CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
IRA plus plus : Distributed Incremental Rotation Averaging
Gao, Xiang1; Zhu, Lingjie2; Cui, Hainan3,4,5; Xie, Zexiao1; Shen, Shuhan3,4,5
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2022-07-01
Volume32Issue:7Pages:4885-4892
Corresponding AuthorShen, Shuhan(shshen@nlpr.ia.ac.cn)
AbstractBy observing that the recently presented Incremental Rotation Averaging (IRA) suffers from drifting and efficiency problems in large-scale situations, it is upgraded in this work to possess stronger scalability in both accuracy and efficiency based on the thought of divide and conquer. This upgraded version is termed as IRA++. Specifically, the original Epipolar-geometry Graph (EG) is clustered into several sub-graphs and inner-rotation averaging is distributedly performed in each of them with IRA at first. Then, the relative rotation between each pair of inner-sub-EG coordinate systems is distributedly estimated by a voting-based single rotation averaging method. Subsequently, IRA-based inter-rotation averaging is performed to obtain the absolute rotation of each inner-sub-EG coordinate system. And finally, the absolute rotations of all the cameras in the original EG are globally aligned and optimized to get the final rotation averaging result. Comprehensive evaluations on the 1DSfM, Campus, and San Francisco datasets demonstrate the advantages of our proposed IRA++ over IRA and several other state-of-the-art rotation averaging methods in both efficiency and accuracy, especially the accuracy in noise-polluted and efficiency in large-scale situations.
KeywordBarium Optimization Cameras Rotation measurement Pipelines Estimation Scalability Structure from motion rotation averaging divide and conquer
DOI10.1109/TCSVT.2021.3118883
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[62003319] ; National Science Foundation of China[61873265] ; National Science Foundation of China[62076026] ; Shandong Provincial Natural Science Foundation[ZR2020QF075] ; China Postdoctoral Science Foundation[2020M682239]
Funding OrganizationNational Science Foundation of China ; Shandong Provincial Natural Science Foundation ; China Postdoctoral Science Foundation
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000819817700063
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49153
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorShen, Shuhan
Affiliation1.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
2.Alibaba AI Labs, Hangzhou 311121, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.CASIA SenseTime Res Grp, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gao, Xiang,Zhu, Lingjie,Cui, Hainan,et al. IRA plus plus : Distributed Incremental Rotation Averaging[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(7):4885-4892.
APA Gao, Xiang,Zhu, Lingjie,Cui, Hainan,Xie, Zexiao,&Shen, Shuhan.(2022).IRA plus plus : Distributed Incremental Rotation Averaging.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(7),4885-4892.
MLA Gao, Xiang,et al."IRA plus plus : Distributed Incremental Rotation Averaging".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.7(2022):4885-4892.
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