In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D Gaussian Splatting stands out for its explicit representation of scenes using 3D Gaussians, making it appealing for tasks like 3D point cloud extraction and surface reconstruction. Motivated by its potential, we address the domain of 3D scene reconstruction, aiming to leverage the capabilities of the Microsoft HoloLens 2 for instant 3D Gaussian Splatting. We present HoloGS, a novel workflow utilizing HoloLens sensor data, which bypasses the need for pre-processing steps like Structure from Motion by instantly accessing the required input data i.e. the images, camera poses and the point cloud from depth sensing. We provide comprehensive investigations, including the training process and the rendering quality, assessed through the Peak Signal-to-Noise Ratio, and the geometric 3D accuracy of the densified point cloud from Gaussian centers, measured by Chamfer Distance. We evaluate our approach on two self-captured scenes: An outdoor scene of a cultural heritage statue and an indoor scene of a fine-structured plant. Our results show that the HoloLens data, including RGB images, corresponding camera poses, and depth sensing based point clouds to initialize the Gaussians, are suitable as input for 3D Gaussian Splatting.
在摄影测量学、计算机视觉和计算机图形学领域，神经网络3D场景重建的任务促使人们探索了各种技术。其中，3D高斯喷溅技术因其使用3D高斯显式表示场景而脱颖而出，这使其在3D点云提取和表面重建等任务中显得非常吸引人。受到其潜力的激励，我们致力于3D场景重建领域，目标是利用微软HoloLens 2的能力进行即时的3D高斯喷溅。我们提出了一种名为HoloGS的新型工作流程，该流程利用HoloLens传感器数据，无需进行运动恢复结构等预处理步骤，即可直接获取所需的输入数据，即图像、相机位置和来自深度感测的点云。我们进行了全面的调查，包括训练过程和通过峰值信噪比评估的渲染质量，以及通过钱伯斯距离测量的来自高斯中心的密集点云的几何3D精度。我们在两个自采集的场景上评估了我们的方法：一个是户外的文化遗产雕像场景，另一个是室内的细结构植物场景。我们的结果显示，HoloLens的数据，包括RGB图像、相应的相机位置和基于深度感测的点云以初始化高斯分布，适合作为3D高斯喷溅的输入。