AdaVoMP能预测3D物体的机械属性，分辨率比最好方法高16^3倍，还省计算，适合物理仿真。
AdaVoMP提出预测3D物体杨氏模量（E）、泊松比（ν）和密度（ρ）的密集空间变化分布。它使用稀疏自适应体素结构SAV，通过稀疏Transformer编码器-解码器自回归生成每输入形状的独特SAV。相比最准确的前期方法VoMP，分辨率提高16^3倍。实验表明，AdaVoMP在测试时计算量更少的情况下估计更准确的体积属性。可将高分辨率复杂3D物体转换为可仿真的资产，实现逼真的可变形模拟。
Adaptive Volumetric Mechanical Property Fields Invariant to Resolution
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.