@inproceedings{2e433d1a41084baa9b71976908b21a8d,
title = "Few-Shot Online Learning for 3D Object Detection in Autonomous Driving",
abstract = "For autonomous driving, the performance of 3D object detection is limited by offline training, and these methods usually lack the adaption ability for long-term autonomy, which leads to significant performance degeneration across different scenarios, i.e. domain shift. This paper proposes a few-shot online learning method to transfer knowledge from 2D images to 3D point clouds. In particular, the point cloud clusters are automatically labeled by the 3D-2D projection and 3D object tracking, and the learning strategy allows the classifier to learn multiple classes with limited samples in a short period of time. The final 3D detection results are obtained from the fusion of the online learning 3D detector and an end-to-end 3D detector. Experimental results on the KITTI dataset demonstrate the effectiveness of our system compared to the baseline methods.",
keywords = "3D object detection, Autonomous Driving, Few-shot learning, Online learning",
author = "Dexin Yao and Binhong Liu and Rui Yang and Zhi Yan and Wenxing Fu and Tao Yang",
note = "Publisher Copyright: {\textcopyright} Beijing HIWING Scientific and Technological Information Institute 2024.; 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 ; Conference date: 09-09-2023 Through 11-09-2023",
year = "2024",
doi = "10.1007/978-981-97-1087-4_27",
language = "英语",
isbn = "9789819710867",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "282--291",
editor = "Yi Qu and Mancang Gu and Yifeng Niu and Wenxing Fu",
booktitle = "Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) - Volume III",
}