This simple tool efficiently matches image pairs using multiple famous image matching algorithms. The tool features a Graphical User Interface (GUI) designed using gradio. You can effortlessly select two images and a matching algorithm and obtain a precise matching result. Note: the images source can be either local images or webcam images.

Try it on

Here is a demo of the tool:

The tool currently supports various popular image matching algorithms, namely:

• XoFTR, CVPR 2024
• EfficientLoFTR, CVPR 2024
• MASt3R, CVPR 2024
• DUSt3R, CVPR 2024
• OmniGlue, CVPR 2024
• XFeat, CVPR 2024
• RoMa, CVPR 2024
• DeDoDe, 3DV 2024
• Mickey, CVPR 2024
• GIM, ICLR 2024
• ALIKED, ICCV 2023
• LightGlue, ICCV 2023
• DarkFeat, AAAI 2023
• SFD2, CVPR 2023
• IMP, CVPR 2023
• ASTR, CVPR 2023
• SEM, CVPR 2023
• DeepLSD, CVPR 2023
• GlueStick, ICCV 2023
• ConvMatch, AAAI 2023
• LoFTR, CVPR 2021
• SOLD2, CVPR 2021
• LineTR, RA-L 2021
• DKM, CVPR 2023
• NCMNet, CVPR 2023
• TopicFM, AAAI 2023
• AspanFormer, ECCV 2022
• LANet, ACCV 2022
• LISRD, ECCV 2022
• REKD, CVPR 2022
• CoTR, ICCV 2021
• ALIKE, TMM 2022
• RoRD, IROS 2021
• SGMNet, ICCV 2021
• SuperPoint, CVPRW 2018
• SuperGlue, CVPR 2020
• D2Net, CVPR 2019
• R2D2, NeurIPS 2019
• DISK, NeurIPS 2020
• Key.Net, ICCV 2019
• OANet, ICCV 2019
• SOSNet, CVPR 2019
• HardNet, NeurIPS 2017
• SIFT, IJCV 2004

Just try it on

or deploy it locally following the instructions below.

git clone --recursive https://github.com/Vincentqyw/image-matching-webui.git
cd image-matching-webui
conda env create -f environment.yaml
conda activate imw

or using docker:

docker pull vincentqin/image-matching-webui:latest
docker run -it -p 7860:7860 vincentqin/image-matching-webui:latest python app.py --server_name "0.0.0.0" --server_port=7860

Deploy to Railway, setting up a Custom Start Command in Deploy section:

python -m api.server

python ./app.py

then open http://localhost:7860 in your browser.

I provide an example to add local feature in hloc/extractors/example.py. Then add feature settings in confs in file hloc/extract_features.py. Last step is adding some settings to matcher_zoo in file ui/config.yaml.

External contributions are very much welcome. Please follow the PEP8 style guidelines using a linter like flake8. This is a non-exhaustive list of features that might be valuable additions:

• add CPU CI
• add webcam support
• add line feature matching algorithms
• example to add a new feature extractor / matcher
• ransac to filter outliers
• add rotation images options before matching
• support export matches to colmap (#issue 6)
• add config file to set default parameters
• dynamically load models and reduce GPU overload

Adding local features / matchers as submodules is very easy. For example, to add the GlueStick:

git submodule add https://github.com/cvg/GlueStick.git third_party/GlueStick

If remote submodule repositories are updated, don't forget to pull submodules with:

git submodule update --init --recursive
git submodule update --remote

if you only want to update one submodule, use git submodule update --remote third_party/GlueStick.

To format code before committing, run:

pre-commit run -a  # Auto-checks and fixes

• Image Matching: Local Features & Beyond
• Long-term Visual Localization

This code is built based on Hierarchical-Localization. We express our gratitude to the authors for their valuable source code.