OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915644266512384 |
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| author | Liu, Shiyong Tang, Xiao Li, Zhihao He, Yingfan Ye, Chongjie Liu, Jianzhuang Huang, Binxiao Zhou, Shunbo Wu, Xiaofei |
| author_facet | Liu, Shiyong Tang, Xiao Li, Zhihao He, Yingfan Ye, Chongjie Liu, Jianzhuang Huang, Binxiao Zhou, Shunbo Wu, Xiaofei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering Liu, Shiyong Tang, Xiao Li, Zhihao He, Yingfan Ye, Chongjie Liu, Jianzhuang Huang, Binxiao Zhou, Shunbo Wu, Xiaofei Graphics Computer Vision and Pattern Recognition 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. |
| title | OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.16177 |