Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography | VitaVision
PaperCommunications of the ACM · 1981

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Martin A. FischlerRobert C. Bolles
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Fischler–Bolles RANSAC

Founding random-sample-consensus paradigm: fit a parametric model to data containing an unknown fraction of gross outliers by drawing minimal random subsets, instantiating candidate models, counting consensus inliers, and retaining the largest consensus set.

Cited as a reference by 3 pages, grouped by domain

Geometry 3
EPnP: O(n) Perspective-n-PointLocally Optimized RANSAC (LO-RANSAC)RANSAC

Appears in 2 narratives

NarrativeForty Years Against OutliersNarrativeThe Geometry Pipeline Collapses

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’81’82’83’84’85’86’87’88’89’90’91’92’93’94’95’96’97’98’99’00’01’02’03’04’05’06’07’08’09’10’11’12’13’14’15’16’17’18’19’20Locally Optimized RANSAC (2003)EPnP: An Accurate O(n) Solution to the PnP Problem (2009)USAC: A Universal Framework for Random Sample Consensus (2013)Structure-from-Motion Revisited (2016)MAGSAC: marginalizing sample consensus (2019)SuperGlue: Learning Feature Matching with Graph Neural Networks (2020)
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