sign
+        imgs_generated[2] = self.model.get_outputs_for_camera_ray_bundle(ray_bundle)["rgb"]
+        ray_bundle.origins[..., :2] += 1 * orth_right_direction[:2] * shift_sign
+        imgs_generated[3] = self.model.get_outputs_for_camera_ray_bundle(ray_bundle)["rgb"]
+        ray_bundle.origins = original_ray_origins
+        for shift, img in imgs_generated.items():
+            img = (self._downsample_img((img).permute(2, 0, 1)) * 255).unsqueeze(0).to(torch.uint8).to(self.device)
+            fids[shift].update(img, real=False)
+
+    def _update_vertical_shift_fid(self, fids: Dict[int, FrechetInceptionDistance], ray_bundle, orig_img):
+        """Updates the FID metrics (for shifted views) for the given ray bundle and images."""
+        # Update "true" FID (with hack to only compute it once)
+        img_original = (
+            (self._downsample_img((orig_img).permute(2, 0, 1)) * 255).unsqueeze(0).to(torch.uint8).to(self.device)
+        )
+        fids_list = list(fids.values())
+        fids_list[0].update(img_original, real=True)
+        for fid in fids_list[1:]:
+            fid.real_features_sum = fids_list[0].real_features_sum
+            fid.real_features_cov_sum = fids_list[0].real_features_cov_sum
+            fid.real_features_num_samples = fids_list[0].real_features_num_samples
+
+        # Compute FID for shifted views
+        assert fids.keys() == {1}, "Shift amounts are hardcoded for now."
+        imgs_generated = {}
+
+        original_ray_origins = ray_bundle.origins.clone()
+        ray_bundle.origins[..., 2] += 1.0
+        imgs_generated[1] = self.model.get_outputs_for_camera_ray_bundle(ray_bundle)["rgb"]
+        ray_bundle.origins = original_ray_origins
+
+        for shift, img in imgs_generated.items():
+            img = (self._downsample_img((img).permute(2, 0, 1)) * 255).unsqueeze(0).to(torch.uint8).to(self.device)
+            fids[shift].update(img, real=False)
+
+    def _update_actor_fids(
+        self,
+        fids: Dict[str, FrechetInceptionDistance],
+        actor_edits: Dict[str, List[Tuple]],
+        ray_bundle,
+        orig_img,
+    ) -> None:
+        """Updates the FID metrics (for shifted actor views) for the given ray bundle and images."""
+        # Update "true" FID (with hack to only compute it once)
+        img_original = (
+            (self._downsample_img((orig_img).permute(2, 0, 1)) * 255).unsqueeze(0).to(torch.uint8).to(self.device)
+        )
+        fids["true"].update(img_original, real=True)
+        for edit_type in actor_edits.keys():
+            fids[edit_type].real_features_sum = fids["true"].real_features_sum
+            fids[edit_type].real_features_cov_sum = fids["true"].real_features_cov_sum
+            fids[edit_type].real_features_num_samples = fids["true"].real_features_num_samples
+
+        # Compute FID for actor edits
+        imgs_generated_per_edit = {}
+        for edit_type in actor_edits.keys():
+            imgs = []
+            for rotation, lateral in actor_edits[edit_type]:
+                self.model.dynamic_actors.actor_editing["rotation"] = rotation
+                self.model.dynamic_actors.actor_editing["lateral"] = lateral
+                imgs.append(self.model.get_outputs_for_camera_ray_bundle(ray_bundle)["rgb"])
+            imgs_generated_per_edit[edit_type] = imgs
+
+        for edit_type, imgs in imgs_generated_per_edit.items():
+            for img in imgs:
+                img = (self._downsample_img((img).permute(2, 0, 1)) * 255).unsqueeze(0).to(torch.uint8).to(self.device)
+                fids[edit_type].update(img, real=False)
+
+        self.model.dynamic_actors.actor_editing["rotation"] = 0
+        self.model.dynamic_actors.actor_editing["lateral"] = 0
diff --git a/nerfstudio/pipelines/base_pipeline.py b/nerfstudio/pipelines/base_pipeline.py
index a746a81a..c1242f82 100644
--- a/nerfstudio/pipelines/base_pipeline.py
+++ b/nerfstudio/pipelines/base_pipeline.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,6 +16,7 @@
 """
 Abstracts for the Pipeline class.
 """
+
 from __future__ import annotations
 
 import typing
@@ -32,6 +34,7 @@
 from torch.nn import Parameter
 from torch.nn.parallel import DistributedDataParallel as DDP
 
+from nerfstudio.cameras.lidars import transform_points
 from nerfstudio.configs.base_config import InstantiateConfig
 from nerfstudio.data.datamanagers.base_datamanager import DataManager, DataManagerConfig, VanillaDataManager
 from nerfstudio.data.datamanagers.full_images_datamanager import FullImageDatamanager
@@ -185,7 +188,7 @@ def get_average_eval_image_metrics(
             get_std: Set True if you want to return std with the mean metric.
         """
 
-    def load_pipeline(self, loaded_state: Dict[str, Any], step: int) -> None:
+    def load_pipeline(self, loaded_state: Dict[str, Any], step: int, strict: bool) -> None:
         """Load the checkpoint from the given path
 
         Args:
@@ -263,13 +266,34 @@ def __init__(
             pts = self.datamanager.train_dataparser_outputs.metadata["points3D_xyz"]
             pts_rgb = self.datamanager.train_dataparser_outputs.metadata["points3D_rgb"]
             seed_pts = (pts, pts_rgb)
+        elif (
+            hasattr(self.datamanager, "train_dataparser_outputs")
+            and "point_clouds" in self.datamanager.train_dataparser_outputs.metadata
+            and "lidars" in self.datamanager.train_dataparser_outputs.metadata
+        ):
+            points_in_world = []
+            for l2w, pc in zip(
+                self.datamanager.train_dataparser_outputs.metadata["lidars"].lidar_to_worlds,
+                self.datamanager.train_dataparser_outputs.metadata["point_clouds"],
+            ):
+                points_in_world.append(transform_points(pc[:, :3], l2w))
+            points_in_world = torch.cat([pc[:, :3] for pc in points_in_world], dim=0)
+            if (
+                "point_clouds_rgb" in self.datamanager.train_dataparser_outputs.metadata
+                and self.datamanager.train_dataparser_outputs.metadata["point_clouds_rgb"] is not None
+            ):
+                points_in_world_rgb = torch.cat(self.datamanager.train_dataparser_outputs.metadata["point_clouds_rgb"])
+            else:
+                points_in_world_rgb = torch.rand_like(points_in_world) * 255
+            seed_pts = (points_in_world, points_in_world_rgb)
+
         self.datamanager.to(device)
         # TODO(ethan): get rid of scene_bounds from the model
         assert self.datamanager.train_dataset is not None, "Missing input dataset"
 
         self._model = config.model.setup(
             scene_box=self.datamanager.train_dataset.scene_box,
-            num_train_data=len(self.datamanager.train_dataset),
+            num_train_data=self.datamanager.get_num_train_data(),
             metadata=self.datamanager.train_dataset.metadata,
             device=device,
             grad_scaler=grad_scaler,
@@ -402,7 +426,7 @@ def get_average_eval_image_metrics(
         self.train()
         return metrics_dict
 
-    def load_pipeline(self, loaded_state: Dict[str, Any], step: int) -> None:
+    def load_pipeline(self, loaded_state: Dict[str, Any], step: int, strict: bool = True) -> None:
         """Load the checkpoint from the given path
 
         Args:
@@ -413,7 +437,7 @@ def load_pipeline(self, loaded_state: Dict[str, Any], step: int) -> None:
             (key[len("module.") :] if key.startswith("module.") else key): value for key, value in loaded_state.items()
         }
         self.model.update_to_step(step)
-        self.load_state_dict(state)
+        self.load_state_dict(state, strict=strict)
 
     def get_training_callbacks(
         self, training_callback_attributes: TrainingCallbackAttributes
diff --git a/nerfstudio/pipelines/dynamic_batch.py b/nerfstudio/pipelines/dynamic_batch.py
deleted file mode 100644
index 2d0eb37c..00000000
--- a/nerfstudio/pipelines/dynamic_batch.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-A pipeline that dynamically chooses the number of rays to sample.
-"""
-
-from dataclasses import dataclass, field
-from typing import Literal, Optional, Type
-
-import torch
-from torch.cuda.amp.grad_scaler import GradScaler
-
-from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager
-from nerfstudio.pipelines.base_pipeline import VanillaPipeline, VanillaPipelineConfig
-
-
-@dataclass
-class DynamicBatchPipelineConfig(VanillaPipelineConfig):
-    """Dynamic Batch Pipeline Config"""
-
-    _target: Type = field(default_factory=lambda: DynamicBatchPipeline)
-    target_num_samples: int = 262144  # 1 << 18
-    """The target number of samples to use for an entire batch of rays."""
-    max_num_samples_per_ray: int = 1024  # 1 << 10
-    """The maximum number of samples to be placed along a ray."""
-
-
-class DynamicBatchPipeline(VanillaPipeline):
-    """Pipeline with logic for changing the number of rays per batch."""
-
-    config: DynamicBatchPipelineConfig
-    datamanager: VanillaDataManager
-    dynamic_num_rays_per_batch: int
-
-    def __init__(
-        self,
-        config: DynamicBatchPipelineConfig,
-        device: str,
-        test_mode: Literal["test", "val", "inference"] = "val",
-        world_size: int = 1,
-        local_rank: int = 0,
-        grad_scaler: Optional[GradScaler] = None,
-    ):
-        super().__init__(config, device, test_mode, world_size, local_rank)
-        assert isinstance(
-            self.datamanager, VanillaDataManager
-        ), "DynamicBatchPipeline only works with VanillaDataManager."
-
-        self.dynamic_num_rays_per_batch = self.config.target_num_samples // self.config.max_num_samples_per_ray
-        self._update_pixel_samplers()
-
-    def _update_pixel_samplers(self):
-        """Update the pixel samplers for train and eval with the dynamic number of rays per batch."""
-        if self.datamanager.train_pixel_sampler is not None:
-            self.datamanager.train_pixel_sampler.set_num_rays_per_batch(self.dynamic_num_rays_per_batch)
-        if self.datamanager.eval_pixel_sampler is not None:
-            self.datamanager.eval_pixel_sampler.set_num_rays_per_batch(self.dynamic_num_rays_per_batch)
-
-    def _update_dynamic_num_rays_per_batch(self, num_samples_per_batch: int):
-        """Updates the dynamic number of rays per batch variable,
-        based on the total number of samples in the last batch of rays."""
-        self.dynamic_num_rays_per_batch = int(
-            self.dynamic_num_rays_per_batch * (self.config.target_num_samples / num_samples_per_batch)
-        )
-
-    def get_train_loss_dict(self, step: int):
-        model_outputs, loss_dict, metrics_dict = super().get_train_loss_dict(step)
-
-        # update the number of rays for the next step
-        if "num_samples_per_batch" not in metrics_dict:
-            raise ValueError(
-                "'num_samples_per_batch' is not in metrics_dict."
-                "Please return 'num_samples_per_batch' in the models get_metrics_dict function to use this method."
-            )
-        self._update_dynamic_num_rays_per_batch(int(metrics_dict["num_samples_per_batch"]))
-        self._update_pixel_samplers()
-
-        # add the number of rays
-        assert "num_rays_per_batch" not in metrics_dict
-        assert self.datamanager.train_pixel_sampler is not None
-        metrics_dict["num_rays_per_batch"] = torch.tensor(self.datamanager.train_pixel_sampler.num_rays_per_batch)
-
-        return model_outputs, loss_dict, metrics_dict
-
-    def get_eval_loss_dict(self, step: int):
-        model_outputs, loss_dict, metrics_dict = super().get_eval_loss_dict(step)
-
-        # add the number of rays
-        assert "num_rays_per_batch" not in metrics_dict
-        assert self.datamanager.eval_pixel_sampler is not None
-        metrics_dict["num_rays_per_batch"] = torch.tensor(self.datamanager.eval_pixel_sampler.num_rays_per_batch)
-
-        return model_outputs, loss_dict, metrics_dict
diff --git a/nerfstudio/scripts/closed_loop/main.py b/nerfstudio/scripts/closed_loop/main.py
new file mode 100644
index 00000000..b4f3c590
--- /dev/null
+++ b/nerfstudio/scripts/closed_loop/main.py
@@ -0,0 +1,78 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import io
+
+import numpy as np
+import torch
+import tyro
+import uvicorn
+from fastapi import FastAPI, Response
+from PIL import Image
+from torch import Tensor
+
+from nerfstudio.scripts.closed_loop.models import ActorTrajectory, RenderInput
+from nerfstudio.scripts.closed_loop.server import ClosedLoopServer
+
+app = FastAPI()
+
+
+@app.get("/alive")
+async def alive() -> bool:
+    return True
+
+
+@app.get("/get_actors")
+async def get_actors() -> list[ActorTrajectory]:
+    """Get actor trajectories."""
+    actor_trajectories = cl_server.get_actor_trajectories()
+    actor_trajectories = [ActorTrajectory.from_torch(act_traj) for act_traj in actor_trajectories]
+    return actor_trajectories
+
+
+@app.post("/update_actors")
+async def update_actors(actor_trajectories: list[ActorTrajectory]) -> None:
+    """Update actor trajectories (keys correspond to actor uuids)."""
+    torch_actor_trajectories = [act_traj.to_torch() for act_traj in actor_trajectories]
+    cl_server.update_actor_trajectories(torch_actor_trajectories)
+
+
+@app.post("/render_image", response_class=Response, responses={200: {"content": {"image/png": {}}}})
+async def render_image(data: RenderInput) -> Response:
+    torch_pose = torch.tensor(data.pose, dtype=torch.float32)
+    render = cl_server.get_image(torch_pose, data.timestamp, data.camera_name)
+    return Response(content=_torch_to_png(render), media_type="image/png")
+
+
+@app.get("/start_time")
+async def get_start_time() -> int:
+    return int(cl_server.min_time * 1e6)
+
+
+def _torch_to_png(render: Tensor) -> bytes:
+    """Convert a torch tensor to a PNG image."""
+    img = Image.fromarray((render * 255).cpu().numpy().astype(np.uint8))
+    image_stream = io.BytesIO()
+    img.save(image_stream, format="PNG")
+    image_bytes = image_stream.getvalue()
+    return image_bytes
+
+
+if __name__ == "__main__":
+    tyro.extras.set_accent_color("bright_yellow")
+    cl_server = tyro.cli(ClosedLoopServer)
+    cl_server.main()
+    # little hacky to place host and port on the cl_server, but it makes it easier to use tyro
+    uvicorn.run(app, host=cl_server.host, port=cl_server.port)
diff --git a/nerfstudio/scripts/closed_loop/models.py b/nerfstudio/scripts/closed_loop/models.py
new file mode 100644
index 00000000..50297a0b
--- /dev/null
+++ b/nerfstudio/scripts/closed_loop/models.py
@@ -0,0 +1,68 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from typing import List, TypedDict
+
+import torch
+from pydantic import BaseModel
+from torch import Tensor
+
+
+class TrajectoryDict(TypedDict):
+    uuid: str
+    poses: Tensor
+    timestamps: Tensor
+    dims: Tensor
+
+
+class ActorTrajectory(BaseModel):
+    """Trajectory of an actor."""
+
+    uuid: str
+    """Actor UUID."""
+    poses: List[List[List[float]]]
+    """List of 4x4 actor poses."""
+    timestamps: List[int]
+    """List of timestamps in microseconds."""
+    dims: List[float]
+    """Dimensions of the actor."""
+
+    def to_torch(self) -> TrajectoryDict:
+        return {
+            "uuid": self.uuid,
+            "poses": torch.tensor(self.poses, dtype=torch.float32),
+            "timestamps": torch.tensor(self.timestamps, dtype=torch.int64),
+            "dims": torch.tensor(self.dims, dtype=torch.float32),
+        }
+
+    @classmethod
+    def from_torch(cls, data: TrajectoryDict) -> ActorTrajectory:
+        return cls(
+            uuid=data["uuid"],
+            poses=data["poses"].tolist(),
+            timestamps=data["timestamps"].tolist(),
+            dims=data["dims"].tolist(),
+        )
+
+
+class RenderInput(BaseModel):
+    """Input to the render_image endpoint."""
+
+    pose: List[List[float]]
+    """4x4 camera pose matrix"""
+    timestamp: int
+    """Timestamp in microseconds"""
+    camera_name: str
+    """Camera name"""
diff --git a/nerfstudio/scripts/closed_loop/server.py b/nerfstudio/scripts/closed_loop/server.py
new file mode 100644
index 00000000..14bf6f16
--- /dev/null
+++ b/nerfstudio/scripts/closed_loop/server.py
@@ -0,0 +1,200 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Optional
+
+import numpy as np
+import torch
+from torch import Tensor
+
+from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.data.dataparsers.ad_dataparser import OPENCV_TO_NERFSTUDIO
+from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
+from nerfstudio.models.ad_model import ADModel
+from nerfstudio.pipelines.ad_pipeline import ADPipeline
+from nerfstudio.scripts.closed_loop.models import TrajectoryDict
+from nerfstudio.scripts.render import streamline_ad_config
+from nerfstudio.utils.eval_utils import eval_setup
+from nerfstudio.utils.poses import to4x4
+
+
+@dataclass
+class ClosedLoopServer:
+    """Configuration for closed loop renderer instantiation."""
+
+    load_config: Optional[Path] = None
+    """Path to config YAML file."""
+
+    port: int = 8000
+    """Port to run the server on."""
+    host: str = "0.0.0.0"
+    """Host to run the server on."""
+
+    pad_cameras: bool = True
+    """Whether to require all cameras to have the same size."""
+    adjust_pose: bool = False
+    """Whether to adjust the height of rendered cameras to match the nearest training camera."""
+
+    @torch.no_grad()
+    def main(self):
+        assert self.load_config is not None, "Must specify a config file to load"
+        _, self.pipeline, _, _ = eval_setup(
+            self.load_config,
+            test_mode="inference",
+            update_config_callback=streamline_ad_config,
+            strict_load=False,
+        )
+        assert isinstance(self.pipeline, ADPipeline)
+        dataparser_outputs: DataparserOutputs = self.pipeline.datamanager.eval_dataparser_outputs
+        self.model: ADModel = self.pipeline.model
+        assert isinstance(self.model, ADModel), "Closed loop server only works with an ADModel"
+        self.model.eval()
+
+        assert self.pipeline.datamanager.eval_dataset is not None
+        self._train_cams = self.pipeline.datamanager.train_dataset.cameras
+        self._unique_cameras = _find_unique_cameras(self.pipeline.datamanager.eval_dataset.cameras)
+        if self.pad_cameras:
+            self._unique_cameras.height[:] = self._unique_cameras.height.max()
+            self._unique_cameras.width[:] = self._unique_cameras.width.max()
+
+        self.min_time = dataparser_outputs.time_offset
+        self.world_transform = to4x4(dataparser_outputs.dataparser_transform).to(self.model.device)
+        self.actor_transform = to4x4(dataparser_outputs.actor_transform).to(self.model.device)
+
+        sensor_idx_to_name: Dict[int, str] = dataparser_outputs.metadata["sensor_idx_to_name"]
+        self.sensor_name_to_idx = {v: k for k, v in sensor_idx_to_name.items()}
+        assert len(self.sensor_name_to_idx) == len(sensor_idx_to_name), "Sensor names must be unique"
+        self.actor_uuids = [traj["uuid"] for traj in dataparser_outputs.metadata["trajectories"]]
+        self.actor_uuid_to_idx = {uuid: idx for idx, uuid in enumerate(self.actor_uuids)}
+        assert len(self.actor_uuid_to_idx) == len(self.actor_uuids), "Actor UUIDs must be unique"
+
+    @torch.no_grad()
+    def get_image(self, pose: Tensor, timestamp: int, camera_name: str) -> Tensor:
+        """Render an image for the given pose and time.
+
+        Args:
+            pose: 4x4 camera pose matrix
+            timestamp: Timestamp in microseconds
+            camera_name: Camera name
+
+        """
+        camera_name = camera_name.lstrip("CAM_")
+        cam_idx = self.sensor_name_to_idx[camera_name]
+        cam = self._unique_cameras[cam_idx : cam_idx + 1].to(self.model.device)
+
+        pose = self.world_transform.cpu() @ pose
+        pose[:3, :3] = pose[:3, :3] @ OPENCV_TO_NERFSTUDIO
+
+        cam.camera_to_worlds = pose[:3, :4].unsqueeze(0).to(self.model.device)
+        assert cam.times is not None
+        cam.times[...] = (timestamp / 1e6) - self.min_time
+        ray_bundle = cam.generate_rays(camera_indices=0)  # cam is already the desired camera
+
+        if self.adjust_pose:
+            pos = self._train_cams.camera_to_worlds[:, :3, 3]
+            mask = (self._train_cams.metadata["sensor_idxs"] == cam_idx).squeeze(-1)
+            distances = (pose[None, :3, 3] - pos).norm(dim=-1).squeeze(-1)
+            nearest_train_cam_idx = torch.argmin(
+                torch.where(mask, distances, torch.tensor(float("inf"), device=distances.device))
+            )
+            correction_matrices = self.model.camera_optimizer(
+                torch.tensor([nearest_train_cam_idx], device=self.model.device)
+            )
+            ray_bundle.origins = ray_bundle.origins + correction_matrices[:, :3, 3]
+            ray_bundle.directions = torch.einsum("ij,...j->...i", correction_matrices[0, :3, :3], ray_bundle.directions)
+
+        return self.model.get_outputs_for_camera_ray_bundle(ray_bundle)["rgb"]
+
+    @torch.no_grad()
+    def update_actor_trajectories(self, new_trajectories: list[TrajectoryDict]):
+        """Update actor trajectories."""
+        device = self.model.device
+        modified_trajectories = []
+        actor_ids = []
+        actor_transform = self.actor_transform.to(device)
+        for traj in new_trajectories:
+            timestamps_in_seconds = traj["timestamps"].to(torch.float64) / 1e6
+            modified_trajectories.append(
+                {
+                    "poses": self.world_transform @ traj["poses"].to(device) @ actor_transform,
+                    "timestamps": (timestamps_in_seconds - self.min_time).to(device),
+                    "dims": traj["dims"].to(device),
+                    "symmetric": True,  # TODO
+                    "deformable": False,  # TODO
+                    "uuid": traj["uuid"],
+                }
+            )
+            actor_ids.append(self.actor_uuid_to_idx[traj["uuid"]])
+        old_n_actors = self.model.dynamic_actors.n_actors
+        self.model.dynamic_actors._populate_actors(modified_trajectories)
+        self.model.dynamic_actors.actor_to_id[:] = torch.tensor(actor_ids, device=device)
+        self.model.dynamic_actors.n_actors = old_n_actors  # used in hash encoding
+        self.model.dynamic_actors.to(device)  # need to move again as some tensors are created inside _populate_actors
+
+        self.actor_uuids = [traj["uuid"] for traj in new_trajectories]
+
+    @torch.no_grad()
+    def get_actor_trajectories(self) -> list[TrajectoryDict]:
+        """Get actor trajectories."""
+        poses_3x4 = self.model.dynamic_actors.get_poses_3x4()
+        poses = to4x4(poses_3x4)
+        world_inverse = self.world_transform.inverse().view(1, 4, 4)
+        timestamps = (self.model.dynamic_actors.unique_timestamps.to(torch.float64) + self.min_time) * 1e6
+        actor_transform_inv = self.actor_transform.inverse()
+        trajs = []
+        for actor_idx in range(poses.shape[1]):
+            trajs.append(
+                {
+                    "poses": (world_inverse @ poses[:, actor_idx]).cpu() @ actor_transform_inv,
+                    "timestamps": timestamps.cpu().long(),
+                    "dims": self.model.dynamic_actors.actor_sizes[actor_idx].cpu(),
+                    "uuid": self.actor_uuids[actor_idx],
+                }
+            )
+        return trajs
+
+
+class DummyClosedLoopServer(ClosedLoopServer):
+    """Dummy closed loop server for testing."""
+
+    def main(self):
+        self.min_time = 0.0
+
+    def get_image(self, pose: Tensor, timestamp: int, camera_name: str) -> Tensor:
+        return torch.zeros((512, 512, 3), dtype=torch.uint8)
+
+    def update_actor_trajectories(self, new_trajectories: list[TrajectoryDict]):
+        print("Updated actor trajectories")
+
+    def get_actor_trajectories(self) -> list[TrajectoryDict]:
+        return [
+            {
+                "poses": torch.empty((0, 4, 4)),
+                "timestamps": torch.empty(0),
+                "dims": torch.empty((0, 3)),
+                "uuid": "uuid1",
+            }
+        ]
+
+
+def _find_unique_cameras(cameras: Cameras):
+    assert "sensor_idxs" in cameras.metadata, "Cameras must have sensor_idx metadata"
+    sensor_idxs = cameras.metadata["sensor_idxs"]
+    print(f"Found {len(sensor_idxs)} cameras, with unique indices: {torch.unique(sensor_idxs).tolist()}")
+    # need to go through numpy as torch.unique doesn't support return_index
+    first_unique_idx = torch.from_numpy(np.unique(sensor_idxs.numpy(), return_index=True)[1])
+    return cameras[first_unique_idx]
diff --git a/nerfstudio/scripts/eval.py b/nerfstudio/scripts/eval.py
index 103467bb..5d78b09e 100644
--- a/nerfstudio/scripts/eval.py
+++ b/nerfstudio/scripts/eval.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,6 +26,7 @@
 
 import tyro
 
+from nerfstudio.engine.trainer import TrainerConfig
 from nerfstudio.utils.eval_utils import eval_setup
 from nerfstudio.utils.rich_utils import CONSOLE
 
@@ -39,10 +41,12 @@ class ComputePSNR:
     output_path: Path = Path("output.json")
     # Optional path to save rendered outputs to.
     render_output_path: Optional[Path] = None
+    # Optional path to update the data root to
+    data_root_path: Optional[Path] = None
 
     def main(self) -> None:
         """Main function."""
-        config, pipeline, checkpoint_path, _ = eval_setup(self.load_config)
+        config, pipeline, checkpoint_path, _ = eval_setup(self.load_config, update_config_callback=self.update_config)
         assert self.output_path.suffix == ".json"
         if self.render_output_path is not None:
             self.render_output_path.mkdir(parents=True, exist_ok=True)
@@ -59,6 +63,11 @@ def main(self) -> None:
         self.output_path.write_text(json.dumps(benchmark_info, indent=2), "utf8")
         CONSOLE.print(f"Saved results to: {self.output_path}")
 
+    def update_config(self, config: TrainerConfig) -> TrainerConfig:
+        config.pipeline.datamanager.dataparser.data = self.data_root_path
+
+        return config
+
 
 def entrypoint():
     """Entrypoint for use with pyproject scripts."""
diff --git a/nerfstudio/scripts/licensing/check_copyright.py b/nerfstudio/scripts/licensing/check_copyright.py
new file mode 100644
index 00000000..f920c7d7
--- /dev/null
+++ b/nerfstudio/scripts/licensing/check_copyright.py
@@ -0,0 +1,75 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import datetime
+import subprocess
+import sys
+
+CURRENT_YEAR = str(datetime.datetime.now().year)
+COPYRIGHT_STR = f"# Copyright {CURRENT_YEAR} the authors of NeuRAD and contributors.\n"
+LICENSE_HEADER = """\
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+
+def count_copyrights(lines):
+    count = 0
+    for line in lines:
+        if line.startswith("# Copyright"):
+            count += 1
+        else:
+            break
+    return count
+
+
+def main():
+    files = sys.argv[1:]
+    for file_name in files:
+        if file_name.startswith("tests"):  # exclude tests directory
+            continue
+        with open(file_name, "r") as f:
+            lines = f.readlines()
+
+            if lines[0] != COPYRIGHT_STR:
+                git_status = subprocess.run(
+                    ["git", "status", "--porcelain", "--", file_name],
+                    check=True,
+                    encoding="utf-8",
+                    stdout=subprocess.PIPE,
+                ).stdout[0]
+                if git_status == "A" and lines[0].startswith("# Copyright"):
+                    lines[0] = COPYRIGHT_STR
+                else:
+                    lines.insert(0, COPYRIGHT_STR)
+
+            if LICENSE_HEADER not in "".join(lines):
+                lines.insert(count_copyrights(lines), LICENSE_HEADER)
+
+        with open(file_name, "w") as f:
+            f.writelines(lines)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/nerfstudio/scripts/licensing/copyright.txt b/nerfstudio/scripts/licensing/copyright.txt
deleted file mode 100644
index 40c37329..00000000
--- a/nerfstudio/scripts/licensing/copyright.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
diff --git a/nerfstudio/scripts/licensing/license_headers.sh b/nerfstudio/scripts/licensing/license_headers.sh
deleted file mode 100755
index 286d7ffb..00000000
--- a/nerfstudio/scripts/licensing/license_headers.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/bin/bash
-
-VALID_ARGS=$(getopt -o c --long check -- "$@")
-
-eval set -- "$VALID_ARGS"
-check=false
-while [ : ]; do
-  case "$1" in
-    -c | --check)
-        check=true
-        shift
-        ;;
-    --) shift;
-        break 
-        ;;
-  esac
-done
-
-check_failed=false
-added_headers=false
-for i in $(find nerfstudio/ -name '*.py');
-do
-  if ! grep -q Copyright $i
-  then
-    if [ "$check" = true ];
-      then
-        echo "$i missing copyright header"
-        check_failed=true
-      else
-        cat nerfstudio/scripts/licensing/copyright.txt $i >$i.new && mv $i.new $i
-        echo "Adding license header to $i."
-      fi
-    added_headers=true
-  fi
-done
-
-if [ "$check_failed" = true ];
-  then
-    echo "Run '.nerfstudio/scripts/licensing/license_headers.sh to add missing headers.'"
-    exit 1
-fi
-
-if [ "$added_headers" = false ];
-  then
-    echo "No missing license headers found."
-fi
-
-exit 0
\ No newline at end of file
diff --git a/nerfstudio/scripts/render.py b/nerfstudio/scripts/render.py
index c2d6d83c..13b29f69 100644
--- a/nerfstudio/scripts/render.py
+++ b/nerfstudio/scripts/render.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,18 +17,20 @@
 """
 render.py
 """
+
 from __future__ import annotations
 
 import gzip
 import json
 import os
+import pickle
 import shutil
 import struct
 import sys
 from contextlib import ExitStack, contextmanager
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 import mediapy as media
 import numpy as np
@@ -42,7 +45,12 @@
 from torch import Tensor
 from typing_extensions import Annotated
 
-from nerfstudio.cameras.camera_paths import get_interpolated_camera_path, get_path_from_json, get_spiral_path
+from nerfstudio.cameras.camera_paths import (
+    get_interpolated_camera_path,
+    get_interpolated_spiral_camera_path,
+    get_path_from_json,
+    get_spiral_path,
+)
 from nerfstudio.cameras.cameras import Cameras, CameraType, RayBundle
 from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager, VanillaDataManagerConfig
 from nerfstudio.data.datamanagers.full_images_datamanager import FullImageDatamanagerConfig
@@ -446,6 +454,7 @@ def main(self) -> None:
             self.load_config,
             eval_num_rays_per_chunk=self.eval_num_rays_per_chunk,
             test_mode="inference",
+            update_config_callback=streamline_ad_config,
         )
 
         install_checks.check_ffmpeg_installed()
@@ -581,56 +590,195 @@ def main(self) -> None:
 class RenderInterpolated(BaseRender):
     """Render a trajectory that interpolates between training or eval dataset images."""
 
-    pose_source: Literal["eval", "train"] = "eval"
+    pose_source: Literal["eval", "train", "train+eval"] = "eval"
     """Pose source to render."""
     interpolation_steps: int = 10
     """Number of interpolation steps between eval dataset cameras."""
     order_poses: bool = False
     """Whether to order camera poses by proximity."""
+    sensor_index: Optional[int] = None
+    """Sensor index to render. If None, render all sensors."""
     frame_rate: int = 24
     """Frame rate of the output video."""
     output_format: Literal["images", "video"] = "video"
     """How to save output data."""
 
+    spiral_radius: float = 0.0
+    """Radius of the spiral."""
+    spiral_rotations: float = 1.0
+    """Number of rotations of the spiral."""
+
+    shift: Tuple[float, ...] = (0.0, 0.0, 0.0)
+    """Shift to apply to the camera pose."""
+    shift_time: float = 0.0
+    """Time at which to apply the shift."""
+    shift_steps: int = 0
+    """Number of steps to interpolate the shift over."""
+
+    actor_shift: Tuple[float, ...] = (0.0, 0.0, 0.0)
+    """Shift to apply to all actor poses."""
+    actor_removal_time: Optional[float] = None
+    """Time at which to remove all actors."""
+    actor_stop_time: Optional[float] = 3.2
+    """Time at which to stop all actors."""
+
     def main(self) -> None:
         """Main function."""
         _, pipeline, _, _ = eval_setup(
             self.load_config,
             eval_num_rays_per_chunk=self.eval_num_rays_per_chunk,
             test_mode="test",
+            update_config_callback=streamline_ad_config,
         )
 
+        if self.spiral_radius and any(self.shift):
+            CONSOLE.print(
+                "Warning: Rendering with both spiral and shift is not supported. Only spiral will be rendered."
+            )
+
         install_checks.check_ffmpeg_installed()
 
         if self.pose_source == "eval":
             assert pipeline.datamanager.eval_dataset is not None
             cameras = pipeline.datamanager.eval_dataset.cameras
-        else:
+        elif self.pose_source == "train":
             assert pipeline.datamanager.train_dataset is not None
             cameras = pipeline.datamanager.train_dataset.cameras
+        else:
+            assert pipeline.datamanager.train_dataset is not None
+            assert pipeline.datamanager.eval_dataset is not None
+            cameras = pipeline.datamanager.train_dataset.cameras.cat([pipeline.datamanager.eval_dataset.cameras])
 
-        seconds = self.interpolation_steps * len(cameras) / self.frame_rate
-        camera_path = get_interpolated_camera_path(
-            cameras=cameras,
-            steps=self.interpolation_steps,
-            order_poses=self.order_poses,
-        )
+        if cameras.times is not None:
+            cameras = cameras[torch.argsort(cameras.times.squeeze(-1))]
 
-        _render_trajectory_video(
-            pipeline,
-            camera_path,
-            output_filename=self.output_path,
-            rendered_output_names=self.rendered_output_names,
-            rendered_resolution_scaling_factor=1.0 / self.downscale_factor,
-            seconds=seconds,
-            output_format=self.output_format,
-            image_format=self.image_format,
-            depth_near_plane=self.depth_near_plane,
-            depth_far_plane=self.depth_far_plane,
-            colormap_options=self.colormap_options,
-            render_nearest_camera=self.render_nearest_camera,
-            check_occlusions=self.check_occlusions,
+        if cameras.metadata and "sensor_idxs" in cameras.metadata:
+            sensor_indices = (
+                torch.tensor(self.sensor_index).unsqueeze(0)
+                if self.sensor_index is not None
+                else cameras.metadata["sensor_idxs"].unique()
+            )
+        else:
+            sensor_indices = torch.tensor([0]).unsqueeze(0)
+            cameras.metadata["sensor_idxs"] = torch.zeros_like(cameras.camera_type, dtype=torch.int64)
+
+        modify_actors(pipeline, self.actor_shift, self.actor_removal_time, self.actor_stop_time)
+
+        for sensor_index in sensor_indices:
+            mask = (cameras.metadata["sensor_idxs"] == sensor_index).squeeze(-1)
+            curr_cameras = cameras[mask]
+
+            seconds = self.interpolation_steps * len(curr_cameras) / self.frame_rate
+            if self.spiral_radius:
+                camera_path = get_interpolated_spiral_camera_path(
+                    cameras=curr_cameras,
+                    steps=self.interpolation_steps,
+                    radius=self.spiral_radius,
+                    rotations=self.spiral_rotations,
+                )
+            elif any(self.shift):
+                camera_path = get_shifted_camera_path(
+                    cameras=curr_cameras,
+                    shift=self.shift,
+                    shift_time=self.shift_time,
+                    shift_steps=self.shift_steps,
+                    interpolation_steps=self.interpolation_steps,
+                )
+            else:
+                camera_path = get_interpolated_camera_path(
+                    cameras=curr_cameras,
+                    steps=self.interpolation_steps,
+                    order_poses=self.order_poses,
+                )
+                if curr_cameras.times is not None:
+                    times, stepsize = curr_cameras.times[..., 0], 1.0 / self.interpolation_steps
+                    camera_path.times = torch.from_numpy(
+                        np.interp(
+                            np.append(np.arange(0, len(times) - 1, stepsize), len(times) - 1),
+                            np.arange(len(times)),
+                            times,
+                        )[..., None]
+                    ).float()
+
+            camera_path.metadata = camera_path.metadata or {}
+            if curr_cameras.metadata and "sensor_idxs" in curr_cameras.metadata:
+                camera_path.metadata["sensor_idxs"] = torch.full_like(camera_path.width, sensor_index.item())
+
+            print("Rendering sensor index ", sensor_index.item())
+            _render_trajectory_video(
+                pipeline,
+                camera_path,
+                output_filename=self.output_path.with_stem(f"{self.output_path.stem}_sensor_idx_{sensor_index}"),
+                rendered_output_names=self.rendered_output_names,
+                rendered_resolution_scaling_factor=1.0 / self.downscale_factor,
+                seconds=seconds,
+                output_format=self.output_format,
+                image_format=self.image_format,
+                depth_near_plane=self.depth_near_plane,
+                depth_far_plane=self.depth_far_plane,
+                colormap_options=self.colormap_options,
+                render_nearest_camera=self.render_nearest_camera,
+                check_occlusions=self.check_occlusions,
+            )
+
+
+def modify_actors(pipeline, actor_shift, actor_removal_time, actor_stop_time):
+    actor_shift = torch.nn.Parameter(torch.tensor(actor_shift, dtype=torch.float32, device=pipeline.model.device))
+    with torch.no_grad():
+        pipeline.model.dynamic_actors.actor_positions += actor_shift
+        if actor_removal_time is not None:
+            no_actor_mask = pipeline.model.dynamic_actors.unique_timestamps > actor_removal_time
+            pipeline.model.dynamic_actors.actor_present_at_time[no_actor_mask, :] = False
+        if actor_stop_time is not None:
+            actor_stop_idx = torch.searchsorted(pipeline.model.dynamic_actors.unique_timestamps, actor_stop_time)
+            freeze_position = pipeline.model.dynamic_actors.actor_positions[actor_stop_idx].unsqueeze(0)
+            freeze_rotation = pipeline.model.dynamic_actors.actor_rotations_6d[actor_stop_idx].unsqueeze(0)
+            pipeline.model.dynamic_actors.actor_positions[actor_stop_idx:] = freeze_position
+            pipeline.model.dynamic_actors.actor_rotations_6d[actor_stop_idx:] = freeze_rotation
+
+
+def get_shifted_camera_path(cameras, shift, shift_time, shift_steps, interpolation_steps):
+    if cameras.times is not None:
+        # find index of time closest to shift_time
+        shift_idx = torch.argmin(torch.abs(cameras.times - shift_time))
+    else:
+        # warn user that we are assuming shift_time is the middle of the trajectory
+        CONSOLE.print(
+            "Warning: Assuming shift_time is the middle of the trajectory. "
+            "If this is not the case, please specify times in the camera path JSON."
         )
+        shift_idx = int(shift_time * len(cameras))
+    pre_shift_cams = cameras[:shift_idx]
+    post_shift_cams = cameras[shift_idx:]
+    post_shift_cams.camera_to_worlds = post_shift_cams.camera_to_worlds.clone()
+    post_shift_cams.camera_to_worlds[..., :3, 3] = post_shift_cams.camera_to_worlds[..., :3, 3] + torch.tensor(shift)
+    post_shift_camera_path = get_interpolated_camera_path(post_shift_cams, steps=interpolation_steps, order_poses=False)
+    if (times := post_shift_cams.times) is not None:
+        post_shift_camera_path.times = torch.from_numpy(
+            np.interp(
+                np.append(np.arange(0, len(times) - 1, 1 / interpolation_steps), len(times) - 1),
+                np.arange(len(times)),
+                times.squeeze(-1),
+            )[..., None]
+        ).float()
+
+    if len(pre_shift_cams) == 0:
+        return post_shift_camera_path
+
+    pre_shift_camera_path = get_interpolated_camera_path(pre_shift_cams, steps=interpolation_steps, order_poses=False)
+    mid_shift_camera_path = get_interpolated_camera_path(
+        pre_shift_cams[-1:].cat([post_shift_cams[:1]]), steps=shift_steps, order_poses=False
+    )
+    if (times := pre_shift_cams.times) is not None:
+        pre_shift_camera_path.times = torch.from_numpy(
+            np.interp(
+                np.append(np.arange(0, len(times) - 1, 1 / interpolation_steps), len(times) - 1),
+                np.arange(len(times)),
+                times.squeeze(-1),
+            )[..., None]
+        ).float()
+        mid_shift_camera_path.times = torch.full_like(mid_shift_camera_path.cy, pre_shift_camera_path.times[-1].item())
+    return pre_shift_camera_path.cat([mid_shift_camera_path, post_shift_camera_path])
 
 
 @dataclass
@@ -652,6 +800,7 @@ def main(self) -> None:
             self.load_config,
             eval_num_rays_per_chunk=self.eval_num_rays_per_chunk,
             test_mode="test",
+            update_config_callback=streamline_ad_config,
         )
 
         install_checks.check_ffmpeg_installed()
@@ -703,21 +852,46 @@ def _disable_datamanager_setup(cls):
 class DatasetRender(BaseRender):
     """Render all images in the dataset."""
 
+    pose_source: Literal["train", "val", "test", "train+test", "train+val"] = "test"
+    """Split to render."""
     output_path: Path = Path("renders")
     """Path to output video file."""
     data: Optional[Path] = None
     """Override path to the dataset."""
+    config_output_dir: Optional[Path] = None
+    """Override the config output dir. Used to load the model."""
     downscale_factor: Optional[float] = None
     """Scaling factor to apply to the camera image resolution."""
-    split: Literal["train", "val", "test", "train+test"] = "test"
-    """Split to render."""
-    rendered_output_names: Optional[List[str]] = field(default_factory=lambda: None)
+    rendered_output_names: List[str] = field(default_factory=lambda: ["all"])
     """Name of the renderer outputs to use. rgb, depth, raw-depth, gt-rgb etc. By default all outputs are rendered."""
+    strict_load: bool = True
+    """Whether to strictly load the config."""
+    load_ignore_keys: Optional[List[str]] = field(
+        default_factory=lambda: []
+    )  # e.g. ["model.camera_optimizer.pose_adjustment", "_model.camera_optimizer.pose_adjustment"]
+    """Keys to ignore when loading the config."""
+
+    render_height: Optional[int] = None
+    """Height to render the images at."""
+    render_width: Optional[int] = None
+    """Width to render the images at."""
+    output_height: Optional[int] = None
+    """Height to crop the output images at."""
+    output_width: Optional[int] = None
+    """Width to crop the output images at."""
+
+    shift: Tuple[float, float, float] = (0, 0, 0)
+    """Shift to apply to the camera pose."""
+    calculate_and_save_metrics: bool = False
+    """Whether to calculate and save metrics."""
+    metrics_filename: Path = Path("metrics.pkl")
+    """Filename to save the metrics to."""
 
     def main(self):
         config: TrainerConfig
 
         def update_config(config: TrainerConfig) -> TrainerConfig:
+            config = streamline_ad_config(config)
             data_manager_config = config.pipeline.datamanager
             assert isinstance(data_manager_config, (VanillaDataManagerConfig, FullImageDatamanagerConfig))
             data_manager_config.eval_num_images_to_sample_from = -1
@@ -727,9 +901,13 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
                 data_manager_config.train_num_times_to_repeat_images = -1
             if self.data is not None:
                 data_manager_config.data = self.data
+            if self.config_output_dir is not None:
+                config.output_dir = self.config_output_dir
             if self.downscale_factor is not None:
                 assert hasattr(data_manager_config.dataparser, "downscale_factor")
                 setattr(data_manager_config.dataparser, "downscale_factor", self.downscale_factor)
+            # Remove any frame limit on the the dataparser
+            config.pipeline.datamanager.dataparser.max_eval_frames = None
             return config
 
         config, pipeline, _, _ = eval_setup(
@@ -737,11 +915,15 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
             eval_num_rays_per_chunk=self.eval_num_rays_per_chunk,
             test_mode="inference",
             update_config_callback=update_config,
+            strict_load=self.strict_load,
+            ignore_keys=self.load_ignore_keys,
         )
         data_manager_config = config.pipeline.datamanager
         assert isinstance(data_manager_config, (VanillaDataManagerConfig, FullImageDatamanagerConfig))
 
-        for split in self.split.split("+"):
+        self.output_path.mkdir(exist_ok=True, parents=True)
+        metrics_out = dict()
+        for split in self.pose_source.split("+"):
             datamanager: VanillaDataManager
             dataset: Dataset
             if split == "train":
@@ -758,8 +940,27 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
                 dataparser_outputs = getattr(dataset, "_dataparser_outputs", None)
                 if dataparser_outputs is None:
                     dataparser_outputs = datamanager.dataparser.get_dataparser_outputs(split=datamanager.test_split)
+            dataset.cameras.height = (
+                torch.full_like(dataset.cameras.height, self.render_height)
+                if self.render_height is not None
+                else dataset.cameras.height
+            )
+            dataset.cameras.width = (
+                torch.full_like(dataset.cameras.width, self.render_width)
+                if self.render_width is not None
+                else dataset.cameras.width
+            )
+            shift_relative_to_cam = torch.tensor(self.shift, dtype=torch.float32)
+            # add homogenous point
+            shift_relative_to_cam = torch.cat([shift_relative_to_cam, torch.tensor([1.0], dtype=torch.float32)])
+            shift_relative_to_cam = shift_relative_to_cam.to(dataset.cameras.camera_to_worlds.device)
+            # shift the camera poses
+            dataset.cameras.camera_to_worlds[..., :3, 3:4] = (
+                dataset.cameras.camera_to_worlds @ shift_relative_to_cam.reshape(1, 4, 1)
+            )
+
             dataloader = FixedIndicesEvalDataloader(
-                input_dataset=dataset,
+                dataset=dataset,
                 device=datamanager.device,
                 num_workers=datamanager.world_size * 4,
             )
@@ -776,9 +977,33 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
                 TimeElapsedColumn(),
             ) as progress:
                 for camera_idx, (camera, batch) in enumerate(progress.track(dataloader, total=len(dataset))):
+                    # Try to get the original filename
+                    image_name = (
+                        Path(dataparser_outputs.image_filenames[camera_idx]).with_suffix("").relative_to(images_root)
+                    )
+
                     with torch.no_grad():
                         outputs = pipeline.model.get_outputs_for_camera(camera)
 
+                    if self.output_height is not None:
+                        dataset.cameras.height[batch["image_idx"]] = torch.full_like(
+                            dataset.cameras.height[0:1], self.output_height
+                        )
+                        batch["image"] = batch["image"][..., : self.output_height, :, :]
+                        outputs["rgb"] = outputs["rgb"][..., : self.output_height, :, :]
+
+                    if self.output_width is not None:
+                        dataset.cameras.width[batch["image_idx"]] = torch.full_like(
+                            dataset.cameras.width[0:1], self.output_width
+                        )
+                        batch["image"] = batch["image"][..., : self.output_width, :]
+                        outputs["rgb"] = outputs["rgb"][..., : self.output_width, :]
+
+                    if self.calculate_and_save_metrics:
+                        with torch.no_grad():
+                            metrics_dict, _ = pipeline.model.get_image_metrics_and_images(outputs, batch)
+                            metrics_out[str(image_name)] = metrics_dict
+
                     gt_batch = batch.copy()
                     gt_batch["rgb"] = gt_batch.pop("image")
                     all_outputs = (
@@ -788,8 +1013,10 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
                         + [f"raw-gt-{x}" for x in gt_batch.keys()]
                     )
                     rendered_output_names = self.rendered_output_names
-                    if rendered_output_names is None:
+                    if "all" in rendered_output_names:
                         rendered_output_names = ["gt-rgb"] + list(outputs.keys())
+                    elif rendered_output_names == ["none"]:
+                        rendered_output_names = []
                     for rendered_output_name in rendered_output_names:
                         if rendered_output_name not in all_outputs:
                             CONSOLE.rule("Error", style="red")
@@ -803,10 +1030,6 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
 
                         is_raw = False
                         is_depth = rendered_output_name.find("depth") != -1
-                        image_name = f"{camera_idx:05d}"
-
-                        # Try to get the original filename
-                        image_name = dataparser_outputs.image_filenames[camera_idx].relative_to(images_root)
 
                         output_path = self.output_path / split / rendered_output_name / image_name
                         output_path.parent.mkdir(exist_ok=True, parents=True)
@@ -857,29 +1080,58 @@ def update_config(config: TrainerConfig) -> TrainerConfig:
                             )
 
                         # Save to file
+                        height = (
+                            min(output_image.shape[0], self.output_height)
+                            if self.output_height
+                            else output_image.shape[0]
+                        )
+                        width = (
+                            min(output_image.shape[1], self.output_width)
+                            if self.output_width
+                            else output_image.shape[1]
+                        )
+                        output_image = output_image[:height, :width]
                         if is_raw:
-                            with gzip.open(output_path.with_suffix(".npy.gz"), "wb") as f:
+                            with gzip.open(output_path.parent / (output_path.name + ".npy.gz"), "wb") as f:
                                 np.save(f, output_image)
                         elif self.image_format == "png":
-                            media.write_image(output_path.with_suffix(".png"), output_image, fmt="png")
+                            media.write_image(output_path.parent / (output_path.name + ".png"), output_image, fmt="png")
                         elif self.image_format == "jpeg":
                             media.write_image(
-                                output_path.with_suffix(".jpg"), output_image, fmt="jpeg", quality=self.jpeg_quality
+                                output_path.parent / (output_path.name + ".jpg"),
+                                output_image,
+                                fmt="jpeg",
+                                quality=self.jpeg_quality,
                             )
                         else:
                             raise ValueError(f"Unknown image format {self.image_format}")
 
+        if self.calculate_and_save_metrics:
+            metrics_out_path = Path(self.output_path, self.metrics_filename)
+            with open(metrics_out_path, "wb") as f:
+                pickle.dump(metrics_out, f)
+            CONSOLE.print(f"[bold][green]:glowing_star: Metrics saved to {metrics_out_path}")
+
         table = Table(
             title=None,
             show_header=False,
             box=box.MINIMAL,
             title_style=style.Style(bold=True),
         )
-        for split in self.split.split("+"):
+        for split in self.pose_source.split("+"):
             table.add_row(f"Outputs {split}", str(self.output_path / split))
         CONSOLE.print(Panel(table, title="[bold][green]:tada: Render on split {} Complete :tada:[/bold]", expand=False))
 
 
+def streamline_ad_config(config):
+    if getattr(config.pipeline.datamanager, "num_processes", None):
+        config.pipeline.datamanager.num_processes = 0
+    config.pipeline.model.eval_num_rays_per_chunk = 2**17
+    if getattr(config.pipeline.datamanager.dataparser, "add_missing_points", None):
+        config.pipeline.datamanager.dataparser.add_missing_points = False
+    return config
+
+
 Commands = tyro.conf.FlagConversionOff[
     Union[
         Annotated[RenderCameraPath, tyro.conf.subcommand(name="camera-path")],
diff --git a/nerfstudio/scripts/train.py b/nerfstudio/scripts/train.py
index fde0a5c3..e4501dbe 100644
--- a/nerfstudio/scripts/train.py
+++ b/nerfstudio/scripts/train.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -51,10 +52,16 @@
 from datetime import timedelta
 from typing import Any, Callable, Literal, Optional
 
+import torch.multiprocessing as mp
+
+try:
+    mp.set_start_method("spawn")
+except RuntimeError:
+    pass
+
 import numpy as np
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
 import tyro
 import yaml
 
diff --git a/nerfstudio/scripts/viewer/run_viewer.py b/nerfstudio/scripts/viewer/run_viewer.py
index 79e7167f..f1b80af4 100644
--- a/nerfstudio/scripts/viewer/run_viewer.py
+++ b/nerfstudio/scripts/viewer/run_viewer.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -58,11 +59,21 @@ class RunViewer:
 
     def main(self) -> None:
         """Main function."""
+
+        def disable_mp_in_config(config):
+            if getattr(config.pipeline.datamanager, "num_processes", None):
+                config.pipeline.datamanager.num_processes = 0
+            if getattr(config.pipeline.datamanager.dataparser, "add_missing_points", None):
+                config.pipeline.datamanager.dataparser.add_missing_points = False
+            return config
+
         config, pipeline, _, step = eval_setup(
             self.load_config,
             eval_num_rays_per_chunk=None,
             test_mode="test",
+            update_config_callback=disable_mp_in_config,
         )
+
         num_rays_per_chunk = config.viewer.num_rays_per_chunk
         assert self.viewer.num_rays_per_chunk == -1
         config.vis = self.vis
@@ -111,7 +122,7 @@ def _start_viewer(config: TrainerConfig, pipeline: Pipeline, step: int):
     config.logging.local_writer.enable = False
     writer.setup_local_writer(config.logging, max_iter=config.max_num_iterations, banner_messages=banner_messages)
 
-    assert viewer_state and pipeline.datamanager.train_dataset
+    assert viewer_state and pipeline.datamanager.train_dataset is not None
     viewer_state.init_scene(
         train_dataset=pipeline.datamanager.train_dataset,
         train_state="completed",
diff --git a/nerfstudio/utils/colormaps.py b/nerfstudio/utils/colormaps.py
index d772d40b..69fda11c 100644
--- a/nerfstudio/utils/colormaps.py
+++ b/nerfstudio/utils/colormaps.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -41,6 +42,10 @@ class ColormapOptions:
     """ Maximum value for the output colormap """
     invert: bool = False
     """ Whether to invert the output colormap """
+    range_min: Optional[float] = None
+    """ Minimum value for the input tensor image """
+    range_max: Optional[float] = None
+    """ Maximum value for the input tensor image """
 
 
 def apply_colormap(
@@ -66,6 +71,11 @@ def apply_colormap(
     if image.shape[-1] == 3:
         return image
 
+    # clip to range
+    min_range = colormap_options.range_min if colormap_options.range_min is not None else torch.min(image)
+    max_range = colormap_options.range_max if colormap_options.range_max is not None else torch.max(image)
+    image = torch.clip(image, min_range, max_range)
+
     # rendering depth outputs
     if image.shape[-1] == 1 and torch.is_floating_point(image):
         output = image
diff --git a/nerfstudio/utils/eval_utils.py b/nerfstudio/utils/eval_utils.py
index 2c4bfcd1..89f29f8d 100644
--- a/nerfstudio/utils/eval_utils.py
+++ b/nerfstudio/utils/eval_utils.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,7 +21,7 @@
 import os
 import sys
 from pathlib import Path
-from typing import Callable, Literal, Optional, Tuple
+from typing import Callable, List, Literal, Optional, Tuple
 
 import torch
 import yaml
@@ -31,7 +32,9 @@
 from nerfstudio.utils.rich_utils import CONSOLE
 
 
-def eval_load_checkpoint(config: TrainerConfig, pipeline: Pipeline) -> Tuple[Path, int]:
+def eval_load_checkpoint(
+    config: TrainerConfig, pipeline: Pipeline, strict: bool = True, ignore_keys: List = []
+) -> Tuple[Path, int]:
     ## TODO: ideally eventually want to get this to be the same as whatever is used to load train checkpoint too
     """Helper function to load checkpointed pipeline
 
@@ -59,7 +62,10 @@ def eval_load_checkpoint(config: TrainerConfig, pipeline: Pipeline) -> Tuple[Pat
     load_path = config.load_dir / f"step-{load_step:09d}.ckpt"
     assert load_path.exists(), f"Checkpoint {load_path} does not exist"
     loaded_state = torch.load(load_path, map_location="cpu")
-    pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"])
+    for key in ignore_keys:
+        loaded_state["pipeline"].pop(key, None)
+
+    pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"], strict)
     CONSOLE.print(f":white_check_mark: Done loading checkpoint from {load_path}")
     return load_path, load_step
 
@@ -69,6 +75,8 @@ def eval_setup(
     eval_num_rays_per_chunk: Optional[int] = None,
     test_mode: Literal["test", "val", "inference"] = "test",
     update_config_callback: Optional[Callable[[TrainerConfig], TrainerConfig]] = None,
+    strict_load: bool = True,
+    ignore_keys: List = [],
 ) -> Tuple[TrainerConfig, Pipeline, Path, int]:
     """Shared setup for loading a saved pipeline for evaluation.
 
@@ -107,6 +115,6 @@ def eval_setup(
     pipeline.eval()
 
     # load checkpointed information
-    checkpoint_path, step = eval_load_checkpoint(config, pipeline)
+    checkpoint_path, step = eval_load_checkpoint(config, pipeline, strict_load, ignore_keys)
 
     return config, pipeline, checkpoint_path, step
diff --git a/nerfstudio/utils/math.py b/nerfstudio/utils/math.py
index 0ba9e6a5..7c9b11de 100644
--- a/nerfstudio/utils/math.py
+++ b/nerfstudio/utils/math.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,13 +18,14 @@
 import itertools
 import math
 from dataclasses import dataclass
-from typing import Literal, Tuple
+from typing import List, Literal, Optional, Tuple
 
 import torch
 from jaxtyping import Bool, Float
 from torch import Tensor
 
 from nerfstudio.data.scene_box import OrientedBox
+from nerfstudio.utils.misc import torch_compile
 
 
 def components_from_spherical_harmonics(
@@ -104,6 +106,44 @@ class Gaussians:
     mean: Float[Tensor, "*batch dim"]
     cov: Float[Tensor, "*batch dim dim"]
 
+    def to_std(self):
+        """Converts Gaussians to GaussiansStd"""
+        return GaussiansStd(mean=self.mean, std=torch.sqrt(torch.diagonal(self.cov, dim1=-2, dim2=-1)))
+
+
+@dataclass
+class GaussiansStd:
+    """Stores Gaussians
+
+    Args:
+        mean: Mean of multivariate Gaussian
+        std: Standard deviation of multivariate Gaussian.
+    """
+
+    mean: Float[Tensor, "*batch dim"]
+    std: Float[Tensor, "*batch 1"]
+
+    def nelement(self) -> int:
+        return self.mean.nelement()
+
+    def __getitem__(self, index: int) -> "GaussiansStd":
+        return GaussiansStd(mean=self.mean[index], std=self.std[index])
+
+    def __len__(self) -> int:
+        return len(self.mean)
+
+    def cat(self, other: List["GaussiansStd"]) -> "GaussiansStd":
+        means = [self.mean] + [o.mean for o in other]
+        stds = [self.std] + [o.std for o in other]
+        return GaussiansStd(
+            mean=torch.cat(means, dim=0),
+            std=torch.cat(stds, dim=0),
+        )
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.mean.dtype
+
 
 def compute_3d_gaussian(
     directions: Float[Tensor, "*batch 3"],
@@ -187,6 +227,99 @@ def conical_frustum_to_gaussian(
     return compute_3d_gaussian(directions, means, dir_variance, radius_variance)
 
 
+def multisampled_frustum_to_gaussian(
+    origins: Float[Tensor, "*batch num_samples 3"],
+    directions: Float[Tensor, "*batch num_samples 3"],
+    starts: Float[Tensor, "*batch num_samples 1"],
+    ends: Float[Tensor, "*batch num_samples 1"],
+    radius: Float[Tensor, "*batch num_samples 1"],
+    rand: bool = True,
+    std_scale: float = 0.5,
+    eps: float = 1e-10,
+) -> GaussiansStd:
+    """Approximates frustums with a Gaussian distributions via multisampling.
+    Proposed in ZipNeRF https://arxiv.org/pdf/2304.06706.pdf
+    Taken from https://github.com/SuLvXiangXin/zipnerf-pytorch/blob/b1cb42943d244301a013bd53f9cb964f576b0af4/internal/render.py#L92
+    Args:
+        origins: Origins of cones.
+        directions: Direction (axis) of frustums.
+        starts: Start of frustums.
+        ends: End of frustums.
+        radius: Radii of cone a distance of 1 from the origin.
+        rand: Whether should add noise to points or not.
+        std_scale: Standard deviation scale parameter.
+        eps: Small number.
+    Returns:
+        GaussiansStd: Approximation of frustums via multisampling
+    """
+
+    # middle points
+    t_m = (starts + ends) / 2.0
+    # half of the width
+    t_d = (ends - starts) / 2.0
+
+    # prepare 6-point hexagonal pattern for each sample
+    j = torch.arange(6, device=starts.device, dtype=starts.dtype)
+    t = starts + t_d / (t_d**2 + 3 * t_m**2) * (
+        ends**2 + 2 * t_m**2 + 3 / 7**0.5 * (2 * j / 5 - 1) * ((t_d**2 - t_m**2) ** 2 + 4 * t_m**4).sqrt()
+    )  # [..., num_samples, 6]
+
+    deg = torch.pi / 3 * starts.new_tensor([0, 2, 4, 3, 5, 1]).broadcast_to(t.shape)
+    if rand:
+        # randomly rotate and flip
+        mask = torch.rand_like(starts) > 0.5  # [..., num_samples, 1]
+        deg = deg + 2 * torch.pi * torch.rand_like(starts)
+        deg = torch.where(mask, deg, 5 * torch.pi / 3.0 - deg)
+    else:
+        # rotate 30 degree and flip every other pattern
+        mask = (
+            (
+                torch.arange(
+                    end=starts.shape[-2],
+                    device=starts.device,
+                    dtype=starts.dtype,
+                )
+                % 2
+                == 0
+            )
+            .unsqueeze(-1)
+            .broadcast_to(starts.shape)
+        )  # [..., num_samples, 6]
+        deg = torch.where(mask, deg, deg + torch.pi / 6.0)
+        deg = torch.where(mask, deg, 5 * torch.pi / 3.0 - deg)
+
+    means = torch.stack(
+        [
+            radius * t * torch.cos(deg) / 2**0.5,
+            radius * t * torch.sin(deg) / 2**0.5,
+            t,
+        ],
+        dim=-1,
+    )  # [..., "num_samples", 6, 3]
+    std = (std_scale * radius * t) / math.sqrt(2)  # [..., "num_samples", 6]
+
+    # two basis in parallel to the image plane
+    rand_vec = torch.rand(
+        list(directions.shape[:-2]) + [1, 3],
+        device=directions.device,
+        dtype=directions.dtype,
+    )  # [..., 1, 3]
+    ortho1 = torch.nn.functional.normalize(
+        torch.cross(directions, rand_vec, dim=-1), dim=-1, eps=eps
+    )  # [..., num_samples, 3]
+    ortho2 = torch.nn.functional.normalize(
+        torch.cross(directions, ortho1, dim=-1), dim=-1, eps=eps
+    )  # [..., num_samples, 3]
+
+    # just use directions to be the third vector of the orthonormal basis,
+    # while the cross section of cone is parallel to the image plane
+    basis_matrix = torch.stack([ortho1, ortho2, directions], dim=-1)
+    means = torch.matmul(means, basis_matrix.transpose(-1, -2))  # [..., "num_samples", 6, 3]
+    means = means + origins[..., None, :]
+
+    return GaussiansStd(mean=means, std=std.unsqueeze(-1))
+
+
 def expected_sin(x_means: torch.Tensor, x_vars: torch.Tensor) -> torch.Tensor:
     """Computes the expected value of sin(y) where y ~ N(x_means, x_vars)
 
@@ -241,6 +374,48 @@ def intersect_aabb(
     return t_min, t_max
 
 
+@torch_compile(dynamic=True, mode="reduce-overhead", backend="eager")
+def intersect_aabb_batch(
+    origins: torch.Tensor,
+    directions: torch.Tensor,
+    aabb: torch.Tensor,
+    max_bound: float = 1e10,
+    invalid_value: float = 1e10,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Implementation of ray intersection with AABB box
+
+    Args:
+        origins: [B, N, 3] tensor of 3d positions
+        directions: [B, N, 3] tensor of normalized directions
+        aabb: [B, 6] array of aabb box in the form of [x_min, y_min, z_min, x_max, y_max, z_max]
+        max_bound: Maximum value of t_max
+        invalid_value: Value to return in case of no intersection
+
+    Returns:
+        t_min, t_max - two tensors of shapes (B, N) representing distance of intersection from the origin.
+    """
+    aabb = aabb.unsqueeze(1)
+
+    tx_min = (aabb[:, :, :3] - origins) / directions
+    tx_max = (aabb[:, :, 3:] - origins) / directions
+
+    t_min = torch.stack((tx_min, tx_max)).amin(dim=0)
+    t_max = torch.stack((tx_min, tx_max)).amax(dim=0)
+
+    t_min = t_min.amax(dim=-1)
+    t_max = t_max.amin(dim=-1)
+
+    t_min = torch.clamp(t_min, min=0, max=max_bound)
+    t_max = torch.clamp(t_max, min=0, max=max_bound)
+
+    cond = t_max <= t_min
+    t_min = torch.where(cond, invalid_value, t_min)
+    t_max = torch.where(cond, invalid_value, t_max)
+
+    return t_min, t_max
+
+
 def intersect_obb(
     origins: torch.Tensor,
     directions: torch.Tensor,
@@ -363,6 +538,53 @@ def normalized_depth_scale_and_shift(
     return scale, shift
 
 
+def power_fn(x: torch.Tensor, lam: float = -1.5, max_bound: float = 1e10) -> torch.Tensor:
+    """Power transformation function from Eq. 4 in ZipNeRF paper."""
+
+    if lam == 1:
+        return x
+    if lam == 0:
+        return torch.log1p(x)
+    # infinity case
+    if lam > max_bound:
+        return torch.expm1(x)
+    # -infinity case
+    if lam < -max_bound:
+        return -torch.expm1(-x)
+
+    lam_1 = abs(lam - 1)
+    return (lam_1 / lam) * ((x / lam_1 + 1) ** lam - 1)
+
+
+def inv_power_fn(
+    x: torch.Tensor,
+    lam: float = -1.5,
+    eps: float = 1e-10,
+    max_bound: float = 1e10,
+) -> torch.Tensor:
+    """Inverse power transformation function from Eq. 4 in ZipNeRF paper."""
+
+    if lam == 1:
+        return x
+    if lam == 0:
+        return torch.expm1(x)
+    # infinity case
+    if lam > max_bound:
+        return torch.log1p(x)
+    # -infinity case
+    if lam < -max_bound:
+        return -torch.log(1 - x)
+
+    lam_1 = abs(lam - 1)
+    return ((x * lam / lam_1 + 1).clamp_min(eps) ** (1 / lam) - 1) * lam_1
+
+
+@torch_compile(dynamic=True, mode="reduce-overhead", backend="eager")
+def erf_approx(x: torch.Tensor) -> torch.Tensor:
+    """Error function approximation proposed in ZipNeRF paper (Eq. 11)."""
+    return torch.sign(x) * torch.sqrt(1 - torch.exp(-4 / torch.pi * x.pow(2)))
+
+
 def columnwise_squared_l2_distance(
     x: Float[Tensor, "*M N"],
     y: Float[Tensor, "*M N"],
@@ -518,3 +740,59 @@ def generate_polyhedron_basis(
 
     basis = verts.flip(-1)
     return basis
+
+
+def chamfer_distance(
+    source_pc: torch.Tensor,
+    target_pc: torch.Tensor,
+    chunk_size: Optional[int] = None,
+    normalize_with_target: bool = False,
+) -> torch.Tensor:
+    """Computes chamfer distance (minimum squared distance from every point to the other point cloud) between two point clouds.
+
+    Args:
+        source_pc: Source point cloud [N,3].
+        target_pc: Target point cloud [M,3].
+        chunk_size: Chunk size to use for computing chamfer distance. If None, use full point clouds.
+        normalize_with_target: Whether to normalize chamfer distance with target point cloud size.
+
+    Returns:
+        torch.Tensor: Chamfer distance between source and target.
+    """
+
+    min_dist_source_to_target = torch.tensor(0.0).to(source_pc.device)
+    min_dist_target_to_source = torch.tensor(0.0).to(source_pc.device)
+
+    # Add batch dimension as expected by torch.cdist
+    source_pc = source_pc.view(1, -1, 3)
+    target_pc = target_pc.view(1, -1, 3)
+
+    def _chamfer_dist(source, target):
+        dist = torch.cdist(source, target, p=2, compute_mode="use_mm_for_euclid_dist_if_necessary").pow(2)
+        dist = dist.view(source.shape[1], target.shape[1])
+        min_dist_source_to_target, _ = torch.min(dist, dim=1)
+        min_dist_target_to_source, _ = torch.min(dist, dim=0)
+        return min_dist_source_to_target.sum(), min_dist_target_to_source.sum()
+
+    if chunk_size is None:
+        min_dist_source_to_target, min_dist_target_to_source = _chamfer_dist(source_pc, target_pc)
+    else:
+        for i in range(0, source_pc.shape[1], chunk_size):
+            max_idx = min(i + chunk_size, source_pc.shape[1])
+            min_dist_source_to_target_add, _ = _chamfer_dist(source_pc[:, i:max_idx], target_pc)
+            min_dist_source_to_target_add = (
+                min_dist_source_to_target_add / target_pc.shape[1]
+                if normalize_with_target
+                else min_dist_source_to_target_add
+            )
+            min_dist_source_to_target += min_dist_source_to_target_add
+        for i in range(0, target_pc.shape[1], chunk_size):
+            max_idx = min(i + chunk_size, target_pc.shape[1])
+            min_dist_target_to_source_add, _ = _chamfer_dist(target_pc[:, i:max_idx], source_pc)
+            min_dist_target_to_source_add = (
+                min_dist_target_to_source_add / target_pc.shape[1]
+                if normalize_with_target
+                else min_dist_target_to_source_add
+            )
+            min_dist_target_to_source += min_dist_target_to_source_add
+    return min_dist_source_to_target + min_dist_target_to_source
diff --git a/nerfstudio/utils/misc.py b/nerfstudio/utils/misc.py
index ae3c4d55..47e68597 100644
--- a/nerfstudio/utils/misc.py
+++ b/nerfstudio/utils/misc.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -177,12 +178,12 @@ def torch_compile(*args, **kwargs) -> Any:
             return args[0]
         else:
             return torch.jit.script
-    elif platform.system() == "Windows":
+    elif (os_name := platform.system()) in ("Windows", "Darwin"):
         # torch.compile is not supported on Windows
         # https://github.com/orgs/pytorch/projects/27
         # TODO: @jkulhanek, remove this once torch.compile is supported on Windows
         warnings.warn(
-            "Windows does not yet support torch.compile and the performance will be affected.", RuntimeWarning
+            f"{os_name} does not yet support torch.compile and the performance will be affected.", RuntimeWarning
         )
         if args and isinstance(args[0], torch.nn.Module):
             return args[0]
diff --git a/nerfstudio/utils/poses.py b/nerfstudio/utils/poses.py
index d0c7c15c..3f1178e2 100644
--- a/nerfstudio/utils/poses.py
+++ b/nerfstudio/utils/poses.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,9 +18,12 @@
 """
 
 import torch
+import torch.nn.functional as F
 from jaxtyping import Float
 from torch import Tensor
 
+from nerfstudio.cameras.camera_utils import rotmat_to_unitquat, unitquat_slerp, unitquat_slerp_fast, unitquat_to_rotmat
+
 
 def to4x4(pose: Float[Tensor, "*batch 3 4"]) -> Float[Tensor, "*batch 4 4"]:
     """Convert 3x4 pose matrices to a 4x4 with the addition of a homogeneous coordinate.
@@ -74,10 +78,121 @@ def normalize(poses: Float[Tensor, "*batch 3 4"]) -> Float[Tensor, "*batch 3 4"]
     Args:
         poses: A collection of poses to be normalized.
 
-    Returns;
+    Returns:
         Normalized collection of poses.
     """
     pose_copy = torch.clone(poses)
     pose_copy[..., :3, 3] /= torch.max(torch.abs(poses[..., :3, 3]))
 
     return pose_copy
+
+
+def interpolate_trajectories_6d(poses, pose_times, query_times, pose_valid_mask=None, flatten=True):
+    """Interpolate trajectory poses at query times using linear interpolation.
+
+    Args:
+        poses: A collection of 9d representation poses (6d rot + 3d position) to be interpolated N_times x N_trajectories.
+        pose_times: The timestamps of the poses N_times.
+        query_times: The timestamps to interpolate the poses at [N_queries, 1].
+        pose_valid_mask: A mask indicating which poses are valid N_times x N_trajectories.
+        flatten: Whether to return the interpolated poses as a flat (masked) tensor or as a 3D (unmasked) tensor. Flatten also returns the indices of the queries and objects used for interpolation. Not flattening returns the mask used to find said indices. Default is True.
+
+    Returns:
+        The interpolated poses at the query times (M x 9)
+        The indices of the queries used for interpolation (M).
+        The indices of the tractories used for interpolation (M).
+    """
+    assert len(poses.shape) == 3, "Poses must be of shape [num_actors, num_poses, 9]"
+    if len(poses) == 0:
+        if flatten:
+            return (
+                torch.empty(0, 9, device=poses.device),
+                torch.empty(0, dtype=torch.long),
+                torch.empty(0, dtype=torch.long),
+            )
+        else:
+            return torch.empty(0, 9, device=poses.device), torch.empty(len(query_times), 0, dtype=torch.bool)
+    # Orthogonalize the first two axes
+    a1 = F.normalize(poses[..., :3], dim=-1)
+    a2 = poses[..., 3:6]
+    a2 = a2 - (a1 * a2).sum(-1, keepdim=True) * a1
+    a2 = F.normalize(a2, dim=-1)
+
+    positions = poses[..., 6:9]
+    poses = torch.cat([a1, a2, positions], dim=-1)
+    right_idx = torch.searchsorted(pose_times, query_times.squeeze(-1))
+    left_idx = (right_idx - 1).clamp(min=0)
+    right_idx = right_idx.clamp(max=len(pose_times) - 1)
+
+    # Compute the fraction between the left (previous) and right (after) timestamps
+    right_time = pose_times[right_idx]
+    left_time = pose_times[left_idx]
+    time_diff = right_time - left_time + 1e-6
+    fraction = (query_times.squeeze(-1) - left_time) / time_diff  # 0 = all left, 1 = all right
+    fraction = fraction.clamp(0.0, 1.0)  # clamp to handle out of bounds
+
+    if pose_valid_mask is None:
+        pose_valid_mask = torch.ones_like(poses[..., 0, 0], dtype=torch.bool)
+    trajs_to_sample = pose_valid_mask[left_idx] | pose_valid_mask[right_idx]  # [num_queries, n_trajs]
+
+    poses_left = poses[left_idx]
+    poses_right = poses[right_idx]
+
+    if flatten:
+        query_idxs, object_idxs = torch.where(trajs_to_sample)  # 2 x [num_queries*n_valid_trajs]
+        poses_left = poses_left[query_idxs, object_idxs]  # [num_queries*n_valid_objects, 9]
+        poses_right = poses_right[query_idxs, object_idxs]  # [num_queries*n_valid_objects, 9]
+        interpolated_poses = poses_left + (poses_right - poses_left) * fraction[query_idxs].unsqueeze(-1)
+        return interpolated_poses, query_idxs, object_idxs
+    else:
+        interpolated_poses = poses_left + (poses_right - poses_left) * fraction.unsqueeze(-1).unsqueeze(-1)
+        return interpolated_poses, trajs_to_sample  # trajs_to_sample is a validity mask
+
+
+def interpolate_trajectories(poses, pose_times, query_times, pose_valid_mask=None, clamp_frac=True):
+    """Interpolate trajectory poses at query times using linear interpolation.
+
+    Args:
+        poses: A collection of poses to be interpolated N_times x N_trajectories.
+        pose_times: The timestamps of the poses N_times.
+        query_times: The timestamps to interpolate the poses at N_queries.
+        pose_valid_mask: A mask indicating which poses are valid N_times x N_trajectories.
+
+    Returns:
+        The interpolated poses at the query times (M x 3 x 4)
+        The indices of the queries used for interpolation (M).
+        The indices of the tractories used for interpolation (M).
+    """
+    assert len(poses.shape) == 4, "Poses must be of shape [num_actors, num_poses, 3, 4]"
+    # bugs are crawling like crazy if we only have one query time, fix with maybe squeeze
+    qt = query_times if query_times.shape[-1] == 1 else query_times.squeeze()
+    right_idx = torch.searchsorted(pose_times, qt)
+    left_idx = (right_idx - 1).clamp(min=0)
+    right_idx = right_idx.clamp(max=len(pose_times) - 1)
+
+    # Compute the fraction between the left (previous) and right (after) timestamps
+    right_time = pose_times[right_idx]
+    left_time = pose_times[left_idx]
+    time_diff = right_time - left_time + 1e-6
+    fraction = (qt - left_time) / time_diff  # 0 = all left, 1 = all right
+    if clamp_frac:
+        fraction = fraction.clamp(0.0, 1.0)  # clamp to handle out of bounds
+
+    if pose_valid_mask is None:
+        pose_valid_mask = torch.ones_like(poses[..., 0, 0], dtype=torch.bool)
+    trajs_to_sample = pose_valid_mask[left_idx] | pose_valid_mask[right_idx]  # [num_queries, n_trajs]
+    query_idxs, object_idxs = torch.where(trajs_to_sample)  # 2 x [num_queries*n_valid_trajs]
+
+    quat = rotmat_to_unitquat(poses[..., :3, :3])
+    quat_left = quat[left_idx][query_idxs, object_idxs]  # [num_queries*n_valid_objects, 4]
+    quat_right = quat[right_idx][query_idxs, object_idxs]  # [num_queries*n_valid_objects, 4]
+    interp_fn = unitquat_slerp if (fraction < 0).any() or (fraction > 1).any() else unitquat_slerp_fast
+    interp_quat = interp_fn(quat_left, quat_right, fraction[query_idxs])
+    interp_rot = unitquat_to_rotmat(interp_quat)
+
+    pos_left = poses[left_idx][query_idxs, object_idxs, :3, 3]  # [num_queries*n_valid_objects, 3]
+    pos_right = poses[right_idx][query_idxs, object_idxs, :3, 3]  # [num_queries*n_valid_objects, 3]
+    interp_pos = pos_left + (pos_right - pos_left) * fraction[query_idxs].unsqueeze(-1)
+
+    interpolated_poses = torch.cat([interp_rot, interp_pos.unsqueeze(-1)], dim=-1)
+    return interpolated_poses, query_idxs, object_idxs
diff --git a/nerfstudio/utils/tensor_dataclass.py b/nerfstudio/utils/tensor_dataclass.py
index 293d978d..3001cef0 100644
--- a/nerfstudio/utils/tensor_dataclass.py
+++ b/nerfstudio/utils/tensor_dataclass.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,7 +17,7 @@
 
 import dataclasses
 from copy import deepcopy
-from typing import Callable, Dict, List, NoReturn, Optional, Tuple, TypeVar, Union
+from typing import Any, Callable, Dict, Iterable, List, NoReturn, Optional, Tuple, TypeVar, Union
 
 import numpy as np
 import torch
@@ -253,7 +254,7 @@ def custom_tensor_dims_fn(k, v):
             lambda x: x.broadcast_to((*shape, x.shape[-1])), custom_tensor_dims_fn=custom_tensor_dims_fn
         )
 
-    def to(self: TensorDataclassT, device) -> TensorDataclassT:
+    def to(self: TensorDataclassT, device, **kwargs) -> TensorDataclassT:
         """Returns a new TensorDataclass with the same data but on the specified device.
 
         Args:
@@ -262,7 +263,30 @@ def to(self: TensorDataclassT, device) -> TensorDataclassT:
         Returns:
             A new TensorDataclass with the same data but on the specified device.
         """
-        return self._apply_fn_to_fields(lambda x: x.to(device))
+        return self._apply_fn_to_fields(lambda x: x.to(device, **kwargs))
+
+    def cat(
+        self: TensorDataclassT, tensors: List[TensorDataclassT], dim: int = 0, ignore_fields: Iterable = set()
+    ) -> TensorDataclassT:
+        """Concatenates the tensor dataclass with the tensors along the specified dimension.
+
+        Args:
+            tensors: The tensors to concatenate with the tensor dataclass.
+            dim: The dimension to concatenate along.
+
+        Returns:
+            A new TensorDataclass with the tensors concatenated along the specified dimension.
+        """
+        if not isinstance(tensors, (list, tuple)):
+            tensors = [tensors]
+        assert all(type(t) == type(self) for t in tensors), "All tensors must be of the same type."
+        assert dataclasses.is_dataclass(self)
+        new_fields = {
+            field.name: _cat(getattr(self, field.name), [getattr(t, field.name) for t in tensors], dim)
+            for field in dataclasses.fields(self)
+            if field.name not in ignore_fields
+        }
+        return self.__class__(**new_fields)
 
     def pin_memory(self: TensorDataclassT) -> TensorDataclassT:
         """Pins the tensor dataclass memory
@@ -344,7 +368,24 @@ def _apply_fn_to_dict(
                     new_dict[f] = fn(v)
                 elif isinstance(v, Dict):
                     new_dict[f] = self._apply_fn_to_dict(v, fn, dataclass_fn)
+                elif isinstance(v, (list, tuple)):
+                    new_dict[f] = [fn(vi) for vi in v]
                 else:
                     new_dict[f] = deepcopy(v)
 
         return new_dict
+
+
+def _cat(x: Any, y: List[Any], dim: int):
+    if isinstance(x, TensorDataclass):
+        return x.cat(y, dim=dim)
+    elif isinstance(x, torch.Tensor):
+        return torch.cat([x, *y], dim=dim)
+    elif isinstance(x, Dict):
+        # TODO: maybe handle key mismatch better. Currently only merges keys that are in all dicts
+        return {k: _cat(v, [yi[k] for yi in y], dim) for k, v in x.items()}
+    elif isinstance(x, (list, tuple)):
+        return [_cat(xi, [yi[i] for yi in y], dim) for i, xi in enumerate(x)]
+    else:
+        # If we can't concatenate, we return the value of x
+        return deepcopy(x)
diff --git a/nerfstudio/utils/writer.py b/nerfstudio/utils/writer.py
index d460e941..73c020ac 100644
--- a/nerfstudio/utils/writer.py
+++ b/nerfstudio/utils/writer.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -318,7 +319,7 @@ def write_image(self, name: str, image: Float[Tensor, "H W C"], step: int) -> No
         import wandb  # wandb is slow to import, so we only import it if we need it.
 
         image = torch.permute(image, (2, 0, 1))
-        wandb.log({name: wandb.Image(image)}, step=step)
+        wandb.log({name: wandb.Image(image, file_type="jpg")}, step=step)
 
     def write_scalar(self, name: str, scalar: Union[float, torch.Tensor], step: int) -> None:
         import wandb  # wandb is slow to import, so we only import it if we need it.
diff --git a/nerfstudio/viewer/control_panel.py b/nerfstudio/viewer/control_panel.py
index 6524c530..b4640105 100644
--- a/nerfstudio/viewer/control_panel.py
+++ b/nerfstudio/viewer/control_panel.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,6 +15,7 @@
 
 """ Control panel for the viewer """
 from collections import defaultdict
+from functools import partial
 from typing import Callable, DefaultDict, List, Tuple, get_args
 
 import numpy as np
@@ -25,6 +27,7 @@
 from nerfstudio.data.scene_box import OrientedBox
 from nerfstudio.utils.colormaps import ColormapOptions, Colormaps
 from nerfstudio.viewer.viewer_elements import (  # ViewerButtonGroup,
+    ViewerButton,
     ViewerButtonGroup,
     ViewerCheckbox,
     ViewerDropdown,
@@ -32,6 +35,7 @@
     ViewerNumber,
     ViewerRGB,
     ViewerSlider,
+    ViewerVec2,
     ViewerVec3,
 )
 
@@ -55,6 +59,9 @@ def __init__(
         rerender_cb: Callable[[], None],
         update_output_cb: Callable,
         update_split_output_cb: Callable,
+        rerender_lidar_cb: Callable,
+        rendering_disabled_cb: Callable,
+        max_time: float = 1.0,
         default_composite_depth: bool = True,
     ):
         self.viser_scale_ratio = scale_ratio
@@ -85,6 +92,47 @@ def __init__(
         )
         self._min = ViewerNumber("Min", 0.0, cb_hook=lambda _: rerender_cb(), hint="Min value of the colormap")
         self._max = ViewerNumber("Max", 1.0, cb_hook=lambda _: rerender_cb(), hint="Max value of the colormap")
+        self._range_min = ViewerNumber(
+            "Range Min", 0.0, cb_hook=lambda _: rerender_cb(), hint="Min value of the range (0.0 for auto))"
+        )
+        self._range_max = ViewerNumber(
+            "Range Max", 0.0, cb_hook=lambda _: rerender_cb(), hint="Max value of the range (0.0 for auto))"
+        )
+
+        self._lidar_enabled = ViewerCheckbox(
+            "Enable",
+            False,
+            cb_hook=lambda han: [self.update_control_panel(), rerender_cb(), rerender_lidar_cb(han)],
+            hint="Render lidar",
+        )
+        self._lidar_position = ViewerVec3(
+            "Position", (0.0, 0.0, 0.0), cb_hook=rerender_lidar_cb, hint="Position of the lidar"
+        )
+        self._lidar_snap_to_camera = ViewerButton(
+            "Snap to Camera", cb_hook=partial(rerender_lidar_cb, snap_to_cam=True)
+        )
+        self._lidar_beams = ViewerSlider("Num Beams", 24, 1, 256, 1, cb_hook=rerender_lidar_cb, hint="Number of beams")
+        self._lidar_fov = ViewerVec2(
+            "Vertical FoV", (-15.0, 15.0), cb_hook=rerender_lidar_cb, hint="Vertical field of view"
+        )
+        self._lidar_azim_res = ViewerSlider(
+            "Azimuth Res", 0.5, 0.01, 1.0, 0.01, cb_hook=rerender_lidar_cb, hint="Azimuth resolution (deg)"
+        )
+        self._lidar_max_dist = ViewerSlider(
+            "Distance Cutoff", 250.0, 10.0, 500.0, 10.0, cb_hook=rerender_lidar_cb, hint="Distance cutoff (m)"
+        )
+        self._lidar_use_ray_drop = ViewerCheckbox(
+            "Use Ray Drop",
+            True,
+            cb_hook=lambda han: [rerender_lidar_cb(han), self._lidar_ray_drop_threshold.set_visible(han.value)],
+            hint="Use ray drop probability",
+        )
+        self._lidar_ray_drop_threshold = ViewerSlider(
+            "Ray Drop Threshold", 0.0, 0.0, 1.0, 0.01, cb_hook=rerender_lidar_cb, visible=True
+        )
+        self._lidar_color_by_intensity = ViewerCheckbox(
+            "Color by Intensity", False, cb_hook=rerender_lidar_cb, hint="Color the lidar points by intensity"
+        )
 
         self._split = ViewerCheckbox(
             "Enable",
@@ -119,6 +167,12 @@ def __init__(
             "Max ", 1.0, cb_hook=lambda _: rerender_cb(), hint="Max value of the colormap of the second output"
         )
 
+        self._rendering_disabled = ViewerCheckbox(
+            "Disable Rendering",
+            False,
+            cb_hook=lambda han: [self.update_control_panel(), rerender_cb(), rendering_disabled_cb(han)],
+        )
+        self._color_override = ViewerRGB("Color Override", (255, 255, 255), cb_hook=rendering_disabled_cb)
         self._train_util = ViewerSlider(
             "Train Util",
             default_value=0.85,
@@ -186,13 +240,17 @@ def _update_crop_handle(han):
             rpy = vtf.SO3(self._crop_handle.wxyz).as_rpy_radians()
             self._crop_rot.value = (float(rpy.roll), float(rpy.pitch), float(rpy.yaw))
 
-        self._time = ViewerSlider("Time", 0.0, 0.0, 1.0, 0.01, cb_hook=lambda _: rerender_cb(), hint="Time to render")
+        self._time = ViewerSlider(
+            "Time", 0.0, 0.0, max_time, 0.01, cb_hook=lambda _: rerender_cb(), hint="Time to render"
+        )
         self._time_enabled = time_enabled
 
         self.add_element(self._train_speed)
         self.add_element(self._train_util)
 
         with self.viser_server.add_gui_folder("Render Options"):
+            self.add_element(self._rendering_disabled)
+            self.add_element(self._color_override, additional_tags=("rendering_disabled",))
             self.add_element(self._max_res)
             self.add_element(self._output_render)
             self.add_element(self._colormap)
@@ -202,6 +260,20 @@ def _update_crop_handle(han):
             self.add_element(self._normalize, additional_tags=("colormap",))
             self.add_element(self._min, additional_tags=("colormap",))
             self.add_element(self._max, additional_tags=("colormap",))
+            self.add_element(self._range_min, additional_tags=("colormap",))
+            self.add_element(self._range_max, additional_tags=("colormap",))
+
+        with self.viser_server.add_gui_folder("Lidar Rendering"):
+            self.add_element(self._lidar_enabled)
+            self.add_element(self._lidar_beams, additional_tags=("lidar",))
+            self.add_element(self._lidar_fov, additional_tags=("lidar",))
+            self.add_element(self._lidar_azim_res, additional_tags=("lidar",))
+            self.add_element(self._lidar_position, additional_tags=("lidar",))
+            self.add_element(self._lidar_snap_to_camera, additional_tags=("lidar",))
+            self.add_element(self._lidar_max_dist, additional_tags=("lidar",))
+            self.add_element(self._lidar_use_ray_drop, additional_tags=("lidar",))
+            self.add_element(self._lidar_ray_drop_threshold, additional_tags=("lidar",))
+            self.add_element(self._lidar_color_by_intensity, additional_tags=("lidar",))
 
         # split options
         with self.viser_server.add_gui_folder("Split Screen"):
@@ -283,6 +355,10 @@ def update_control_panel(self) -> None:
             e.set_hidden(not self._split.value or self.split_output_render == "rgb")
         for e in self._elements_by_tag["crop"]:
             e.set_hidden(not self.crop_viewport)
+        for e in self._elements_by_tag["lidar"]:
+            e.set_hidden(not self.lidar_enabled)
+        for e in self._elements_by_tag["rendering_disabled"]:
+            e.set_hidden(not self.rendering_disabled)
         self._time.set_hidden(not self._time_enabled)
         self._split_percentage.set_hidden(not self._split.value)
         self._split_output_render.set_hidden(not self._split.value)
@@ -333,6 +409,16 @@ def train_util(self) -> float:
         """Returns the current train util setting"""
         return self._train_util.value
 
+    @property
+    def rendering_disabled(self) -> bool:
+        """Returns whether rendering is enabled"""
+        return self._rendering_disabled.value
+
+    @property
+    def color_override(self) -> Tuple[int, int, int]:
+        """Returns whether the color override is enabled"""
+        return self._color_override.value
+
     @property
     def max_res(self) -> int:
         """Returns the current max res setting"""
@@ -385,6 +471,8 @@ def colormap_options(self) -> ColormapOptions:
             colormap_min=self._min.value,
             colormap_max=self._max.value,
             invert=self._invert.value,
+            range_min=self._range_min.value if self._range_min.value != 0 else None,
+            range_max=self._range_max.value if self._range_max.value != 0 else None,
         )
 
     @property
@@ -402,6 +490,56 @@ def split_colormap_options(self) -> ColormapOptions:
     def layer_depth(self):
         return self._layer_depth.value
 
+    @property
+    def lidar_enabled(self) -> bool:
+        """Returns the current lidar enabled setting"""
+        return self._lidar_enabled.value
+
+    @property
+    def lidar_position(self) -> Tuple[float, float, float]:
+        """Returns the current lidar position"""
+        return self._lidar_position.value
+
+    @lidar_position.setter
+    def lidar_position(self, value: Tuple[float, float, float]):
+        """Sets the lidar position"""
+        self._lidar_position.value = value
+
+    @property
+    def lidar_beams(self) -> int:
+        """Returns the current lidar beams"""
+        return self._lidar_beams.value
+
+    @property
+    def lidar_fov(self) -> Tuple[float, float]:
+        """Returns the current lidar fov"""
+        return self._lidar_fov.value
+
+    @property
+    def lidar_azim_res(self) -> float:
+        """Returns the current lidar azimuthal resolution"""
+        return self._lidar_azim_res.value
+
+    @property
+    def lidar_max_dist(self) -> float:
+        """Returns the current lidar max distance"""
+        return self._lidar_max_dist.value
+
+    @property
+    def lidar_use_ray_drop(self) -> bool:
+        """Whether to use ray drop probability when rendering lidar"""
+        return self._lidar_use_ray_drop.value
+
+    @property
+    def lidar_ray_drop_threshold(self) -> float:
+        """The threshold at which to consider a lidar ray to be dropped"""
+        return self._lidar_ray_drop_threshold.value
+
+    @property
+    def lidar_color_by_intensity(self) -> bool:
+        """Whether to color the lidar points by intensity"""
+        return self._lidar_color_by_intensity.value
+
 
 def _get_colormap_options(dimensions: int, dtype: type) -> List[Colormaps]:
     """
diff --git a/nerfstudio/viewer/render_state_machine.py b/nerfstudio/viewer/render_state_machine.py
index 1fa7c07d..5a7b98d6 100644
--- a/nerfstudio/viewer/render_state_machine.py
+++ b/nerfstudio/viewer/render_state_machine.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -24,9 +25,11 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-from viser import ClientHandle
+from viser import ClientHandle, PointCloudHandle
 
 from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.cameras.lidars import intensity_to_rgb
+from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.model_components.renderers import background_color_override_context
 from nerfstudio.models.splatfacto import SplatfactoModel
 from nerfstudio.utils import colormaps, writer
@@ -86,6 +89,7 @@ def __init__(self, viewer: Viewer, viser_scale_ratio: float, client: ClientHandl
         self.viser_scale_ratio = viser_scale_ratio
         self.client = client
         self.running = True
+        self.disabled = False
 
     def action(self, action: RenderAction):
         """Takes an action and updates the state machine
@@ -190,6 +194,14 @@ def _render_img(self, camera_state: CameraState):
                     # Convert to z_depth if depth compositing is enabled.
                     R = camera.camera_to_worlds[0, 0:3, 0:3].T
                     camera_ray_bundle = camera.generate_rays(camera_indices=0, obb_box=obb)
+                    if outputs["depth"].shape[:-1] != camera_ray_bundle.directions.shape[:-1]:
+                        # If the depth image is not the same size as the camera rays, we need to resize it
+                        outputs["depth"] = F.interpolate(
+                            outputs["depth"][None, None, ..., 0],  # HW1 -> 11HW (batch, channel, H, W)
+                            size=camera_ray_bundle.directions.shape[:-1],
+                            mode="bilinear",
+                            align_corners=False,
+                        )[0, 0, ..., None]  # 11HW -> HW1  (drop batch and move channel to last dim)
                     pts = camera_ray_bundle.directions * outputs["depth"]
                     pts = (R @ (pts.view(-1, 3).T)).T.view(*camera_ray_bundle.directions.shape)
                     outputs["gl_z_buf_depth"] = -pts[..., 2:3]  # negative z axis is the coordinate convention
@@ -203,7 +215,7 @@ def _render_img(self, camera_state: CameraState):
     def run(self):
         """Main loop for the render thread"""
         while self.running:
-            if not self.viewer.ready:
+            if not self.viewer.ready or self.disabled:
                 time.sleep(0.1)
                 continue
             if not self.render_trigger.wait(0.2):
@@ -344,3 +356,96 @@ def _calculate_image_res(self, aspect_ratio: float) -> Tuple[int, int]:
             raise ValueError(f"Invalid state: {self.state}")
 
         return image_height, image_width
+
+
+class LidarRenderer(threading.Thread):
+    """Simple lidar renderering thread that renders lidar data and sends it to the viewer
+
+    Args:
+        viewer: the viewer object
+    """
+
+    def __init__(self, viewer: Viewer, client: ClientHandle):
+        super().__init__()
+        self.viewer = viewer
+        self.client = client
+        self.running = True
+        self.rendered_pc_handle: Optional[PointCloudHandle] = None
+        self._render_next = False
+
+    def trigger(self):
+        self._render_next = True
+
+    def run(self):
+        """Main loop for the thread"""
+        while self.running:
+            if not self._render_next:
+                time.sleep(0.1)
+                continue
+            try:
+                self._render_lidar()
+                self._render_next = False
+            except viewer_utils.IOChangeException:
+                continue
+
+    def _render_lidar(self):
+        control_panel = self.viewer.control_panel
+        # Construct rays from lidar specifications
+        device = self.viewer.get_model().device
+        v_angles = torch.linspace(*np.deg2rad(control_panel.lidar_fov), control_panel.lidar_beams, device=device)
+        h_angles = torch.arange(0, 2 * np.pi, np.deg2rad(control_panel.lidar_azim_res), device=device)
+        v_angles, h_angles = torch.meshgrid(v_angles, h_angles)
+        v_angles, h_angles = v_angles.flatten(), h_angles.flatten()
+        directions = torch.stack(
+            [
+                torch.cos(v_angles) * torch.cos(h_angles),
+                torch.cos(v_angles) * torch.sin(h_angles),
+                torch.sin(v_angles),
+            ],
+            dim=-1,
+        )
+        origins = torch.tensor(control_panel.lidar_position, dtype=torch.float32, device=device)
+        lidar_ray_bundle = RayBundle(
+            origins=origins,
+            directions=directions,
+            pixel_area=torch.zeros_like(v_angles, dtype=torch.float32)[..., None],  # TODO: is this relevant?
+            metadata={"is_lidar": torch.ones_like(v_angles, dtype=torch.bool)[..., None]},
+            times=torch.tensor([self.viewer.control_panel.time], dtype=torch.float32, device=device),
+        )
+
+        # Run model
+        with self.viewer.train_lock or contextlib.nullcontext():
+            self.viewer.get_model().eval()
+            with torch.no_grad():
+                outputs = self.viewer.get_model().get_outputs_for_camera_ray_bundle(lidar_ray_bundle)
+            self.viewer.get_model().train()
+
+        point_cloud = torch.cat(
+            [
+                outputs["depth"] * directions + origins,
+                outputs["intensity"],
+            ],
+            dim=-1,
+        )
+        if "ray_drop_prob" in outputs and control_panel.lidar_use_ray_drop:
+            point_cloud = point_cloud[outputs["ray_drop_prob"].squeeze(-1) < control_panel.lidar_ray_drop_threshold]
+        else:
+            point_cloud = point_cloud[outputs["depth"].squeeze(-1) < control_panel.lidar_max_dist]
+        point_cloud = point_cloud.cpu().numpy()
+
+        if self.rendered_pc_handle is not None:
+            self.rendered_pc_handle.remove()
+
+        if control_panel.lidar_color_by_intensity:
+            colors = intensity_to_rgb(point_cloud[:, 3])
+        else:
+            colors = (200, 200, 200)
+
+        self.rendered_pc_handle = self.client.add_point_cloud(
+            name="/rendered_point_cloud",
+            points=point_cloud[:, :3],
+            colors=colors,
+            point_size=0.05,
+            point_shape="circle",
+            # pc is in global coords so no need to set position and rotation
+        )
diff --git a/nerfstudio/viewer/viewer.py b/nerfstudio/viewer/viewer.py
index db84e955..a1b614d8 100644
--- a/nerfstudio/viewer/viewer.py
+++ b/nerfstudio/viewer/viewer.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -29,6 +30,7 @@
 
 from nerfstudio.cameras.camera_optimizers import CameraOptimizer
 from nerfstudio.cameras.cameras import CameraType
+from nerfstudio.cameras.lidars import intensity_to_rgb, transform_points
 from nerfstudio.configs import base_config as cfg
 from nerfstudio.data.datasets.base_dataset import InputDataset
 from nerfstudio.models.base_model import Model
@@ -39,7 +41,7 @@
 from nerfstudio.viewer.control_panel import ControlPanel
 from nerfstudio.viewer.export_panel import populate_export_tab
 from nerfstudio.viewer.render_panel import populate_render_tab
-from nerfstudio.viewer.render_state_machine import RenderAction, RenderStateMachine
+from nerfstudio.viewer.render_state_machine import LidarRenderer, RenderAction, RenderStateMachine
 from nerfstudio.viewer.utils import CameraState, parse_object
 from nerfstudio.viewer.viewer_elements import ViewerControl, ViewerElement
 from nerfstudio.viewer_legacy.server import viewer_utils
@@ -48,7 +50,7 @@
     from nerfstudio.engine.trainer import Trainer
 
 
-VISER_NERFSTUDIO_SCALE_RATIO: float = 10.0
+VISER_NERFSTUDIO_SCALE_RATIO: float = 1.0
 
 
 @decorate_all([check_main_thread])
@@ -156,6 +158,7 @@ def __init__(
         )
 
         self.render_statemachines: Dict[int, RenderStateMachine] = {}
+        self.lidar_renderers: Dict[int, LidarRenderer] = {}
         self.viser_server.on_client_disconnect(self.handle_disconnect)
         self.viser_server.on_client_connect(self.handle_new_client)
 
@@ -183,6 +186,18 @@ def __init__(
         self.show_images.on_click(lambda _: self.set_camera_visibility(True))
         self.show_images.on_click(lambda _: self.toggle_cameravis_button())
         self.show_images.visible = False
+        # Add buttons to toggle training point cloud visibility
+        self.hide_lidars = self.viser_server.add_gui_button(
+            label="Hide Train Lidars", disabled=False, icon=viser.Icon.EYE_OFF, color=None, visible=False
+        )
+        self.hide_lidars.on_click(lambda _: self.set_lidar_visibility(False))
+        self.hide_lidars.on_click(lambda _: self.toggle_lidarvis_button())
+        self.show_lidars = self.viser_server.add_gui_button(
+            label="Show Train Lidars", disabled=False, icon=viser.Icon.EYE, color=None, visible=False
+        )
+        self.show_lidars.on_click(lambda _: self.set_lidar_visibility(True))
+        self.show_lidars.on_click(lambda _: self.toggle_lidarvis_button())
+
         mkdown = self.make_stats_markdown(0, "0x0px")
         self.stats_markdown = self.viser_server.add_gui_markdown(mkdown)
         tabs = self.viser_server.add_gui_tab_group()
@@ -195,6 +210,9 @@ def __init__(
                 self._trigger_rerender,
                 self._output_type_change,
                 self._output_split_type_change,
+                self._rerender_lidar,
+                self._rendering_disabled_change,
+                max_time=self.pipeline.datamanager.train_dataparser_outputs.metadata.get("duration", 1.0),
                 default_composite_depth=self.config.default_composite_depth,
             )
         config_path = self.log_filename.parents[0] / "config.yml"
@@ -278,6 +296,10 @@ def toggle_cameravis_button(self) -> None:
         self.hide_images.visible = not self.hide_images.visible
         self.show_images.visible = not self.show_images.visible
 
+    def toggle_lidarvis_button(self) -> None:
+        self.hide_lidars.visible = not self.hide_lidars.visible
+        self.show_lidars.visible = not self.show_lidars.visible
+
     def make_stats_markdown(self, step: Optional[int], res: Optional[str]) -> str:
         # if either are None, read it from the current stats_markdown content
         if step is None:
@@ -326,10 +348,14 @@ def get_camera_state(self, client: viser.ClientHandle) -> CameraState:
     def handle_disconnect(self, client: viser.ClientHandle) -> None:
         self.render_statemachines[client.client_id].running = False
         self.render_statemachines.pop(client.client_id)
+        self.lidar_renderers[client.client_id].running = False
+        self.lidar_renderers.pop(client.client_id)
 
     def handle_new_client(self, client: viser.ClientHandle) -> None:
         self.render_statemachines[client.client_id] = RenderStateMachine(self, VISER_NERFSTUDIO_SCALE_RATIO, client)
         self.render_statemachines[client.client_id].start()
+        self.lidar_renderers[client.client_id] = LidarRenderer(self, client)
+        self.lidar_renderers[client.client_id].start()
 
         @client.camera.on_update
         def _(_: viser.CameraHandle) -> None:
@@ -346,6 +372,12 @@ def set_camera_visibility(self, visible: bool) -> None:
             for idx in self.camera_handles:
                 self.camera_handles[idx].visible = visible
 
+    def set_lidar_visibility(self, visible: bool) -> None:
+        """Toggle the visibility of the training cameras."""
+        with self.viser_server.atomic():
+            for handle in self.lidar_handles.values():
+                handle.visible = visible
+
     def update_camera_poses(self):
         # TODO this fn accounts for like ~5% of total train time
         # Update the train camera locations based on optimization
@@ -379,6 +411,32 @@ def _trigger_rerender(self) -> None:
             camera_state = self.get_camera_state(clients[id])
             self.render_statemachines[id].action(RenderAction("move", camera_state))
 
+    def _rerender_lidar(self, *args, snap_to_cam: bool = False) -> None:
+        """Handle lidar update message from viewer."""
+        for client_id, client in self.viser_server.get_clients().items():
+            if snap_to_cam:
+                # TODO: this will propagate to all clients, needs to be client-specific somehow!
+                camera_state = self.get_camera_state(client=client)
+                self.control_panel.lidar_position = camera_state.c2w[:3, 3].tolist()
+
+            if self.control_panel.lidar_enabled:
+                self.lidar_renderers[client_id].trigger()
+            else:  # clear the lidar point cloud
+                handle = self.lidar_renderers[client_id].rendered_pc_handle
+                if handle is not None:
+                    handle.remove()
+
+    def _rendering_disabled_change(self, _):
+        clients = self.viser_server.get_clients()
+        for id in clients:
+            self.render_statemachines[id].disabled = self.control_panel.rendering_disabled
+            if self.render_statemachines[id].disabled:
+                bg_img = (
+                    np.ones((10, 10, 3), dtype=np.uint8) * np.array(self.control_panel.color_override)[None, None, :]
+                )
+                self.viser_server.set_background_image(bg_img)
+                self.render_statemachines[id].interrupt_render_flag = True
+
     def _toggle_training_state(self, _) -> None:
         """Toggle the trainer's training state."""
         if self.trainer is not None:
@@ -459,6 +517,27 @@ def _(event: viser.SceneNodePointerEvent[viser.CameraFrustumHandle]) -> None:
             self.camera_handles[idx] = camera_handle
             self.original_c2w[idx] = c2w
 
+        # draw the lidar point clouds (if any)
+        self.lidar_handles: Dict[int, viser.PointCloudHandle] = {}
+        lidars = train_dataset.metadata.get("lidars", None) if train_dataset.metadata else None
+        if lidars is not None:
+            point_clouds = train_dataset.metadata["point_clouds"]
+            for idx in range(len(lidars)):
+                point_cloud = point_clouds[idx]
+                point_cloud = transform_points(point_cloud, lidars.lidar_to_worlds[idx])
+                max_points = self.config.max_points_per_cloud
+                if max_points is not None and point_cloud.shape[0] > max_points:
+                    point_cloud = point_cloud[torch.randperm(point_cloud.shape[0])[:max_points]]
+                self.lidar_handles[idx] = self.viser_server.add_point_cloud(
+                    f"/lidar/{idx:06d}",
+                    points=point_cloud[:, :3].cpu().numpy(),
+                    colors=intensity_to_rgb(point_cloud[:, 3].cpu().numpy()),
+                    point_size=0.02,
+                    point_shape="circle",
+                )
+        if self.lidar_handles:
+            self.hide_lidars.visible = True  # only show lidar buttons if there are lidars
+
         self.train_state = train_state
         self.train_util = 0.9
 
diff --git a/nerfstudio/viewer/viewer_elements.py b/nerfstudio/viewer/viewer_elements.py
index 654503a3..2cb2ea79 100644
--- a/nerfstudio/viewer/viewer_elements.py
+++ b/nerfstudio/viewer/viewer_elements.py
@@ -1,3 +1,4 @@
+# Copyright 2024 the authors of NeuRAD and contributors.
 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -677,6 +678,41 @@ def _create_gui_handle(self, viser_server: ViserServer) -> None:
         )
 
 
+class ViewerVec2(ViewerParameter[Tuple[float, float]]):
+    """
+    2 number boxes in a row to input a vector
+
+    Args:
+        name: The name of the vector
+        default_value: The default value of the vector
+        step: The step of the vector
+        disabled: If the vector is disabled
+        visible: If the vector is visible
+        cb_hook: Callback to call on update
+        hint: The hint text
+    """
+
+    def __init__(
+        self,
+        name,
+        default_value: Tuple[float, float],
+        step=0.1,
+        disabled=False,
+        visible=True,
+        cb_hook: Callable[[ViewerVec2], Any] = lambda element: None,
+        hint: Optional[str] = None,
+    ):
+        assert len(default_value) == 2
+        super().__init__(name, default_value, disabled=disabled, visible=visible, cb_hook=cb_hook)
+        self.step = step
+        self.hint = hint
+
+    def _create_gui_handle(self, viser_server: ViserServer) -> None:
+        self.gui_handle = viser_server.add_gui_vector2(
+            self.name, self.default_value, step=self.step, disabled=self.disabled, visible=self.visible, hint=self.hint
+        )
+
+
 class ViewerVec3(ViewerParameter[Tuple[float, float, float]]):
     """
     3 number boxes in a row to input a vector
@@ -710,3 +746,37 @@ def _create_gui_handle(self, viser_server: ViserServer) -> None:
         self.gui_handle = viser_server.add_gui_vector3(
             self.name, self.default_value, step=self.step, disabled=self.disabled, visible=self.visible, hint=self.hint
         )
+
+
+class ViewerVecN(ViewerParameter[Tuple[float, ...]]):
+    """
+    N number boxes in a row to input a vector
+
+    Args:
+        name: The name of the vector
+        default_value: The default value of the vector
+        step: The step of the vector
+        disabled: If the vector is disabled
+        visible: If the vector is visible
+        cb_hook: Callback to call on update
+        hint: The hint text
+    """
+
+    def __init__(
+        self,
+        name,
+        default_value: Tuple[float, ...],
+        step=0.1,
+        disabled=False,
+        visible=True,
+        cb_hook: Callable[[ViewerVecN], Any] = lambda element: None,
+        hint: Optional[str] = None,
+    ):
+        super().__init__(name, default_value, disabled=disabled, visible=visible, cb_hook=cb_hook)
+        self.step = step
+        self.hint = hint
+
+    def _create_gui_handle(self, viser_server: ViserServer) -> None:
+        self.gui_handle = viser_server.add_gui_vectorN(
+            self.name, self.default_value, step=self.step, disabled=self.disabled, visible=self.visible, hint=self.hint
+        )
diff --git a/pyproject.toml b/pyproject.toml
index 5ac3f71b..7ed90adb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,7 +57,7 @@ dependencies = [
     "torchvision>=0.14.1",
     "torchmetrics[image]>=1.0.1",
     "typing_extensions>=4.4.0",
-    "viser==0.1.27",
+    "viser@git+https://github.com/atonderski/viser.git",
     "nuscenes-devkit>=1.1.1",
     "wandb>=0.13.3",
     "xatlas",
@@ -66,7 +66,10 @@ dependencies = [
     "gsplat>=0.1.9",
     "pytorch-msssim",
     "pathos",
-    "packaging"
+    "packaging",
+    "zod>=0.1.7",
+    "pandaset@git+https://github.com/scaleapi/pandaset-devkit.git#egg=pandaset&subdirectory=python",
+    "av2==0.2.1",
 ]
 
 [project.urls]
diff --git a/tests/cameras/test_rays.py b/tests/cameras/test_rays.py
index 43597978..d509d876 100644
--- a/tests/cameras/test_rays.py
+++ b/tests/cameras/test_rays.py
@@ -30,7 +30,7 @@ def test_frustum_get_position():
     assert positions == pytest.approx(target_position, abs=1e-6)
 
 
-def test_frustum_get_gaussian_blob():
+def test_frustum_get_conical_gaussian_blob():
     """Test gaussian blob calculation"""
 
     frustum = Frustums(
@@ -41,7 +41,7 @@ def test_frustum_get_gaussian_blob():
         pixel_area=torch.ones((5, 1)),
     )
 
-    gaussian_blob = frustum.get_gaussian_blob()
+    gaussian_blob = frustum.get_conical_gaussian_blob()
     assert gaussian_blob.mean.shape == (5, 3)
     assert gaussian_blob.cov.shape == (5, 3, 3)
 
@@ -72,4 +72,4 @@ def test_get_mock_frustum():
 
 
 if __name__ == "__main__":
-    test_frustum_get_gaussian_blob()
+    test_frustum_get_conical_gaussian_blob()
diff --git a/tests/process_data/test_misc.py b/tests/process_data/test_misc.py
index 1b2404b5..3a494194 100644
--- a/tests/process_data/test_misc.py
+++ b/tests/process_data/test_misc.py
@@ -39,7 +39,7 @@ def test_scalar_first_scalar_last_quaternions():
 
     # Expected Rotation matrix
     # fmt: off
-    R_expected = np.array( 
+    R_expected = np.array(
         [
             [ 0.81379768, -0.44096961,  0.37852231],
             [ 0.46984631,  0.88256412,  0.01802831],