Sequential Point Clouds: A Survey
It addresses the need for better understanding and methods in sequential point cloud processing, which is crucial for applications like autonomous driving and robotics, but is incremental as it is a review paper.
This paper surveys deep learning methods for sequential point clouds, summarizing and comparing quantitative results across public benchmarks for tasks like dynamic flow estimation, object detection, and segmentation.
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.