RoDyGS: Robust Dynamic Gaussian Splatting for Casual Videos
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909015032725504 |
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| author | Lee, Junmyeong Choi, Hoseung Jeong, Yoonwoo Cho, Minsu |
| author_facet | Lee, Junmyeong Choi, Hoseung Jeong, Yoonwoo Cho, Minsu |
| contents | 4D reconstruction from casually captured monocular videos is challenging due to inherent ambiguity in reconstructing dynamic 3D geometry. To address this challenge, we introduce Robust Dynamic Gaussian Splatting (RoDyGS), a method that reconstructs dynamic scene representation from casual monocular videos. RoDyGS explicitly separates static and dynamic scene elements, and applies spatiotemporal regularization to enforce physically plausible geometry and temporally consistent motion. Furthermore, we propose a comprehensive benchmark, Kubric-MRig, which provides extensive camera and object motion along with simultaneous multi-view capture, features that are absent in previous benchmarks. Experiments demonstrate that RoDyGS significantly outperforms previous pose-free dynamic novel view synthesis approaches and achieves competitive rendering quality compared to existing pose-free static novel view synthesis approaches. Our proejct page is available at https://rodygs.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RoDyGS: Robust Dynamic Gaussian Splatting for Casual Videos Lee, Junmyeong Choi, Hoseung Jeong, Yoonwoo Cho, Minsu Computer Vision and Pattern Recognition 4D reconstruction from casually captured monocular videos is challenging due to inherent ambiguity in reconstructing dynamic 3D geometry. To address this challenge, we introduce Robust Dynamic Gaussian Splatting (RoDyGS), a method that reconstructs dynamic scene representation from casual monocular videos. RoDyGS explicitly separates static and dynamic scene elements, and applies spatiotemporal regularization to enforce physically plausible geometry and temporally consistent motion. Furthermore, we propose a comprehensive benchmark, Kubric-MRig, which provides extensive camera and object motion along with simultaneous multi-view capture, features that are absent in previous benchmarks. Experiments demonstrate that RoDyGS significantly outperforms previous pose-free dynamic novel view synthesis approaches and achieves competitive rendering quality compared to existing pose-free static novel view synthesis approaches. Our proejct page is available at https://rodygs.github.io |
| title | RoDyGS: Robust Dynamic Gaussian Splatting for Casual Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.03077 |