  Object detection and tracking are vital and fundamental tasks for autonomous
driving, aiming at identifying and locating objects from those predefined
categories in a scene. 3D point cloud learning has been attracting more and
more attention among all other forms of self-driving data. Currently, there are
many deep learning methods for 3D object detection. However, the tasks of
object detection and tracking for point clouds still need intensive study due
to the unique characteristics of point cloud data. To help get a good grasp of
the present situation of this research, this paper shows recent advances in
deep learning methods for 3D object detection and tracking.
