VoMP: Predicting Volumetric Mechanical Property Fields
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.
690051e222d452aac6dd4400
Rishit Dagli
claimed_verified
2026-03-03T10:45:32.665Z
60796959c59d9e1697fa2324
https://cdn-avatars.huggingface.co/v1/production/uploads/60796959c59d9e1697fa2324/wxnDm-p3YgB95NV2p4LGF.png
Rishit Dagli
rishitdagli
user
rishitdagli
690051e222d452aac6dd4401
Donglai Xiang
690051e222d452aac6dd4402
Vismay Modi
690051e222d452aac6dd4403
Charles Loop
690051e222d452aac6dd4404
Clement Fuji Tsang
690051e222d452aac6dd4405
Anka He Chen
690051e222d452aac6dd4406
Anita Hu
690051e222d452aac6dd4407
Gavriel State
690051e222d452aac6dd4408
David I. W. Levin
690051e222d452aac6dd4409
Maria Shugrina
VoMP: Predicting Volumetric Mechanical Property Fields
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.
VoMP uses a feed-forward method with a Geometry Transformer to predict accurate volumetric material properties from 3D objects, outperforming existing methods in both accuracy and speed.
feed-forward method Young's modulus Poisson's ratio density 3D objects voxelization per-voxel multi-view features Geometry Transformer material latent codes manifold of physically plausible materials annotation pipeline segmented 3D datasets material databases vision-language model benchmark