https://github.com/cvg/gluestick

Joint Deep Matcher for Points and Lines 🖼️💥🖼️ (ICCV 2023)
https://github.com/cvg/gluestick

computer-vision deep-learning graph-neural-networks image-matching local-features machine-learning torch

Last synced: 2 days ago
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Joint Deep Matcher for Points and Lines 🖼️💥🖼️ (ICCV 2023)

• Host: GitHub
• URL: https://github.com/cvg/gluestick
• Owner: cvg
• License: mit
• Created: 2023-04-03T08:19:25.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-17T11:37:51.000Z (27 days ago)
• Last Synced: 2024-10-19T15:28:56.410Z (25 days ago)
• Topics: computer-vision, deep-learning, graph-neural-networks, image-matching, local-features, machine-learning, torch
• Language: Jupyter Notebook
• Homepage: https://iago-suarez.com/gluestick
• Size: 34.9 MB
• Stars: 555
• Watchers: 16
• Forks: 44
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GlueStick

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cvg/GlueStick/blob/main/gluestick_matching_demo.ipynb) [![arXiv](https://img.shields.io/badge/arXiv-2304.02008-b31b1b.svg?style=flat)](https://arxiv.org/abs/2304.02008) [![Project Page](https://badgen.net/badge/color/project/green?icon=awesome&label)](https://iago-suarez.com/gluestick)

Joint deep matcher for points and lines 🖼️💥🖼️

**Update: we are pleased to announce that the training code has been released within our new training framework, [GlueFactory](https://github.com/cvg/glue-factory).**

![Visualization of point and line matches](resources/demo_seq1.gif)

This repository contains the official implementation of 

[GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://arxiv.org/abs/2304.02008), accepted at ICCV 2023.

## Install 🛠️

To install the software in Ubuntu 22.04 follow these instructions:

```bash

sudo apt-get install build-essential cmake libopencv-dev libopencv-contrib-dev

git clone --recursive https://github.com/cvg/GlueStick.git

cd GlueStick

# Create and activate a virtual environment

python -m venv venv

source venv/bin/activate

pip install -r requirements.txt

pip install .

```

## Running GlueStick 🏃

Download the weights of the model:

```

wget https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar -P resources/weights

```

You can execute the inference with it with:

```

python -m gluestick.run -img1 resources/img1.jpg -img2 resources/img2.jpg

```

## Training 🏋️

The training code is available in a separate repository, [GlueFactory](https://github.com/cvg/glue-factory). Within GlueFactory, you can not only train GlueStick, but also other deep matchers such as [LightGlue](https://github.com/cvg/LightGlue), use multiple feature extractors, line extractors, robust estimators, as well as run evaluations on multiple benchmarks.

## Licence 📜

Our code is licenced under [MIT licence](https://github.com/cvg/GlueStick/blob/main/LICENSE).

However, bear in mind that it uses a SuperPoint backbone that has a 

[non-commercial licence](https://github.com/magicleap/SuperPointPretrainedNetwork/blob/master/LICENSE). 

Therefore, the overall system is non-commercial 😞. We are working on an analogous version based on 

[DISK](https://github.com/cvlab-epfl/disk) to avoid this problem.

## Citation 📝

If you use this code in your project, please consider citing the following paper:

```bibtex

@InProceedings{pautrat_suarez_2023_gluestick,

    title={{GlueStick}: Robust Image Matching by Sticking Points and Lines Together},

    author={Pautrat, R{\'e}mi* and Su{\'a}rez, Iago* and Yu, Yifan and Pollefeys, Marc and Larsson, Viktor},

    booktitle={International Conference on Computer Vision (ICCV)},

    year={2023}

}

```