We propose to represent shapes as the deformation and combination of
learnable elementary 3D structures, which are primitives resulting from
training over a collection of shape. We demonstrate that the learned elementary
3D structures lead to clear improvements in 3D shape generation and matching.
More precisely, we present two complementary approaches for learning elementary
structures: (i) patch deformation learning and (ii) point translation learning.
Both approaches can be extended to abstract structures of higher dimensions for
improved results. We evaluate our method on two tasks: reconstructing ShapeNet
objects and estimating dense correspondences between human scans (FAUST inter
challenge). We show 16% improvement over surface deformation approaches for
shape reconstruction and outperform FAUST inter challenge state of the art by
6%.

本文提出将形状表示为可学习的三维基本结构的变形和组合，并演示了学习到的基本结构在三维形状生成和匹配方面的显著改进，通过区块变形学习和点翻译学习两种方法，在 ShapeNet 对象重建和人体扫描之间的稠密对应估计中取得了比现有方法更好的结果。