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

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• SparseCam4D enables high-quality 4D reconstruction from sparse and uncalibrated camera inputs by exploiting abundant yet inconsistent generative observations.method 95%
• The Spatio-Temporal Distortion Field is a key innovation for modeling inconsistencies in generative observations across spatial and temporal dimensions.method 95%
• SparseCam4D achieves spatio-temporally consistent high-fidelity renderings and significantly outperforms existing approaches on multi-camera dynamic scene benchmarks.result 95%
• SparseCam4D achieves higher PSNR, SSIM, and lower LPIPS compared to MonoFusion*, 4DGaussians, and other baselines on dynamic scene reconstruction.result 95%
• Removing the Spatio-Temporal Distortion Field leads to severe blur and temporal instability in the reconstructed 4D scenes.result 90%
• The framework is implemented in PyTorch and uses the Real-Time4DGS codebase as a foundation.technical 90%
• Direct reconstruction from diffusion observations can lead to severe oversmoothness that paradoxically favors PSNR computation, hindering accurate reflection of changes.limitation 85%

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