  Shape matching has been a long-studied problem for the computer graphics and
vision community. The objective is to predict a dense correspondence between
meshes that have a certain degree of deformation. Existing methods either
consider the local description of sampled points or discover correspondences
based on global shape information. In this work, we investigate a hierarchical
learning design, to which we incorporate local patch-level information and
global shape-level structures. This flexible representation enables
correspondence prediction and provides rich features for the matching stage.
Finally, we propose a novel optimal transport solver by recurrently updating
features on non-confident nodes to learn globally consistent correspondences
between the shapes. Our results on publicly available datasets suggest robust
performance in presence of severe deformations without the need for extensive
training or refinement.
