# Graph-Cut RANSAC

## TL;DR

GC-RANSAC introduces a graph-cut based local optimization step that enhances robust model estimation, achieving higher accuracy and real-time performance across various geometric problems in computer vision.

## Contribution

It presents a simple, globally optimal, and efficient graph-cut based local optimization method integrated into RANSAC, improving accuracy over state-of-the-art techniques.

## Key findings

- More geometrically accurate than existing methods
- Operates in real-time on standard CPU
- Effective across multiple geometric estimation problems

## 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).