  Point cloud learning has lately attracted increasing attention due to its
wide applications in many areas, such as computer vision, autonomous driving,
and robotics. As a dominating technique in AI, deep learning has been
successfully used to solve various 2D vision problems. However, deep learning
on point clouds is still in its infancy due to the unique challenges faced by
the processing of point clouds with deep neural networks. Recently, deep
learning on point clouds has become even thriving, with numerous methods being
proposed to address different problems in this area. To stimulate future
research, this paper presents a comprehensive review of recent progress in deep
learning methods for point clouds. It covers three major tasks, including 3D
shape classification, 3D object detection and tracking, and 3D point cloud
segmentation. It also presents comparative results on several publicly
available datasets, together with insightful observations and inspiring future
research directions.
