This paper addresses the problem of computing dense correspondence between 3D shapes in the form of point clouds, which is a challenging and fundamental problem in computer vision and digital geometry processing. Conventional approaches often solve the problem in a supervised manner, requiring massive annotated data, which is difficult and/or expensive to obtain. Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -- the first unsupervised and end-to-end deep learning-based framework -- to drive the learning of dense correspondence by means of deformation-like reconstruction to overcome the need for annotated data. Specifically, CorrNet3D consists of a deep feature embedding module and two novel modules called correspondence indicator and symmetric deformation. Feeding a pair of raw point clouds, our model first learns the pointwise features and passes them into the indicator to generate a learnable correspondence matrix used to permute the input pair. The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence. The extensive experiments on both synthetic and real-world datasets of rigid and non-rigid 3D shapes show our CorrNet3D outperforms state-of-the-art methods to a large extent, including those taking meshes as input. CorrNet3D is a flexible framework in that it can be easily adapted to supervised learning if annotated data are available.

本文介绍了一种基于无监督和端到端深度学习的框架CorrNet3D，以学习3D形状之间的密集对应关系，并证明了其相对于最先进的方法具有更好的性能，源代码和预先训练好的模型可通过链接获得。

CorrNet3D: 3D点云密集对应的无监督端到端学习