Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection

doi:10.1016/j.neucom.2020.05.086

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection
	Tian, Yonglin 1,2; Wang, Kunfeng 3; Wang, Yuang 4; Tian, Yulin 5; Wang, Zilei 1; Wang, Fei-Yue 2
刊名	NEUROCOMPUTING
	2020-10-21
卷号	411 页码:32-44
关键词	3D object detection Point cloud Multimodal fusion Ground plane fitting
ISSN号	0925-2312
DOI	10.1016/j.neucom.2020.05.086
通讯作者	Wang, Kunfeng(wangkf@mail.buct.edu.cn)
英文摘要	This paper focuses on the construction of strong local features and the effective fusion of image and LiDAR data for 3D object detection. We adopt different modalities of LiDAR data to generate rich features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3D anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LiDAR data by introducing an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network. (C) 2020 Published by Elsevier B.V.
资助项目	Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Fundamental Research Funds for the Central Universities
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000571724600004
资助机构	Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/41999]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Wang, Kunfeng
作者单位	1.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 4.Univ Sci & Technol Beijing, Beijing 100083, Peoples R China 5.North China Univ Technol, Beijing 100144, Peoples R China
推荐引用方式 GB/T 7714	Tian, Yonglin,Wang, Kunfeng,Wang, Yuang,et al. Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection[J]. NEUROCOMPUTING,2020,411:32-44.
APA	Tian, Yonglin,Wang, Kunfeng,Wang, Yuang,Tian, Yulin,Wang, Zilei,&Wang, Fei-Yue.(2020).Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection.NEUROCOMPUTING,411,32-44.
MLA	Tian, Yonglin,et al."Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection".NEUROCOMPUTING 411(2020):32-44.