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.

翻译：无监督点云形状对应旨在建立源点云与目标点云之间的逐点对应关系。现有方法通常通过直接计算点云间的逐点特征相似度来获取对应关系。然而，非刚性物体具有强可变形性与非常规形状，这使得直接建立非常规形状点云间的对应关系成为长期存在的挑战。针对该问题，本文提出了一种无监督模板辅助点云形状对应网络（TANet），包含模板生成模块与模板辅助模块。所提TANet具有多项优势：其一，模板生成模块建立了具有显式结构的可学习模板集合；其二，模板辅助模块通过深度利用生成的模板，从多视角建立更精确的形状对应关系。在四个人体与动物数据集上的大量实验表明，TANet取得了优于现有最优方法的性能。