Abstract
A novel method for robust estimation, called Graph-Cut
RANSAC1
, 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 sofar-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)