Current novel view synthesis tasks primarily rely on high-quality and clear images. However, in foggy scenes, scattering and attenuation can significantly degrade the reconstruction and rendering quality. Although NeRF-based dehazing reconstruction algorithms have been developed, their use of deep fully connected neural networks and per-ray sampling strategies leads to high computational costs. Moreover, NeRF's implicit representation struggles to recover fine details from hazy scenes. In contrast, recent advancements in 3D Gaussian Splatting achieve high-quality 3D scene reconstruction by explicitly modeling point clouds into 3D Gaussians. In this paper, we propose leveraging the explicit Gaussian representation to explain the foggy image formation process through a physically accurate forward rendering process. We introduce DehazeGS, a method capable of decomposing and rendering a fog-free background from participating media using only muti-view foggy images as input. We model the transmission within each Gaussian distribution to simulate the formation of fog. During this process, we jointly learn the atmospheric light and scattering coefficient while optimizing the Gaussian representation of the hazy scene. In the inference stage, we eliminate the effects of scattering and attenuation on the Gaussians and directly project them onto a 2D plane to obtain a clear view. Experiments on both synthetic and real-world foggy datasets demonstrate that DehazeGS achieves state-of-the-art performance in terms of both rendering quality and computational efficiency.
当前的新视图合成任务主要依赖高质量和清晰的图像。然而，在雾霾场景中，散射和衰减会显著降低重建和渲染质量。尽管基于 NeRF 的去雾重建算法已经被开发出来，但其深度全连接神经网络和逐射线采样策略导致了较高的计算成本。此外，NeRF 的隐式表示在恢复雾霾场景的细节方面存在困难。相比之下，近期 3D 高斯点绘制（3D Gaussian Splatting）的进展通过将点云显式建模为 3D 高斯分布，实现了高质量的 3D 场景重建。 本文提出了 DehazeGS，一种基于显式高斯表示的方法，通过物理准确的前向渲染过程解释雾霾图像的形成过程。DehazeGS 能够利用多视角雾霾图像作为输入，分解并渲染出无雾的背景。我们在每个高斯分布内建模光的传输过程，以模拟雾霾的形成。在这一过程中，我们联合学习大气光和散射系数，同时优化雾霾场景的高斯表示。 在推理阶段，我们消除散射和衰减对高斯分布的影响，并将其直接投影到二维平面，获得清晰的视图。基于合成和真实雾霾数据集的实验表明，DehazeGS 在渲染质量和计算效率方面均达到了当前最先进的水平。