Geometric Continuity and Consistency Learning for Self-Supervised Point Cloud Completion

Point cloud completion aims to infer the complete point clouds from incomplete ones. In real-world scenarios, where the paired data is absent, self-supervised methods have emerged as a promising solution. Although existing self-supervised methods perform well at relatively low resolutions, they suffer significant performance degradation at higher resolution primarily because they focus on point cloud reconstruction at patch-level or point-level. In this paper, we propose a self-supervised method based on Geometric Continuity and Consistency Learning (GCCL) at multi-scale level to improve the accuracy of predicting local details and global shapes of point clouds. Specifically, to capture local details, we employ a patch-topoint strategy and a coarse-fine manner for geometric continuity learning. To constrain the global shapes, we construct multiple branches for mutual supervision and utilize class priors to build a memory queue for contrasting current features, enhancing the network focus on geometric consistency learning. We evaluate GCCL on multiple datasets, and the results show that our method outperforms existing self-supervised methods by a 4.4 improvement in CD-$\ell_2$ on the synthetic PCN dataset and can generate more uniformly distributed completion results on realworld datasets