论文标题
顺序点云：调查
Sequential Point Clouds: A Survey
论文作者
论文摘要
Point Cloud吸引了越来越多的研究关注以及现实世界的应用。但是，这些应用程序中的许多（例如自主驾驶和机器人操纵）实际上是基于顺序点云（即四个维度），因为静态点云数据可以提供的信息仍然有限。最近，研究人员将越来越多的精力投入顺序点云。本文对基于深度学习的方法进行了广泛的评论，用于顺序云研究，包括动态流估计，对象检测\＆跟踪，点云分段和点云预测。本文进一步总结并比较了公共基准数据集对审查方法的定量结果。最后，通过讨论当前顺序云研究中的挑战并指出了有见地的潜在潜在研究方向来结束本文。
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.
