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Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L-1 Loss | |
Chen, Yaran1,2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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ISSN | 2162-237X |
2022-02-01 | |
卷号 | 33期号:2页码:762-773 |
摘要 | The 3-D object detection is crucial for many real-world applications, attracting many researchers' attention. Beyond 2-D object detection, 3-D object detection usually needs to extract appearance, depth, position, and orientation information from light detection and ranging (LiDAR) and camera sensors. However, due to more degrees of freedom and vertices, existing detection methods that directly transform from 2-D to 3-D still face several challenges, such as exploding increase of anchors' number and inefficient or hard-to-optimize objective. To this end, we present a fast segmentation method for 3-D point clouds to reduce anchors, which can largely decrease the computing cost. Moreover, taking advantage of 3-D generalized Intersection of Union (GIoU) and L-1 losses, we propose a fused loss to facilitate the optimization of 3-D object detection. A series of experiments show that the proposed method has alleviated the abovementioned issues effectively. |
关键词 | 3-D object detection generalized Intersection of Union (GIoU) loss segmentation |
DOI | 10.1109/TNNLS.2020.3028964 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China (NSFC)[62006226] ; Natural Science Foundation of Beijing[L191002] |
项目资助者 | National Natural Science Foundation of China (NSFC) ; Natural Science Foundation of Beijing |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000752016400027 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 强化与进化学习 |
国重实验室规划方向分类 | 多尺度信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47350 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Zhao, Dongbin |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chen, Yaran,Li, Haoran,Gao, Ruiyuan,et al. Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L-1 Loss[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(2):762-773. |
APA | Chen, Yaran,Li, Haoran,Gao, Ruiyuan,&Zhao, Dongbin.(2022).Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L-1 Loss.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(2),762-773. |
MLA | Chen, Yaran,et al."Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L-1 Loss".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.2(2022):762-773. |
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