Awesome

Progressive-X

The Progressive-X algorithm proposed in paper: Daniel Barath and Jiri Matas; Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm, International Conference on Computer Vision, 2019. It is available at https://arxiv.org/pdf/1906.02290

Installation C++

To build and install C++ only Progressive-X, clone or download this repository and then build the project by CMAKE.

$ git clone --recursive https://github.com/danini/progressive-x.git
$ cd build
$ cmake ..
$ make

Install Python package and compile C++

python3 ./setup.py install

or

pip3 install -e .

Example project

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. Then

$ cd build
$ ./SampleProject

Jupyter Notebook code for re-producing the results in the paper

The code for multiple homography fitting is available at: notebook.

The code for multiple two-view motion fitting is available at: notebook.

Jupyter Notebook example

The example for multiple homography fitting is available at: notebook.

The example for multiple two-view motion fitting is available at: notebook.

The example for multiple 6D pose fitting is available at: notebook.

The example for multiple vanishing point detection is available at: notebook.

Requirements

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

Acknowledgements

When using the algorithm, please cite Barath, Daniel, and Matas, Jiří. "Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm". Proceedings of the IEEE International Conference on Computer Vision. 2019.

If you use Progressive-X with Graph-Cut RANSAC as a proposal engine, please cite Barath, Daniel, and Matas, Jiří. "Graph-cut RANSAC." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.

If you use Progressive-X with Progressive NAPSAC sampler, please cite Barath, Daniel and Noskova, Jana and Ivashechkin, Maksym and Matas, Jiří. "MAGSAC++, a fast, reliable and accurate robust estimator" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2020.