VoMP: Predicting Volumetric Mechanical Property Fields

📝 Original Info

• Title: VoMP: Predicting Volumetric Mechanical Property Fields
• ArXiv ID: 2510.22975
• Date: 2025-10-27
• Authors: 정보 없음 (제공된 텍스트에 저자 정보가 포함되어 있지 않음)

📝 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.

💡 Deep Analysis

📄 Full Content

Reference