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ICCV
2011
IEEE

RECON: Scale-Adaptive Robust Estimation via Residual Consensus

13 years 13 days ago
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 an...
Rahul Raguram, Jan-Michael Frahm
Added 11 Dec 2011
Updated 11 Dec 2011
Type Journal
Year 2011
Where ICCV
Authors Rahul Raguram, Jan-Michael Frahm
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