Inverting RANSAC: Global Model Detection via Inlier Rate Estimation
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.
Cite
Text
Litman et al. "Inverting RANSAC: Global Model Detection via Inlier Rate Estimation." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7299161Markdown
[Litman et al. "Inverting RANSAC: Global Model Detection via Inlier Rate Estimation." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/litman2015cvpr-inverting/) doi:10.1109/CVPR.2015.7299161BibTeX
@inproceedings{litman2015cvpr-inverting,
title = {{Inverting RANSAC: Global Model Detection via Inlier Rate Estimation}},
author = {Litman, Roee and Korman, Simon and Bronstein, Alexander and Avidan, Shai},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2015},
doi = {10.1109/CVPR.2015.7299161},
url = {https://mlanthology.org/cvpr/2015/litman2015cvpr-inverting/}
}