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SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection
Ye, Yangyang1; Chen, Houjin1; Zhang, Chi2; Hao, Xiaoli1; Zhang, Zhaoxiang2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2020-02-28
Volume379Pages:53-63
Abstract

Real-time 3D object detection is a fundamental technique in numerous applications, such as autonomous driving, unmanned aerial vehicles (UAV) and robot vision. However, current LiDAR-based 3D object detection algorithms allocate inadequate attention to the inhomogeneity of LiDAR point clouds and the shape encoding capability of regional proposal schemes. This paper introduces a novel 3D object detection network called the Shape Attention Regional Proposal Network (SARPNET), which deploys a new low-level feature encoder to remedy the sparsity and inhomogeneity of LiDAR point clouds with an even sample method, and embodies a shape attention mechanism that learns the statistic 3D shape priors of objects and uses them to spatially enhance semantic embeddings. Experimental results show that the proposed one-stage method outperforms state-of-the-art one-stage and even two-stage methods on the KITTI 3D object detection benchmark. It achieved a BEV AP of (87.26%, 62.80%), 3D AP of (75.64%, 60.43%), and orientation AP of (88.86%, 71.01%) for the detection of cars and cyclists, respectively. Besides, the method is the third winner in the nuScenes 3D Detection challenge of CVPR2019 Workshop on Autonomous Driving (WAD). (C) 2019 Elsevier B.V. All rights reserved.

KeywordShape attention 3D shape priors Feature encoder 3D object detection LiDAR point cloud
DOI10.1016/j.neucom.2019.09.086
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61771042] ; Fundamental Research Funds of BJTU[2017JBZ002] ; National Natural Science Foundation of China[61761146004] ; National Natural Science Foundation of China[61836014] ; National Key R&D Program of China[2018YFB1004600] ; Microsoft Collaborative Research Project ; National Natural Science Foundation of China[61602481] ; National Natural Science Foundation of China[61773375] ; Beijing Municipal Natural Science Foundation[Z181100008918010] ; Beijing Municipal Natural Science Foundation[Z181100008918010] ; National Natural Science Foundation of China[61773375] ; National Natural Science Foundation of China[61602481] ; Microsoft Collaborative Research Project ; National Key R&D Program of China[2018YFB1004600] ; National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[61761146004] ; Fundamental Research Funds of BJTU[2017JBZ002] ; National Natural Science Foundation of China[61771042]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000507464700005
PublisherELSEVIER
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory环境多维感知
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Cited Times:52[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29502
Collection模式识别实验室
Corresponding AuthorChen, Houjin
Affiliation1.Beijing Jiaotong Univ, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Ye, Yangyang,Chen, Houjin,Zhang, Chi,et al. SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection[J]. NEUROCOMPUTING,2020,379:53-63.
APA Ye, Yangyang,Chen, Houjin,Zhang, Chi,Hao, Xiaoli,&Zhang, Zhaoxiang.(2020).SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection.NEUROCOMPUTING,379,53-63.
MLA Ye, Yangyang,et al."SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection".NEUROCOMPUTING 379(2020):53-63.
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