arXiv:2311.06043
Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey
원문 초록
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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Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic
A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds
DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos
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