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.

非监督点云形状对应旨在建立源点云和目标点云之间的点对点对应关系。本文提出了一种无监督模板辅助点云形状对应网络（TANet），包括模板生成模块和模板辅助模块。通过多方面运用生成的模板来建立更准确的形状对应关系，TANet 在人类和动物数据集上的广泛实验表明其在性能上胜过现有的方法。