Paper Detail
Rishit Dagli, Donglai Xiang, Vismay Modi, Xuning Yang, Gavriel State, David I. W. Levin, Maria Shugrina
Accurate mechanical properties (or materials) Young's modulus ($E$), Poisson's ratio ($ν$) and density ($ρ$) are essential for reliable physics simulation of digital worlds, but most 3D assets lack this information. We propose AdaVoMP, a method for predicting accurate dense spatially-varying ($E$, $ν$, $ρ$) for input 3D objects across representations, improving the resolution, accuracy, and memory efficiency over the state-of-the-art. The foundation of our technique is a sparse and adaptive voxel structure SAV that efficiently represents both the input 3D shape and the material field output. We replace the fixed-voxel model of the most accurate prior method, VoMP, with a novel sparse transformer encoder-decoder model that learns to generate a unique SAV autoregressively for every input shape to represent its materials, achieving a resolution $16^3\times$ higher than prior art. Experiments show that AdaVoMP estimates more accurate volumetric properties, even with lesser test-time compute than all prior art. This allows us to convert high-resolution complex 3D objects into simulation-ready assets, resulting in realistic deformable simulations.
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@article{dagli2026adaptive,
title = {Adaptive Volumetric Mechanical Property Fields Invariant to Resolution},
author = {Rishit Dagli and Donglai Xiang and Vismay Modi and Xuning Yang and Gavriel State and David I. W. Levin and Maria Shugrina},
year = {2026},
abstract = {Accurate mechanical properties (or materials) Young's modulus (\$E\$), Poisson's ratio (\$ν\$) and density (\$ρ\$) are essential for reliable physics simulation of digital worlds, but most 3D assets lack this information. We propose AdaVoMP, a method for predicting accurate dense spatially-varying (\$E\$, \$ν\$, \$ρ\$) for input 3D objects across representations, improving the resolution, accuracy, and memory efficiency over the state-of-the-art. The foundation of our technique is a sparse and adaptive voxe},
url = {https://arxiv.org/abs/2606.18231},
keywords = {cs.CV, cs.LG, cs.RO},
eprint = {2606.18231},
archiveprefix = {arXiv},
}
{}