Title: Graph-Cut RANSAC,
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).