  Point cloud analysis has a wide range of applications in many areas such as
computer vision, robotic manipulation, and autonomous driving. While deep
learning has achieved remarkable success on image-based tasks, there are many
unique challenges faced by deep neural networks in processing massive,
unordered, irregular and noisy 3D points. To stimulate future research, this
paper analyzes recent progress in deep learning methods employed for point
cloud processing and presents challenges and potential directions to advance
this field. It serves as a comprehensive review on two major tasks in 3D point
cloud processing-- namely, 3D shape classification and semantic segmentation.
