OccluGaussian：面向大场景重建与渲染的遮挡感知高斯点云技术
OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering
AI总结：
OccluGaussian通过遮挡感知的场景划分和区域渲染技术，提升大规模场景重建与渲染的质量和效率。
摘要：
在使用3D高斯点云进行大规模场景重建时，通常将场景划分为多个较小区域并单独重建。然而，现有划分方法缺乏遮挡感知，导致每个区域可能包含严重的遮挡区域。因此，这些区域内相机之间的相关性较低，导致整体重建的平均贡献较低。本文提出了一种遮挡感知的场景划分策略，通过基于位置和共可见性对训练相机进行聚类，获取多个区域。此类区域内的相机相关性更强，平均贡献更高，有助于高质量的场景重建。我们进一步提出了一种基于区域的渲染技术以加速大规模场景渲染，该技术会剔除对当前视角所在区域不可见的高斯点云。这种技术显著提高了渲染速度，而不会影响质量。在多个大规模场景上的大量实验表明，我们的方法在重建结果和渲染速度上均优于现有最先进方法。项目页面：https://occlugaussian.github.io。
英文摘要：
In large-scale scene reconstruction using 3D Gaussian splatting, it is common to partition the scene into multiple smaller regions and reconstruct them individually. However, existing division methods are occlusion-agnostic, meaning that each region may contain areas with severe occlusions. As a result, the cameras within those regions are less correlated, leading to a low average contribution to the overall reconstruction. In this paper, we propose an occlusion-aware scene division strategy that clusters training cameras based on their positions and co-visibilities to acquire multiple regions. Cameras in such regions exhibit stronger correlations and a higher average contribution, facilitating high-quality scene reconstruction. We further propose a region-based rendering technique to accelerate large scene rendering, which culls Gaussians invisible to the region where the viewpoint is located. Such a technique significantly speeds up the rendering without compromising quality. Extensive experiments on multiple large scenes show that our method achieves superior reconstruction results with faster rendering speed compared to existing state-of-the-art approaches. Project page: https://occlugaussian.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
