Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey

November 10, 2023 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey"

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Authors Yang Peng arXiv ID 2311.06043 Category cs.CV: Computer Vision Cross-listed cs.AI Citations 7 Venue arXiv.org Last Checked 3 days ago
Abstract
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.
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