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：本文探讨通过点级排序从三维点云中寻找代表性点子集的任务。目前仅有少数研究尝试解决这一具有挑战性的视觉问题，且均依赖于难以获取的点与点云标签。与这些工作不同，我们提出通过自监督学习实现三维点云点级排序的任务，称之为自排序。我们进一步贡献了首个可端到端训练的自监督点级排序网络，该网络采用新颖的可微分点评分-排序策略，并构建分层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和任务（包括未见类别点云的零样本排序）中具有可扩展性，且性能甚至优于有监督排序方法。