对分辨率具有鲁棒性的自适应体积力学性质场
Adaptive Volumetric Mechanical Property Fields Invariant to Resolution
摘要
准确的力学性质（或材料特性），如杨氏模量（E）、泊松比（ν）和密度（ρ），对于数字世界的可靠物理模拟至关重要。不过，大多数3D模型都缺乏这些信息。我们提出了AdaVoMP方法，该方法能够针对各种表示形式下的输入3D对象，准确预测其密集且空间上变化的E、ν、ρ值。与现有技术相比，该方法的分辨率、准确性以及内存使用效率都有显著提升。我们的技术基础是一个稀疏且可适应的体素结构SAV，它能够有效表示输入的3D形状以及对应的材料属性。我们用这种新型稀疏变换器编码器-解码器模型取代了最精确的VoMP方法的固定体素模型——这种模型能够自学习地生成适用于每个输入形状的SAV结构，从而准确描述其材料特性。其分辨率是现有技术的16^3倍。实验表明，即使计算成本较低，AdaVoMP也能获得更精确的体积性质数据。这使我们能够将高分辨率的复杂3D模型转化为适合模拟的资产，进而实现真实且可变形的模拟效果。
English 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.