3D point clouds are a crucial type of data collected by LiDAR sensors and widely used in transportation applications due to its concise descriptions and accurate localization. Deep neural networks (DNNs) have achieved remarkable success in processing large amount of disordered and sparse 3D point clouds, especially in various computer vision tasks, such as pedestrian detection and vehicle recognition. Among all the learning paradigms, Self-Supervised Learning (SSL), an unsupervised training paradigm that mines effective information from the data itself, is considered as an essential solution to solve the time-consuming and labor-intensive data labelling problems via smart pre-training task design. This paper provides a comprehensive survey of recent advances on SSL for point clouds. We first present an innovative taxonomy, categorizing the existing SSL methods into four broad categories based on the pretexts' characteristics. Under each category, we then further categorize the methods into more fine-grained groups and summarize the strength and limitations of the representative methods. We also compare the performance of the notable SSL methods in literature on multiple downstream tasks on benchmark datasets both quantitatively and qualitatively. Finally, we propose a number of future research directions based on the identified limitations of existing SSL research on point clouds.

翻译：3D点云是由LiDAR传感器采集的关键数据类型，因其描述简洁、定位精确而在交通应用中被广泛使用。深度神经网络在处理大量无序稀疏的3D点云方面取得了显著成功，尤其在行人检测和车辆识别等计算机视觉任务中表现突出。在所有学习范式中，自监督学习（SSL）作为一种从数据本身挖掘有效信息的无监督训练范式，通过巧妙的预训练任务设计，被认为是解决耗时费力数据标注问题的关键方案。本文对点云自监督学习的最新进展进行了全面综述。首先提出一种创新性分类法，根据预任务特征将现有SSL方法划分为四大类别。随后对每类方法进行更细粒度的分组，总结代表性方法的优势与局限。通过定量与定性分析，我们系统比较了文献中典型SSL方法在基准数据集多个下游任务上的性能表现。最后，基于现有点云SSL研究存在的局限，提出了若干未来研究方向。