VoMP: Predicting Volumetric Mechanical Property Fields
Authors: name: Rishit Dagli, name: Donglai Xiang, name: Vismay Modi, name: Charles Loop, name: Clement Fuji Tsang, name: Anka He Chen, name: Anita Hu, name: Gavriel State, name: David I. W. Levin, name: Maria Shugrina
Organization: NVIDIA

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.