The Graph-Cut RANSAC algorithm proposed in paper: Daniel Barath and Jiri Matas; Graph-Cut RANSAC, Conference on Computer Vision and Pattern Recognition, 2018. It is available at http://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Graph-Cut_RANSAC_CVPR_2018_paper.pdf

When using the algorithm, please cite Barath, Daniel, and Matas, Jiří. "Graph-cut RANSAC." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.

In case you use GC-RANSAC with Progressive NAPSAC sampler (https://arxiv.org/abs/1906.02295), please cite Barath, Daniel, Maksym Ivashechkin, and Jiri Matas. "Progressive NAPSAC: sampling from gradually growing neighborhoods." arXiv preprint arXiv:1906.02295 (2019).

To build and install GraphCutRANSAC, clone or download this repository and then build the project by CMAKE.

To build the sample project showing examples of fundamental matrix, homography and essential matrix fitting, set variable CREATE_SAMPLE_PROJECT = ON when creating the project in CMAKE.

Next to the executable, copy the data folder and, also, create a results folder.

• Eigen 3.0 or higher
• CMake 2.8.12 or higher
• OpenCV 3.0 or higher
• A modern compiler with C++17 support

You can find the python code of GC-RANSAC in the other branch thanks to Dmytro Mischkin.