用视觉和语言先验推断物体每点的物理属性，提升机器人交互可靠性。
PhysGS: Bayesian-Inferred Gaussian Splatting for Physical Property Estimation
- 基于贝叶斯推理在高斯点云上迭代更新材料与属性信念
- 质量估计误差降22.8%，硬度误差降61.2%，摩擦误差降18.1%
- 支持不确定性建模，适合需安全决策的机器人场景
理解摩擦、刚度、硬度及材质等物理属性对机器人安全有效交互至关重要。现有3D重建方法仅关注几何与外观，无法推断底层物理属性。本文提出PhysGS，一种基于贝叶斯推理的3D高斯点云扩展方法，可从视觉线索和视觉-语言先验中估计密集的逐点物理属性。将属性估计建模为高斯点云上的贝叶斯推断，随着新观测不断更新材料与属性信念。PhysGS同时建模随机性与认知不确定性，实现不确定性的感知对象与场景解析。在物体尺度（ABO-500）、室内与室外真实数据集上，相比确定性基线，其质量估计精度提升最高达22.8%，肖氏硬度误差降低61.2%，动摩擦误差降低18.1%。结果表明，PhysGS在单一连续空间框架下统一了3D重建、不确定性建模与物理推理，实现密集物理属性估计。更多结果见https://samchopra2003.github.io/physgs。
原文摘要 · Abstract (English)
Understanding physical properties such as friction, stiffness, hardness, and material composition is essential for enabling robots to interact safely and effectively with their surroundings. However, existing 3D reconstruction methods focus on geometry and appearance and cannot infer these underlying physical properties. We present PhysGS, a Bayesian-inferred extension of 3D Gaussian Splatting that estimates dense, per-point physical properties from visual cues and vision--language priors. We formulate property estimation as Bayesian inference over Gaussian splats, where material and property beliefs are iteratively refined as new observations arrive. PhysGS also models aleatoric and epistemic uncertainties, enabling uncertainty-aware object and scene interpretation. Across object-scale (ABO-500), indoor, and outdoor real-world datasets, PhysGS improves accuracy of the mass estimation by up to 22.8%, reduces Shore hardness error by up to 61.2%, and lowers kinetic friction error by up to 18.1% compared to deterministic baselines. Our results demonstrate that PhysGS unifies 3D reconstruction, uncertainty modeling, and physical reasoning in a single, spatially continuous framework for dense physical property estimation. Additional results are available at https://samchopra2003.github.io/physgs.
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