Graph-Cut RANSAC

Daniel Barath, Jiří Matas; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6733-6741

Abstract


A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation. It runs in real-time for many problems at a speed approximately equal to that of the less accurate alternatives (in milliseconds on standard CPU).

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Barath_2018_CVPR,
author = {Barath, Daniel and Matas, Jiří},
title = {Graph-Cut RANSAC},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}