One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving

doi:10.1109/ICRA48891.2023.10161552

	One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving
	Huitong Yang 1; Xuyang Bai 2; Xinge Zhu 3; Yuexin Ma1
	2023
会议录名称	PROCEEDINGS - IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
ISSN	1050-4729
卷号	2023-May
页码	5602-5609
发表状态	已发表
DOI	10.1109/ICRA48891.2023.10161552
摘要	Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips, costing huge computing resources. The situation becomes even worse for 3D perception methods with large models. Previous vision-centric 3D perception approaches are trained with regular grid-represented feature maps of fixed resolutions, which is not applicable to adapt to other grid scales, limiting wider deployment. In this paper, we leverage the Polar representation when constructing the BEV feature map from images in order to achieve the goal of training once for multiple deployments. Specifically, the feature along rays in Polar space can be easily adaptively sampled and projected to the feature in Cartesian space with arbitrary resolutions. To further improve the adaptation capability, we make multi-scale contextual information interact with each other to enhance the feature representation. Experiments on a large-scale autonomous driving dataset show that our method outperforms others as for the good property of one training for multiple deployments. © 2023 IEEE.
关键词	Computing power Large dataset 'current 3D perception Autonomous driving Computing power Computing resource Feature map Large models Learning-based algorithms Regular grids Training process
会议名称	2023 IEEE International Conference on Robotics and Automation, ICRA 2023
会议地点	London, United kingdom
会议日期	May 29, 2023 - June 2, 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20233514632065
EI主题词	Autonomous vehicles
EI分类号	432 Highway Transportation ; 722.2 Computer Peripheral Equipment ; 722.4 Digital Computers and Systems ; 723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 731.6 Robot Applications
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/325842
专题	信息科学与技术学院信息科学与技术学院_PI研究组_马月昕
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Department of Computer Science and Engineering, Hong Kong University of Science and Technology 3.Department of Information Engineering, The Chinese University of Hong Kong
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Huitong Yang,Xuyang Bai,Xinge Zhu,et al. One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving[C]:Institute of Electrical and Electronics Engineers Inc.,2023:5602-5609.