GLOMAP: Global Structure-from-Motion Revisited

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.

Getting Started

The dependency in this repo needs cmake version > 3.28. So, you have to make sure that you have correct version of cmake.

sudo snap install cmake --classic

Install the dependencies as follows:

sudo apt install ninja-build build-essential libeigen3-dev libsuitesparse-dev libblas-dev libceres-dev libboost-all-dev libflann-dev libsqlite3-dev libgl1-mesa-dev libglu1-mesa-dev freeglut3-dev libcgal-dev qtbase5-dev qtchooser qt5-qmake qtbase5-dev-tools libmetis-dev libfreeimage-dev libglew-dev

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

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

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/south-building/database.db \
    --image_path    ./data/south-building/images \
    --output_path   ./output/south-building/sparse

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

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.

Data Conversion

To convert the data using colmap, you have to install the colmap library.

sudo apt install colmap

Now, convert the data from .bin to .txt format:

colmap model_converter --input_path . --output_path ./output --output_type TXT

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/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

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:

cd ~/glomap/south-building
colmap gui --import_path 0/ --database_path database.db --image_path images

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

• 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.