  Point cloud has drawn more and more research attention as well as real-world
applications. However, many of these applications (e.g. autonomous driving and
robotic manipulation) are actually based on sequential point clouds (i.e. four
dimensions) because the information of the static point cloud data could
provide is still limited. Recently, researchers put more and more effort into
sequential point clouds. This paper presents an extensive review of the deep
learning-based methods for sequential point cloud research including dynamic
flow estimation, object detection \& tracking, point cloud segmentation, and
point cloud forecasting. This paper further summarizes and compares the
quantitative results of the reviewed methods over the public benchmark
datasets. Finally, this paper is concluded by discussing the challenges in the
current sequential point cloud research and pointing out insightful potential
future research directions.
