Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been steep progress in the supervised methods on the synthetic point cloud completion task, it is hardly applicable in real-world scenarios due to the domain gap between the synthetic and real-world datasets or the requirement of prior information. To overcome these limitations, we propose a novel self-supervised framework ACL-SPC for point cloud completion to train and test on the same data. ACL-SPC takes a single partial input and attempts to output the complete point cloud using an adaptive closed-loop (ACL) system that enforces the output same for the variation of an input. We evaluate our proposed ACL-SPC on various datasets to prove that it can successfully learn to complete a partial point cloud as the first self-supervised scheme. Results show that our method is comparable with unsupervised methods and achieves superior performance on the real-world dataset compared to the supervised methods trained on the synthetic dataset. Extensive experiments justify the necessity of self-supervised learning and the effectiveness of our proposed method for the real-world point cloud completion task. The code is publicly available from https://github.com/Sangminhong/ACL-SPC_PyTorch

本研究提出了一种新的自监督框架 ACL-SPC 用于点云完整性恢复的培训和测试在相同的数据上。ACL-SPC采用自适应闭环（ACL）系统，以在输入变化时强制输出相同的输出。实验结果表明，ACL-SPC 可以成功地学习完成部分点云并在真实世界数据集上比受过合成数据集训练的监督方法取得更好的性能，证明了自监督学习的必要性和所提出的方法的有效性。

ACL-SPC: 自监督点云补全的自适应闭环系统