  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.
