交互场景下的序列点云预测研究综述
Sequential Point Cloud Prediction in Interactive Scenarios: A Survey
摘要：
点云在自动驾驶领域得到了广泛的应用，因为它可以提供比2D图像更全面的环境三维表示。基于点云序列的逐点预测是环境理解的重要组成部分，可以辅助自主车辆的决策和运动规划。然而，PCS预测还没有在文献中进行深入研究。本文提出了一个简短的回顾顺序点云预测方法，重点是交互式场景。首先，我们定义了PCS预测问题，并介绍了常用的框架。其次，通过对非预测性问题的回顾，分析和总结了基于PCS的时空特征提取方法。在此基础上，我们回顾了两种类型的PCS预测任务，场景流估计（SFE）和点云位置预测（PCLP），突出他们的联系和区别。最后，我们讨论了一些开放的问题，并指出了一些潜在的研究方向。
英文摘要：
Point cloud has been widely used in the field of autonomous driving since it can provide a more comprehensive three-dimensional representation of the environment than 2D images. Point-wise prediction based on point cloud sequence (PCS) is an essential part of environment understanding, which can assist in the decision-making and motion-planning of autonomous vehicles. However, PCS prediction has not been deeply researched in the literature. This paper proposes a brief review of the sequential point cloud prediction methods, focusing on interactive scenarios. Firstly, we define the PCS prediction problem and introduce commonly-used frameworks. Secondly, by reviewing non-predictive problems, we analyze and summarize the spatio-temporal feature extraction methods based on PCS. On this basis, we review two types of PCS prediction tasks, scene flow estimation (SFE) and point cloud location prediction (PCLP), highlighting their connections and differences. Finally, we discuss some opening issues and point out some potential research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
