  Point cloud data has been extensively studied due to its compact form and
flexibility in representing complex 3D 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 computer vision,
robotics, and autonomous driving, all of which require an understanding of the
underlying 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. This approach aims to learn
generic and useful point cloud representations from unlabeled data,
circumventing the need for extensive manual annotations. 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. We then briefly introduce the commonly used datasets and
evaluation metrics. Following that, we delve into an extensive exploration of
self-supervised point cloud representation learning methods based on these
techniques. Finally, we share our thoughts on some of the challenges and
potential issues that future research in self-supervised learning for
pre-training 3D point clouds may encounter.
