CNNメッシュ埋め込みによる薄殻変形材料のリアルタイムシミュレーション
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding
グラフ畳み込みネットワークと物理に着想を得た損失で薄殻変形物体の高次元形状を低次元特徴空間に埋め込み、RNNで時間発展も学習することで500〜10000倍高速な学習型物理シミュレータを実現した。
著者: Qingyang Tan, Zherong Pan, Lin Gao, Dinesh Manocha
分類: cs.GR, cs.CV, cs.RO
原文アブストラクト
We address the problem of accelerating thin-shell deformable object simulations by dimension reduction. We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space, where the configurations of objects and feature points have approximate one-to-one mapping. Our key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies and a new mesh embedding approach based on physics-inspired loss term. We have applied our approach to accelerate high-resolution thin shell simulations corresponding to cloth-like materials, where the configuration space has tens of thousands of degrees of freedom. We show that our physics-inspired embedding approach leads to higher accuracy compared with prior mesh embedding methods. Finally, we show that the temporal evolution of the mesh in the feature space can also be learned using a recurrent neural network (RNN) leading to fully learnable physics simulators. After training our learned simulator runs $500-10000\times$ faster and the accuracy is high enough for robot manipulation tasks.
