In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering. Only a few works have tried to address this challenging vision problem, all with the help of hard to obtain point and cloud labels. Different from these works, we introduce the task of point-wise ordering in 3D point clouds through self-supervision, which we call self-ordering. We further contribute the first end-to-end trainable network that learns a point-wise ordering in a self-supervised fashion. It utilizes a novel differentiable point scoring-sorting strategy and it constructs an hierarchical contrastive scheme to obtain self-supervision signals. We extensively ablate the method and show its scalability and superior performance even compared to supervised ordering methods on multiple datasets and tasks including zero-shot ordering of point clouds from unseen categories.

翻译：本文探讨了通过逐点排序从三维点云中寻找代表性点子集的任务。仅有少数研究工作尝试解决这一具有挑战性的视觉问题，且均依赖难以获取的点标签与点云标签。不同于这些方法，我们提出通过自监督学习实现三维点云中的逐点排序任务，称之为“自排序”。我们进一步贡献了首个以自监督方式学习逐点排序的端到端可训练网络。该网络采用新型可微点评分-排序策略，并构建了层次化对比方案以获取自监督信号。我们对该方法进行了全面消融实验，结果表明其在多个数据集与任务（包括未见类别点云的零样本排序）中均展现出可扩展性及优于监督排序方法的性能。