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

doi:10.1016/j.neucom.2020.05.086

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection
	Tian, Yonglin1,2 ; Wang, Kunfeng3 ; Wang, Yuang 4; Tian, Yulin 5; Wang, Zilei 1; Wang, Fei-Yue2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-10-21
卷号	411 页码:32-44
通讯作者	Wang, Kunfeng(wangkf@mail.buct.edu.cn)
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.
关键词	3D object detection Point cloud Multimodal fusion Ground plane fitting
DOI	10.1016/j.neucom.2020.05.086
收录类别	SCI
语种	英语
资助项目	Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Fundamental Research Funds for the Central Universities
项目资助者	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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000571724600004
出版者	ELSEVIER
七大方向——子方向分类	人工智能+医疗
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42000
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
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
第一作者单位	中国科学院自动化研究所
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.