Wang Haiyan, Tian Yingli
IEEE Trans Pattern Anal Mach Intell. 2024 Aug;46(8):5504-5523. doi: 10.1109/TPAMI.2024.3365970. Epub 2024 Jul 2.
Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing 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. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.
点云已获得越来越多的研究关注，并发现了众多实际应用。然而，这些应用中的许多，如自动驾驶和机器人操纵，依赖于序列点云，本质上是给数据增加了一个时间维度（即四维），因为静态点云数据所能提供的信息仍然有限。最近的研究工作一直致力于加强对序列点云的理解和利用。本文全面综述了应用于序列点云研究的深度学习方法，包括动态流估计、目标检测与跟踪、点云分割和点云预测。本文还总结并比较了在公共基准数据集上所综述方法的定量结果。最终，本文通过阐述当前序列点云研究中的挑战并指出未来研究的有前景方向来得出结论。
