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dc.contributor.authorHruda, Lukášen_US
dc.contributor.authorDvořák, Janen_US
dc.contributor.authorVasa, Liboren_US
dc.contributor.editorBommes, David and Huang, Huien_US
dc.description.abstractRandom Sample Consensus is a powerful paradigm that was successfully applied in various contexts, including Location Determination Problem, fundamental matrix estimation and global 3D surface registration, where many previously proposed algorithms can be interpreted as a particular implementation of this concept. In general, a set of candidate transformations is generated by some simple procedure, and an aligning transformation is chosen within this set, such that it aligns the largest portion of the input data. We observe that choosing the aligning transformation may also be interpreted as finding consensus among the candidates, which in turn involves measuring similarity of candidate rigid transformations. While it is not difficult to construct a metric that provides reasonable results, most approaches come with certain limitations and drawbacks. In this paper, we investigate possible means of measuring distances in SE(3) and compare their properties both theoretically and experimentally in a model RANSAC registration algorithm. We also propose modifications to existing measures and propose a novel method of locating the consensus transformation based on Vantage Point Tree data structure.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectMesh geometry models
dc.subjectShape analysis
dc.titleOn Evaluating Consensus in RANSAC Surface Registrationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheaders2D and 3D Reconstruction

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  • 38-Issue 5
    Geometry Processing 2019 - Symposium Proceedings

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