VoMP: Predicting Volumetric Mechanical Property Fields

• URL: http://arxiv.org/abs/2510.22975v1
• Date: Mon, 27 Oct 2025 03:56:25 GMT
• Title: VoMP: Predicting Volumetric Mechanical Property Fields
• Authors: Rishit Dagli, Donglai Xiang, Vismay Modi, Charles Loop, Clement Fuji Tsang, Anka He Chen, Anita Hu, Gavriel State, David I. W. Levin, Maria Shugrina,
• Abstract summary: VoMP is a feed-forward method trained to predict Young's modulus ($E$), Poisson's ratio ($nu$), and density ($rho$) throughout the volume of 3D objects.<n>It aggregates per-voxel multi-view features and passes them to our trained Geometry Transformer to predict per-voxel material latent codes.<n>Experiments show that VoMP estimates accurate volumetric properties, far outperforming prior art in accuracy and speed.
• Score: 12.504007202543784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical simulation relies on spatially-varying mechanical properties, often laboriously hand-crafted. VoMP is a feed-forward method trained to predict Young's modulus ($E$), Poisson's ratio ($\nu$), and density ($\rho$) throughout the volume of 3D objects, in any representation that can be rendered and voxelized. VoMP aggregates per-voxel multi-view features and passes them to our trained Geometry Transformer to predict per-voxel material latent codes. These latents reside on a manifold of physically plausible materials, which we learn from a real-world dataset, guaranteeing the validity of decoded per-voxel materials. To obtain object-level training data, we propose an annotation pipeline combining knowledge from segmented 3D datasets, material databases, and a vision-language model, along with a new benchmark. Experiments show that VoMP estimates accurate volumetric properties, far outperforming prior art in accuracy and speed.

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