GLOMAP: Global Structure-from-Motion Revisited

Project page | Paper

About

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},
}

Getting Started

Watch this video to learn how to Install and Use GLOMAP

Install COLMAP

If you have COLMAP already installed and added to path, skip this step.

I recommend downloading the COLMAP pre-built binaries Here

Install GLOMAP

Pre-compiled Windows binaries can be downloaded from the official release page.

Standalone Use

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.

We also provide a guide on improving the obtained reconstruction, which can be found here

Automated Python Scripting

In this section I walk you through how to the run_glomap.py which automates the manual step for running GLOWMAP. Specifically, I added modifiers to output file structure and format to use with 3DGS and Nerfstudio.

Accessing run_glowmap.py

You can either clone this database or manually download the file. Note: only the python script is maintained on this repository. Clone the original project for the most up to date information

Usage

Use run_glowmap.py by passing this command: python run_glowmap.py --image_path path\to\images

note: if the image folder is named "images" or "input" you may have some issues with the script. This will be addressed in future updates

This will run colmap feature_extractor, colmap sequential_matcher, and glowmap mapper sequentially. The data will output in a structure and format immediately usable for the original 3DGS project.

Modifiers

Command Line Arguments for run_glowmap.py

--image_path

Path to the source directory of images.

--matcher_type {sequential_matcher,exhaustive_matcher}

Type of matcher to used by COLMAP (default: sequential_matcher).

--interval {int}

Interval of images to use in source image directory. Increase the number to use less images. For example: 2 uses every other image, 6 uses every 6th image. (default: 1)

--model_type {3dgs,nerfstudio}

Model type to run. '3dgs' includes undistortion, 'nerfstudio' skips undistortion.

Manual GLOWMAP Usage (from original repo)

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

From database

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

From images

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

Visualize and use the results

The results are written out in the COLMAP sparse reconstruction format. Please refer to COLMAP for more details.

The reconstruction can be visualized using the COLMAP GUI, for example:

colmap gui --import_path ./output/south-building/sparse/0 \
--image_path ./data/gerrard-hall/images \
--database_path ./data/gerrard-hall/database.db 

Alternatives like rerun.io also enable visualization of COLMAP and GLOMAP outputs.

If you want to inspect the reconstruction programmatically, you can use pycolmap in Python or link against COLMAP's C++ library interface.

Notes From Original Project Page

• 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 retrieval and matching with learning-based descriptors.

Acknowledgement

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

Support

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.

Contribution

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.

License

Copyright (c) 2024, ETH Zurich.
All rights reserved.

Redistribution and use in source and binary forms, with or without
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    * Neither the name of ETH Zurich nor the names of its contributors may
      be used to endorse or promote products derived from this software
      without specific prior written permission.

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