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Description

**Our Mission** - Develop a C++ framework to provide easy access to 3D reconstruction from images.

**Our Vision** - We enhance reproducible research in 3D reconstruction from images by providing easy-to-read and accurate implementation of state of the art "classic algorithms" that the community considers as references. Using them in libraries, samples, and pipelines help to spread the usage of computer vision techniques to the community.

**Our Credo** - "Keep it simple, keep it maintainable". [OpenMVG](https://github.com/openMVG/openMVG) is designed to be easy to learn, modify and use. Thanks to its strict test driven development and samples, the library can be used as trusted bricks to build larger systems.

Programming language: C++
Latest version: v1.5

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README

OpenMVG (open Multiple View Geometry)

Logo

Introduction

OpenMVG (Multiple View Geometry) "open Multiple View Geometry" is a library for computer-vision scientists and especially targeted to the Multiple View Geometry community. It is designed to provide easy access to the classical problem solvers in Multiple View Geometry and solve them accurately.

The openMVG credo is: "Keep it simple, keep it maintainable". OpenMVG targets readable code that is easy to use and modify by the community.

All the features and modules are unit tested. This test-driven development ensures that the code works as it should and enables more consistent repeatability. Furthermore, it makes it easier for the user to understand and learn the given features.

GitHub license

Build

Please follow this build tutorial to build and use OpenMVG locally or in a Docker.

Continuous integration

  • Build : Linux/Mac (GCC/CLANG): Build Status, Windows (VStudio): Build status
  • Code Quality Codacy Badge Language grade: C/C++ CodeFactor <!-- - Unit test coverage: Coverage Status -->

Authors

See Authors text file

Documentation

See documentation

Contact

openmvg-team[AT]googlegroups.com

Citations

We are recommending citing OpenMVG if you are using the whole library or the adequate paper if you use only a submodule AContrario Ransac, AContrario SfM, GlobalSfM or Tracks:

@inproceedings{moulon2016openmvg,
  title={Openmvg: Open multiple view geometry},
  author={Moulon, Pierre and Monasse, Pascal and Perrot, Romuald and Marlet, Renaud},
  booktitle={International Workshop on Reproducible Research in Pattern Recognition},
  pages={60--74},
  year={2016},
  organization={Springer}
}

[3] Moulon Pierre, Monasse Pascal and Marlet Renaud. ACCV 2012. Adaptive Structure from Motion with a contrario model estimation.

@inproceedings{Moulon2012,
  doi = {10.1007/978-3-642-37447-0_20},
  year  = {2012},
  publisher = {Springer Berlin Heidelberg},
  pages = {257--270},
  author = {Pierre Moulon and Pascal Monasse and Renaud Marlet},
  title = {Adaptive Structure from Motion with a~Contrario Model Estimation},
  booktitle = {Proceedings of the Asian Computer Vision Conference (ACCV 2012)}
}

[4] Moulon Pierre and Monasse Pascal. CVMP 2012. Unordered feature tracking made fast and easy.

@inproceedings{moulon2012unordered,
  title={Unordered feature tracking made fast and easy},
  author={Moulon, Pierre and Monasse, Pascal},
  booktitle={CVMP 2012},
  pages={1},
  year={2012}
}

[5] Moisan Lionel, Moulon Pierre and Monasse Pascal. IPOL 2012. Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers.

@article{moisan2012automatic,
  title={Automatic homographic registration of a pair of images, with a contrario elimination of outliers},
  author={Moisan, Lionel and Moulon, Pierre and Monasse, Pascal},
  journal={Image Processing On Line},
  volume={2},
  pages={56--73},
  year={2012}
}

[6] Moulon Pierre, Monasse Pascal, and Marlet Renaud. ICCV 2013. Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion.

@inproceedings{moulon2013global,
  title={Global fusion of relative motions for robust, accurate and scalable structure from motion},
  author={Moulon, Pierre and Monasse, Pascal and Marlet, Renaud},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={3248--3255},
  year={2013}
}

Acknowledgements

openMVG authors would like to thanks libmv authors for providing an inspiring base to design openMVG. Authors also would like to thanks Mikros Image [1] and LIGM-Imagine laboratory [2] for support and authorization to make this library an opensource project.

[1] http://www.mikrosimage.eu/ [2] http://imagine.enpc.fr/


*Note that all licence references and agreements mentioned in the OpenMVG (open Multiple View Geometry) README section above are relevant to that project's source code only.