OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering
This work addresses inefficiencies in large scene reconstruction and rendering for computer vision and graphics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of low-quality reconstruction and slow rendering in large-scale 3D Gaussian splatting by proposing an occlusion-aware scene division strategy and region-based rendering, achieving superior reconstruction results and faster rendering speed compared to state-of-the-art methods.
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.