CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
View-graph construction framework for robust and efficient structure-from-motion
Cui, Hainan1; Shi, Tianxin1; Zhang, Jun2; Xu, Pengfei2; Meng, Yiping2; Shen, Shuhan1,3
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2021-06-01
Volume114Pages:9
Corresponding AuthorCui, Hainan(hncui@nlpr.ia.ac.cn)
AbstractA view-graph is vital for both the accuracy and robustness of structure-from-motion (SfM). Conventional matrix decomposition techniques treat all edges of view-graph equally; hence, many edge outliers are produced in matching pairs with fewer feature matches. To address this problem, we propose an incremental framework for view-graph construction, where the robustness of matched pairs that have a larger number of feature matches is propagated to their connected images. Given pairwise feature matches, a verified maximum spanning tree (VMST) is first constructed; for each edge in the VMST, we perform a local reconstruction and register its visible cameras. Based on the local reconstruction, pairwise relative geometries are computed and some new epipolar edges are produced. In this way, these newly computed edges inherit the robustness and accuracy of VMST, and by embedding them into VMST, our view-graph is constructed. We feed our view-graph into a standard SfM pipeline and compare this newly formed system with many of state-of-the-art SfM methods. The experimental results demonstrate that our view graph provides a better foundation for conventional SfM systems, and enables them to reconstruct both general and ambiguous images. ? 2020 Elsevier Ltd. All rights reserved.
KeywordStructure-from-motion View-graph construction Epipolar geometry computation
DOI10.1016/j.patcog.2020.107712
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61703397] ; National Natural Science Foundation of China[U1805264] ; Didi GAIA Foundation
Funding OrganizationNational Natural Science Foundation of China ; Didi GAIA Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000625557800001
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/43348
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorCui, Hainan
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
2.Didi Chuxing, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Cui, Hainan,Shi, Tianxin,Zhang, Jun,et al. View-graph construction framework for robust and efficient structure-from-motion[J]. PATTERN RECOGNITION,2021,114:9.
APA Cui, Hainan,Shi, Tianxin,Zhang, Jun,Xu, Pengfei,Meng, Yiping,&Shen, Shuhan.(2021).View-graph construction framework for robust and efficient structure-from-motion.PATTERN RECOGNITION,114,9.
MLA Cui, Hainan,et al."View-graph construction framework for robust and efficient structure-from-motion".PATTERN RECOGNITION 114(2021):9.
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