Inverting RANSAC: Global Model Detection via Inlier Rate Estimation

Roee Litman, Simon Korman, Alexander Bronstein, Shai Avidan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5243-5251

Abstract


This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optimal transformation. We provide a theoretical analysis of the IRE method using a stochastic generative model on the continuous spaces of matches and transformations. This model allows rigorous investigation of the limits of our IRE method for the case of 2D-translation, further giving bounds and insights for the more general case. Our theoretical analysis is validated empirically and is shown to hold in practice for the more general case of 2D-affinities. In addition, we show that the combined framework works on challenging cases of 2D-homography estimation, with very few and possibly noisy inliers, where RANSAC generally fails.

Related Material


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[bibtex]
@InProceedings{Litman_2015_CVPR,
author = {Litman, Roee and Korman, Simon and Bronstein, Alexander and Avidan, Shai},
title = {Inverting RANSAC: Global Model Detection via Inlier Rate Estimation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}