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arXiv · 2603.26481

SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras

Weihong Pan·Xiaoyu Zhang·Zhuang Zhang·Zhichao Ye·Nan Wang·Haomin Liu·Guofeng Zhang
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Abstract

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/

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Weihong Pan, Xiaoyu Zhang, Zhuang Zhang, Zhichao Ye, Nan Wang, Haomin Liu, Guofeng Zhang. 2026-03-27. SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras. https://arxiv.org/abs/2603.26481

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