The MAGSAC algorithm proposed in paper: Daniel Barath, Jana Noskova and Jiri Matas; MAGSAC: Marginalizing sample consensus, Conference on Computer Vision and Pattern Recognition, 2019. It is available at https://arxiv.org/pdf/1803.07469.pdf
Made in OpenCV 3.46.
To run the executable with the examples, copy the "data" folder next to the executable or set the path in the main() function.
The MAGSAC algorithm proposed in paper: Daniel Barath, Jana Noskova and Jiri Matas; MAGSAC: marginalizing sample consensus, Conference on Computer Vision and Pattern Recognition, 2019. It is available at http://openaccess.thecvf.com/content_CVPR_2019/papers/Barath_MAGSAC_Marginalizing_Sample_Consensus_CVPR_2019_paper.pdf
When using the algorithm, please cite Barath, Daniel, and Noskova, Jana and Matas, Jiří. "MAGSAC: marginalizing sample consensus" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
To build and install MAGSAC, clone or download this repository and, also, the sub-modules. 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