AI RESEARCH
ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
arXiv CS.CV
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ArXi:2506.05317v3 Announce Type: replace Neural rendering has advanced significantly in 3D reconstruction and novel view synthesis, and integrating physics into these frameworks opens new applications such as physically accurate digital twins for robotics and XR. However, the inverse problem of estimating physical parameters from visual observations remains challenging. Existing physics-aware neural rendering methods typically require dense multi-view videos, making them impractical for scalable, real-world deployment.