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.

翻译：点云数据因其紧凑的表示形式和灵活表征复杂三维结构的能力而得到广泛研究。点云数据能够精确捕获并表征精细的三维几何结构，使其成为计算机视觉、机器人学和自动驾驶等需要理解底层空间结构的诸多应用中的理想选择。鉴于大规模点云标注面临的挑战，自监督点云表示学习近年来受到越来越多的关注。该方法旨在从无标注数据中学习通用且有效的点云特征表征，从而避免对大量人工标注的依赖。本文全面综述了基于深度神经网络的自监督点云表示学习方法。我们首先阐述研究动机和当前研究总体趋势，继而简要介绍常用数据集与评估指标。随后，我们深入探讨基于这些技术的自监督点云表示学习方法。最后，就三维点云预训练自监督学习未来可能面临的挑战与潜在问题提出我们的思考。