Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey
It provides a concise overview for researchers and practitioners in autonomous driving, but is incremental as it is a survey paper.
This paper surveys recent advances in deep learning methods for 3D object detection and tracking in autonomous driving, highlighting the challenges posed by point cloud data and the need for further study.
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.