{"ID":2849441,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22975","arxiv_id":"2510.22975","title":"VoMP: Predicting Volumetric Mechanical Property Fields","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 ($ν$), and density ($ρ$) 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.","short_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 ($ν$), and density ($ρ$) throughout the volume of 3D objects, in any representation that can be rendered and voxelized. VoMP aggr...","url_abs":"https://arxiv.org/abs/2510.22975","url_pdf":"https://arxiv.org/pdf/2510.22975v2","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\"]","published":"2025-10-27T03:56:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.GR\",\"cs.LG\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}