| title | Adaptive Volumetric Mechanical Property Fields Invariant to Resolution |
|---|---|
| authors | Rishit Dagli, Donglai Xiang, Vismay Modi, Xuning Yang, Gavriel State, David I. W. Levin, Maria Shugrina |
| date | 2026-06-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.18231 |
| affiliation | NVIDIA |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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 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 16^3times 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. |
cite
@misc{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},
eprint={2606.18231},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.18231}
}