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Article: Augmented lagrangian-based algorithm for projective reconstruction from multiple views with minimization of 2D reprojection error

TitleAugmented lagrangian-based algorithm for projective reconstruction from multiple views with minimization of 2D reprojection error
Authors
KeywordsAugmented Lagrangian
Bundle adjustment
Constrained optimization
Projective reconstruction
Issue Date2010
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/
Citation
Journal Of Signal Processing Systems, 2010, v. 61 n. 2, p. 181-192 How to Cite?
AbstractIn this paper, we propose a new factorization-based algorithm for projective reconstruction from multiple views by minimizing the 2D reprojection error in the images. In our algorithm, the projective reconstruction problem is formulated as a constrained minimization problem, which minimizes the 2D reprojection error in multiple images. To solve this constrained minimization problem, we use the augmented Lagrangian approach to generate a sequence of unconstrained minimization problems, which can be readily solved by standard least-squares technique. Thus we can estimate the projective depths, the projection matrices and the positions of 3D points simultaneously by iteratively solving a sequence of unconstrained minimization problems. The proposed algorithm does not require the projective depths as prior knowledge, unlike bundle adjustment techniques. It converges more robustly and rapidly than the penalty based method. Furthermore, it readily handles the case of partial occlusion, where some points cannot be observed in some images. © 2009 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/129211
ISSN
2015 Impact Factor: 0.508
2015 SCImago Journal Rankings: 0.262
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of Hong Kong Special Administrative Region, ChinaHKU 712808E
CRCG of the University of Hong Kong
Funding Information:

The work in this paper was supported by the Research Grants Council of Hong Kong Special Administrative Region, China (GRF project HKU 712808E) and CRCG of the University of Hong Kong.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorMai, Fen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-12-23T08:33:38Z-
dc.date.available2010-12-23T08:33:38Z-
dc.date.issued2010en_HK
dc.identifier.citationJournal Of Signal Processing Systems, 2010, v. 61 n. 2, p. 181-192en_HK
dc.identifier.issn1939-8018en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129211-
dc.description.abstractIn this paper, we propose a new factorization-based algorithm for projective reconstruction from multiple views by minimizing the 2D reprojection error in the images. In our algorithm, the projective reconstruction problem is formulated as a constrained minimization problem, which minimizes the 2D reprojection error in multiple images. To solve this constrained minimization problem, we use the augmented Lagrangian approach to generate a sequence of unconstrained minimization problems, which can be readily solved by standard least-squares technique. Thus we can estimate the projective depths, the projection matrices and the positions of 3D points simultaneously by iteratively solving a sequence of unconstrained minimization problems. The proposed algorithm does not require the projective depths as prior knowledge, unlike bundle adjustment techniques. It converges more robustly and rapidly than the penalty based method. Furthermore, it readily handles the case of partial occlusion, where some points cannot be observed in some images. © 2009 Springer Science+Business Media, LLC.en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/en_HK
dc.relation.ispartofJournal of Signal Processing Systemsen_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectAugmented Lagrangianen_HK
dc.subjectBundle adjustmenten_HK
dc.subjectConstrained optimizationen_HK
dc.subjectProjective reconstructionen_HK
dc.titleAugmented lagrangian-based algorithm for projective reconstruction from multiple views with minimization of 2D reprojection erroren_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1939-8018&volume=61&issue=2&spage=181&epage=192&date=2010&atitle=Augmented+Lagrangian-based+Algorithm+for+Projective+Reconstruction+from+Multiple+Views+with+Minimization+of+2D+Reprojection+Error-
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11265-009-0414-8en_HK
dc.identifier.scopuseid_2-s2.0-77955172216en_HK
dc.identifier.hkuros177933en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77955172216&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume61en_HK
dc.identifier.issue2en_HK
dc.identifier.spage181en_HK
dc.identifier.epage192en_HK
dc.identifier.isiWOS:000280240800005-
dc.publisher.placeUnited Statesen_HK
dc.relation.projectAn Integrated Approach to 3D Shape Recovery from Multiple Views-
dc.identifier.scopusauthoridMai, F=12804393400en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.citeulike5975709-

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