提出渐进联合优化框架，提升稀疏视角下物理参数估计精度。
ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
- 分阶段逐步增加联合优化参数，避免误差累积
- 合成与真实数据上几何精度提升最高10倍
- 适合机器人、XR等需物理准确数字孪生的场景
神经渲染在3D重建与新视角生成方面进展显著，将物理信息融入其中可拓展至机器人和扩展现实（XR）的物理准确数字孪生应用。然而，从视觉观测中反演物理参数的逆问题仍具挑战性。现有物理感知神经渲染方法通常需要密集多视角视频，难以在真实世界中规模化部署。在稀疏视角设置下，当前方法采用的序列优化策略存在严重误差累积：初始3D重建不准确会传播至后续阶段，导致物理状态和材料参数估计性能下降。另一方面，同时优化所有参数因问题高度非凸且常不可微而失败。本文提出ProJo4D，一种渐进联合优化框架，逐步扩展联合优化的参数集。该设计使物理信息梯度能有效修正几何，同时避免对全部参数直接联合优化带来的不稳定性。在合成与真实世界数据集上的评估表明，ProJo4D在4D未来状态预测和物理参数估计方面显著优于先前方法，几何精度最高提升10倍，同时保持计算效率。
原文摘要 · Abstract (English)
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. Under sparse-view settings, the sequential optimization strategies employed by current approaches suffer from severe error accumulation: inaccuracies in initial 3D reconstruction propagate to subsequent stages, degrading physical state and material parameter estimates. On the other hand, simultaneous optimization of all parameters fails due to the highly non-convex and often non-differentiable nature of the problem. We propose ProJo4D, a progressive joint optimization framework that gradually expands the set of jointly optimized parameters. This design enables physics-informed gradients to refine geometry while avoiding the instability of direct joint optimization over all parameters. Evaluations on synthetic and real-world datasets demonstrate that ProJo4D substantially outperforms prior work in 4D future state prediction and physical parameter estimation, achieving up to 10x improvement in geometric accuracy while maintaining computational efficiency. Please visit the project webpage: https://daniel03c1.github.io/ProJo4D/
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