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RANSAC

A robust model-fitting procedure that repeatedly samples minimal point sets and keeps whichever fit gets the most support.

RANSAC (RANdom SAmple Consensus) fits a model in the presence of outliers by repeatedly drawing a minimal random sample of points, fitting a candidate model to that sample, counting how many of the remaining points agree with it within a tolerance (its inlier count), and keeping the candidate with the largest inlier consensus across many iterations. See the RANSAC consensus guarantee for how many iterations that takes to trust with a given confidence.

Examples

  • Fitting a homography from 200 noisy matches, RANSAC repeatedly samples 4 points, fits a candidate homography, and counts inliers, keeping whichever sampled fit wins the most support across many iterations.

See also

homography descriptor matching
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