  Unsupervised point cloud shape correspondence aims to establish point-wise
correspondences between source and target point clouds. Existing methods obtain
correspondences directly by computing point-wise feature similarity between
point clouds. However, non-rigid objects possess strong deformability and
unusual shapes, making it a longstanding challenge to directly establish
correspondences between point clouds with unconventional shapes. To address
this challenge, we propose an unsupervised Template-Assisted point cloud shape
correspondence Network, termed TANet, including a template generation module
and a template assistance module. The proposed TANet enjoys several merits.
Firstly, the template generation module establishes a set of learnable
templates with explicit structures. Secondly, we introduce a template
assistance module that extensively leverages the generated templates to
establish more accurate shape correspondences from multiple perspectives.
Extensive experiments on four human and animal datasets demonstrate that TANet
achieves favorable performance against state-of-the-art methods.
