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dc.contributor.authorBaráth D.
dc.contributor.authorNosková J.
dc.contributor.authorMatas J.
dc.date.accessioned2022-11-24T13:31:01Z
dc.date.available2022-11-24T13:31:01Z
dc.date.issued2022
dc.identifierV3S-360487
dc.identifier.citationBARÁTH, D., J. NOSKOVÁ, and J. MATAS. Marginalizing Sample Consensus. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022, 44(11), 8420-8432. ISSN 0162-8828. DOI 10.1109/TPAMI.2021.3103562.
dc.identifier.issn0162-8828 (print)
dc.identifier.issn1939-3539 (online)
dc.identifier.urihttp://hdl.handle.net/10467/105078
dc.description.abstractA new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not make inlier-outlier decisions, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. Instead of the inlier-outlier threshold, it requires only its loose upper bound which can be chosen from a significantly wider range. Also, we propose a new termination criterion and a technique for selecting a set of inliers in a data-driven manner as a post-processing step after the robust estimation finishes. On a number of publicly available real-world datasets for homography, fundamental matrix fitting and relative pose, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is more geometrically accurate, fails fewer times, and it is often faster. It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold. Therefore, it is easier to be applied to unseen problems and scenes without acquiring information by hand about the setting of the inlier-outlier threshold. The source code and examples both in C++ and Python are available at https://github.com/danini/magsac .eng
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE Computer Society Press
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence
dc.subjectRobust model estimationeng
dc.subjectRANSACeng
dc.subjectnoise scaleeng
dc.subjectM-estimatoreng
dc.subjectmarginalizationeng
dc.titleMarginalizing Sample Consensuseng
dc.typečlánek v časopisecze
dc.typejournal articleeng
dc.identifier.doi10.1109/TPAMI.2021.3103562
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/OPVVV/CZ.02.1.01%2F0.0%2F0.0%2F16_019%2F0000765/CZ/Research Center for Informatics/-
dc.relation.projectidinfo:eu-repo/grantAgreement/Czech Science Foundation/GA/GA18-05360S/CZ/Solving inverse problems for the analysis of fast moving objects/
dc.rights.accessrestrictedAccess
dc.identifier.wos000864325900080
dc.type.statusPeer-reviewed
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85139572054


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