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Conference Paper: Robust photometric stereo via low-rank matrix completion and recovery

TitleRobust photometric stereo via low-rank matrix completion and recovery
Authors
Issue Date2011
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, v. 6494 LNCS, n. PART 3, p. 703-717 How to Cite?
AbstractWe present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recovering a low-rank matrix with both missing entries and corrupted entries, which model all types of non-Lambertian effects such as shadows and specularities. Unlike previous approaches that use Least-Squares or heuristic robust techniques, our method uses advanced convex optimization techniques that are guaranteed to find the correct low-rank matrix by simultaneously fixing its missing and erroneous entries. Extensive experimental results demonstrate that our method achieves unprecedentedly accurate estimates of surface normals in the presence of significant amount of shadows and specularities. The new technique can be used to improve virtually any photometric stereo method including uncalibrated photometric stereo. © 2011 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/326856
ISSN
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorWu, Lun-
dc.contributor.authorGanesh, Arvind-
dc.contributor.authorShi, Boxin-
dc.contributor.authorMatsushita, Yasuyuki-
dc.contributor.authorWang, Yongtian-
dc.contributor.authorMa, Yi-
dc.date.accessioned2023-03-31T05:27:01Z-
dc.date.available2023-03-31T05:27:01Z-
dc.date.issued2011-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, v. 6494 LNCS, n. PART 3, p. 703-717-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/326856-
dc.description.abstractWe present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recovering a low-rank matrix with both missing entries and corrupted entries, which model all types of non-Lambertian effects such as shadows and specularities. Unlike previous approaches that use Least-Squares or heuristic robust techniques, our method uses advanced convex optimization techniques that are guaranteed to find the correct low-rank matrix by simultaneously fixing its missing and erroneous entries. Extensive experimental results demonstrate that our method achieves unprecedentedly accurate estimates of surface normals in the presence of significant amount of shadows and specularities. The new technique can be used to improve virtually any photometric stereo method including uncalibrated photometric stereo. © 2011 Springer-Verlag Berlin Heidelberg.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.titleRobust photometric stereo via low-rank matrix completion and recovery-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-19318-7_55-
dc.identifier.scopuseid_2-s2.0-79952528394-
dc.identifier.volume6494 LNCS-
dc.identifier.issuePART 3-
dc.identifier.spage703-
dc.identifier.epage717-
dc.identifier.eissn1611-3349-

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