Self-supervised learning for pre-training 3D point clouds: A survey

Point cloud data have been extensively studied due to their compact form and flexibility in representing complex 3D geometries and structures. The ability of point cloud data to accurately capture and represent intricate 3D geometry makes it an ideal choice for a wide range of applications, including 3D computer graphics, autonomous driving, robotics, and augmented reality, all of which require an understanding of the underlying geometry and spatial structures. Given the challenges associated with annotating large-scale point clouds, self-supervised point cloud representation learning has attracted increasing attention in recent years. It aims to learn generic and useful point cloud representations from unlabeled data, circumventing the need for extensive manual annotation. In this paper, we present a comprehensive survey of self-supervised point cloud representation learning using DNNs. We begin by presenting the motivation and general trends in recent research, then briefly introduce commonly used datasets and evaluation metrics. Next, we extensively explore self-supervised point cloud representation learning methods. Finally, we share our thoughts on some of the challenges and potential issues that future research into self-supervised learning for pre-training 3D point clouds may encounter. Our curated bibliography can be found at https://github.com/EtronTech/Awesome_3DSSL.