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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.26481 |
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| _version_ | 1866918430459822080 |
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| author | Pan, Weihong Zhang, Xiaoyu Zhang, Zhuang Ye, Zhichao Wang, Nan Liu, Haomin Zhang, Guofeng |
| author_facet | Pan, Weihong Zhang, Xiaoyu Zhang, Zhuang Ye, Zhichao Wang, Nan Liu, Haomin Zhang, Guofeng |
| contents | High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense arrays of tens or even hundreds of synchronized cameras. Dependence on such costly lab setups severely limits practical scalability. To this end, we propose a sparse-camera dynamic reconstruction framework that exploits abundant yet inconsistent generative observations. Our key innovation is the Spatio-Temporal Distortion Field, which provides a unified mechanism for modeling inconsistencies in generative observations across both spatial and temporal dimensions. Building on this, we develop a complete pipeline that enables 4D reconstruction from sparse and uncalibrated camera inputs. We evaluate our method on multi-camera dynamic scene benchmarks, achieving spatio-temporally consistent high-fidelity renderings and significantly outperforming existing approaches. Project page available at https://inspatio.github.io/sparse-cam4d/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras Pan, Weihong Zhang, Xiaoyu Zhang, Zhuang Ye, Zhichao Wang, Nan Liu, Haomin Zhang, Guofeng Computer Vision and Pattern Recognition High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense arrays of tens or even hundreds of synchronized cameras. Dependence on such costly lab setups severely limits practical scalability. To this end, we propose a sparse-camera dynamic reconstruction framework that exploits abundant yet inconsistent generative observations. Our key innovation is the Spatio-Temporal Distortion Field, which provides a unified mechanism for modeling inconsistencies in generative observations across both spatial and temporal dimensions. Building on this, we develop a complete pipeline that enables 4D reconstruction from sparse and uncalibrated camera inputs. We evaluate our method on multi-camera dynamic scene benchmarks, achieving spatio-temporally consistent high-fidelity renderings and significantly outperforming existing approaches. Project page available at https://inspatio.github.io/sparse-cam4d/ |
| title | SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.26481 |