RECON: Scale-Adaptive Robust Estimation via Residual Consensus

In this paper, we present a novel, threshold-free robust estimation framework capable of efficiently fitting models to contaminated data. While RANSAC and its many variants have emerged as popular tools for robust estimation, their performance is largely dependent on the availability of a reasonable prior estimate of the inlier threshold. In this work, we aim to remove this threshold dependency. We build on the observation that models generated from uncontaminated minimal subsets are "consistent" in terms of the behavior of their residuals, while contaminated models exhibit uncorrelated behavior. By leveraging this observation, we then develop a very simple, yet effective algorithm that does not require apriori knowledge of either the scale of the noise, or the fraction of uncontaminated points. The resulting estimator, RECON (REsidual CONsensus), is capable of elegantly adapting to the contamination level of the data, and shows excellent performance even at low inlier ratios and high noise levels. We demonstrate the efficiency of our framework on a variety of challenging estimation problems.