PhysGS: Bayesian-Inferred Gaussian Splatting for Physical Property Estimation
This enables robots to interact more safely and effectively with their surroundings by inferring physical properties, representing a novel domain-specific advancement rather than an incremental improvement.
The paper tackles the problem of estimating dense physical properties like mass, hardness, and friction from visual data, which existing 3D reconstruction methods cannot do, and achieves improvements such as up to 22.8% better mass estimation accuracy and up to 61.2% lower hardness error compared to baselines.
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.