DisC-GS: Discontinuity-aware Gaussian Splatting
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913567583764480 |
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| author | Qu, Haoxuan Li, Zhuoling Rahmani, Hossein Cai, Yujun Liu, Jun |
| author_facet | Qu, Haoxuan Li, Zhuoling Rahmani, Hossein Cai, Yujun Liu, Jun |
| contents | Recently, Gaussian Splatting, a method that represents a 3D scene as a collection of Gaussian distributions, has gained significant attention in addressing the task of novel view synthesis. In this paper, we highlight a fundamental limitation of Gaussian Splatting: its inability to accurately render discontinuities and boundaries in images due to the continuous nature of Gaussian distributions. To address this issue, we propose a novel framework enabling Gaussian Splatting to perform discontinuity-aware image rendering. Additionally, we introduce a Bézier-boundary gradient approximation strategy within our framework to keep the "differentiability" of the proposed discontinuity-aware rendering process. Extensive experiments demonstrate the efficacy of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DisC-GS: Discontinuity-aware Gaussian Splatting Qu, Haoxuan Li, Zhuoling Rahmani, Hossein Cai, Yujun Liu, Jun Computer Vision and Pattern Recognition Recently, Gaussian Splatting, a method that represents a 3D scene as a collection of Gaussian distributions, has gained significant attention in addressing the task of novel view synthesis. In this paper, we highlight a fundamental limitation of Gaussian Splatting: its inability to accurately render discontinuities and boundaries in images due to the continuous nature of Gaussian distributions. To address this issue, we propose a novel framework enabling Gaussian Splatting to perform discontinuity-aware image rendering. Additionally, we introduce a Bézier-boundary gradient approximation strategy within our framework to keep the "differentiability" of the proposed discontinuity-aware rendering process. Extensive experiments demonstrate the efficacy of our framework. |
| title | DisC-GS: Discontinuity-aware Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15196 |