COLMAP

Sparse reconstruction of central Rome.

Reconstruction of central Rome using 21K photos produced by COLMAP’s SfM pipeline.

Dense reconstruction of landmarks.

Dense reconstruction of several landmarks produced by COLMAP’s MVS pipeline.

About

COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections. The software is licensed under the GNU General Public License. If you use this project for your research, please cite:

@inproceedings{schoenberger2016sfm,
    author = {Sch\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},
    title = {Structure-from-Motion Revisited},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2016},
}

@inproceedings{schoenberger2016mvs,
    author = {Sch\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael},
    title = {Pixelwise View Selection for Unstructured Multi-View Stereo},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2016},
}

The latest source code is available at GitHub. COLMAP builds on top of existing works and when using specific algorithms within COLMAP, please also cite the original authors, as specified in the source code.

Download

Executables and other resources can be downloaded from http://people.inf.ethz.ch/jschoenb/colmap/.

Getting Started

  1. Download the pre-built binaries or build the library manually (see Installation).
  2. Download one of the provided datasets (see Datasets) or use your own images.
  3. Watch the short introductory video at YouTube or read the Tutorial for more details.

Support

Please, use the Google Group (colmap@googlegroups.com) for questions and the GitHub issue tracker for bug reports, feature requests/additions, etc.

Acknowledgments

The library was written by Johannes L. Schönberger. Funding was provided by his PhD advisor Jan-Michael Frahm through the grants NSF No. IIS-1349074, No. CNS-1405847, and the MITRE Corp.