单目视频中联合优化物体几何、外观与物理参数
MonoPhysics: Estimating Geometry, Appearance, and Physical Parameters from Monocular Videos
- 用可微分MPM模拟和3D高斯溅射实现单视角联合优化
- 在弹性与塑性物体数据集上达到接近多视角基准的精度
- 解决尺度模糊问题，适合单摄像头物理重建场景
现有逆物理方法依赖多视角视频中的几何约束来恢复尺度与三维结构。但在单目情况下，此类约束缺失，导致严重尺度模糊、几何不准，且外观优化与物理模拟耦合弱。本文提出MonoPhysics，一种基于可微分MPM模拟和3D高斯溅射的单目逆物理估计框架，联合优化变形物体的几何、外观与物理参数。通过全局尺度对齐、物理感知几何精化及可微位置图三个视觉-物理桥梁，实现仅凭单摄像机视角的准确优化。在Vid2Sim及自建的弹性和塑性物体数据集上评估，MonoPhysics在单目设置下优于现有基线，并达到与多视角基线相当的性能。
原文摘要 · Abstract (English)
Existing inverse physics methods recover physical parameters from multi-view videos, where geometric constraints across views resolve scale and 3D structure. In monocular settings, however, such constraints are absent, leading to severe scale ambiguity, inaccurate geometry, and weak coupling between appearance optimization and physical simulation. We propose MonoPhysics, a framework for monocular inverse physics estimation of deformable objects using differentiable MPM simulation and 3D Gaussian Splatting, which jointly optimizes geometry, appearance, and physical parameters from a single camera view. We address these challenges through three visual-physical bridges: global scale alignment, physics-aware geometry refinement, and a differentiable position map, which together enable accurate optimization from monocular observations alone. We evaluate on Vid2Sim and our new dataset of elastic and plastic objects, showing that MonoPhysics outperforms existing baselines in monocular settings and achieves performance comparable to multi-view baselines using only a single camera. Our project page is available at https://daniel03c1.github.io/MonoPhysics/
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