Object detection and tracking are vital and fundamental tasks for autonomous driving, aiming at identifying and locating objects from those predefined categories in a scene. 3D point cloud learning has been attracting more and more attention among all other forms of self-driving data. Currently, there are many deep learning methods for 3D object detection. However, the tasks of object detection and tracking for point clouds still need intensive study due to the unique characteristics of point cloud data. To help get a good grasp of the present situation of this research, this paper shows recent advances in deep learning methods for 3D object detection and tracking.

翻译：目标检测与跟踪是自动驾驶中至关重要且基础的任务，旨在从场景中识别并定位预定义类别的物体。在所有形式的自动驾驶数据中，三维点云学习正日益受到关注。目前，已有许多用于三维目标检测的深度学习方法。然而，由于点云数据的独特特性，点云的目标检测与跟踪任务仍需深入研究。为帮助全面了解该研究现状，本文展示了近年来深度学习在三维目标检测与跟踪方法上的最新进展。