Automatic RANSAC by Likelihood Maximization
Clément Riu, Vincent Nozick, Pascal Monasse
Clément Riu, Vincent Nozick, and Pascal Monasse, Automatic RANSAC by Likelihood Maximization, Image Processing On Line, 12 (2022), pp. 27–49.

Communicated by Mariano Rodríguez
Demo edited by Pascal Monasse


In computer vision, and particularly in 3D reconstruction from images, it is customary to be faced with regression problems contaminated by outlying data. The standard and efficient method to deal with them is the Random Sample Consensus (RANSAC) algorithm. The procedure is simple and versatile, drawing random minimal samples from the data to estimate parameterized candidate models and ranking them based on the amount of compatible data. Such evaluation involves some threshold that separates inliers from outliers. In presence of unknown level of noise, as is usual in practice, it is desirable to remove the dependency on this fixed threshold and to estimate it as an additional unknown. Among the numerous variants of RANSAC, few, that we call 'automatic', propose this approach, which involves changing the maximization criterion of consensus, as it is naturally increasing with the varying threshold. An algorithm of Zach and Cohen (ICCV 2015) uses the likelihood statistics. We present the details and the implementation of their method along with quantitative and qualitative tests on standard stereovision tasks: estimation of homography, fundamental and essential matrix.