MAGSAC: marginalizing sample consensus | VitaVision
PaperCVPR · 2019

MAGSAC: marginalizing sample consensus

Dániel BaráthJiří MatasJana Nosková
arXiv 1803.07469DOIView in graph
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MAGSAC: Marginalising Sample Consensus

Robust estimator that eliminates the user-tuned inlier threshold by treating the noise scale σ as a random variable on [0, σ_max] and marginalising the RANSAC quality function over σ; the final model is a weighted least-squares fit using marginal-likelihood weights via iteratively reweighted least squares (σ-consensus).

Cited as a reference by 2 pages, grouped by domain

Geometry 2
Locally Optimized RANSAC (LO-RANSAC)RANSAC

Appears in 2 narratives

NarrativeForty Years Against OutliersNarrativeThe Geometry Pipeline Collapses

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’81’82’83’84’85’86’87’88’89’90’91’92’93’94’95’96’97’98’99’00’01’02’03’04’05’06’07’08’09’10’11’12’13’14’15’16’17’18’19Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography (1981)USAC: A Universal Framework for Random Sample Consensus (2013)
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