GLOMAP is a general purpose global structure-from-motion pipeline for image-based reconstruction. GLOMAP requires a COLMAP database as input and outputs a COLMAP sparse reconstruction. As compared to COLMAP, this project provides a much more efficient and scalable reconstruction process, typically 1-2 orders of magnitude faster, with on-par or superior reconstruction quality.

If you use this project for your research, please cite

@inproceedings{pan2024glomap,
    author={Pan, Linfei and Barath, Daniel and Pollefeys, Marc and Sch\"{o}nberger, Johannes Lutz},
    title={{Global Structure-from-Motion Revisited}},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2024},
}

To install GLOMAP, first install COLMAP dependencies and then build GLOMAP using the following commands:

mkdir build
cd build
cmake .. -GNinja
ninja && ninja install

After installation, one can run GLOMAP by (starting from a database)

glomap mapper --database_path DATABASE_PATH --output_path OUTPUT_PATH --image_path IMAGE_PATH

For more details on the command line interface, one can type glomap -h or glomap mapper -h for help. To obtain a colored reconstruction, it is recommended to call

colmap color_extractor --image_path IMAGE  --input_path MODEL_INPUT --output_path MODEL_OUTPUT

Note:

• GLOMAP depends on two external libraries - COLMAP and PoseLib. With the default setting, the library is built automatically by GLOMAP via FetchContent. However, if a self-installed version is preferred, one can also disable the FETCH_COLMAP and FETCH_POSELIB CMake options.
• To use FetchContent, the minimum required version of cmake is 3.28. If a self-installed version is used, cmake can be downgraded to 3.10.
• If your system does not provide a recent enough CMake version, you can install it as:

  wget https://github.com/Kitware/CMake/releases/download/v3.30.1/cmake-3.30.1.tar.gz
  tar xfvz cmake-3.30.1.tar.gz && cd cmake-3.30.1
  ./bootstrap && make -j$(nproc) && sudo make install

In this section, we will use datasets from this link as examples. Download the datasets and put them under data folder.

If a COLMAP database already exists, GLOMAP can directly use it to perform mapping:

glomap mapper \
    --database_path ./data/gerrard-hall/database.db \
    --image_path    ./data/gerrard-hall/images \
    --output_path   ./output/gerrard-hall/sparse

To obtain a reconstruction from images, the database needs to be established first. Here, we utilize the functions from COLMAP:

colmap feature_extractor \
    --image_path    ./data/south-building/images \
    --database_path ./data/south-building/database.db
colmap exhaustive_matcher \
    --database_path ./data/south-building/database.db 
glomap mapper \
    --database_path ./data/south-building/database.db \
    --image_path    ./data/south-building/images \
    --output_path   ./output/south-building/sparse

• For larger scale datasets, it is recommended to use sequential_matcher or vocab_tree_matcher from COLMAP.

colmap sequential_matcher --database_path DATABASE_PATH
colmap vocab_tree_matcher --database_path DATABASE_PATH --VocabTreeMatching.vocab_tree_path VOCAB_TREE_PATH

• Alternatively, one can use hloc for image retrival and matching with learning-based descriptors.

We are highly inspired by COLMAP, PoseLib, Theia. Please consider also citing them, if using GLOMAP in your work.

Please, use GitHub Discussions at https://github.com/colmap/glomap/discussions for questions and the GitHub issue tracker at https://github.com/colmap/glomap for bug reports, feature requests/additions, etc.

Contributions (bug reports, bug fixes, improvements, etc.) are very welcome and should be submitted in the form of new issues and/or pull requests on GitHub.

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