  We present a review of 3D point cloud processing and learning for autonomous
driving. As one of the most important sensors in autonomous vehicles, light
detection and ranging (LiDAR) sensors collect 3D point clouds that precisely
record the external surfaces of objects and scenes. The tools for 3D point
cloud processing and learning are critical to the map creation, localization,
and perception modules in an autonomous vehicle. While much attention has been
paid to data collected from cameras, such as images and videos, an increasing
number of researchers have recognized the importance and significance of LiDAR
in autonomous driving and have proposed processing and learning algorithms to
exploit 3D point clouds. We review the recent progress in this research area
and summarize what has been tried and what is needed for practical and safe
autonomous vehicles. We also offer perspectives on open issues that are needed
to be solved in the future.

Computer Vision and Pattern Recognition;Signal Processing