Image Matching Models (IMM)

A unified API for quickly and easily trying 29 (and growing!) image matching models.

Jump to: Install | Use | Models | Add a Model/Contributing | Acknowledgements | Cite

Matching Examples

Compare matching models across various scenes. For example, we show SIFT-LightGlue and LoFTR matches on pairs:

(1) outdoor, (2) indoor, (3) satellite remote sensing, (4) paintings, and (5) a false positive.

SIFT-LightGlue

LoFTR

Extraction Examples

You can also extract keypoints and associated descriptors.

SIFT and DeDoDe

Install

From Source [Recommended]

If you want to to install from source (easiest to edit, use benchmark.py, demo.ipynb),

git clone --recursive https://github.com/gmberton/image-matching-models
cd image-matching-models
pip install .

Some models (omniglue, LoFTR family) require one-off dependencies (tensorflow, pytorch-lightning), which are not included in the default list. To install these, use

pip install .[all]

This will install all dependencies needed to run all models.

We recommend using torch>=2.2, we didn't test most models with older versions.

As package (simplest)

You can install directly to your package directory with

pip install git+https://github.com/gmberton/image-matching-models.git

Similar to the above, to get all optional dependencies, use the [all] addendum:

pip install "image-matching-models[all] @ git+https://github.com/gmberton/image-matching-models.git"

Use

You can use any of the matchers with

from matching import get_matcher
from matching.viz import plot_matches

device = 'cuda'  # 'cpu'
matcher = get_matcher('superpoint-lg', device=device)  # Choose any of our ~30+ matchers listed below
img_size = 512  # optional

img0 = matcher.load_image('assets/example_pairs/outdoor/montmartre_close.jpg', resize=img_size)
img1 = matcher.load_image('assets/example_pairs/outdoor/montmartre_far.jpg', resize=img_size)

result = matcher(img0, img1)
num_inliers, H, inlier_kpts0, inlier_kpts1 = result['num_inliers'], result['H'], result['inlier_kpts0'], result['inlier_kpts1']
# result.keys() = ['num_inliers', 'H', 'all_kpts0', 'all_kpts1', 'all_desc0', 'all_desc1', 'matched_kpts0', 'matched_kpts1', 'inlier_kpts0', 'inlier_kpts1']
plot_matches(img0, img1, result, save_path='plot_matches.png')

You can also run this as a standalone script, which will perform inference on the the examples inside ./assets. You may also resolution (im_size) and number of keypoints (n_kpts). This will take a few seconds on a laptop's CPU, and will produce the same images that you see above.

python main_matcher.py --matcher sift-lg --device cpu --out_dir output_sift-lg

where sift-lg will use SIFT + LightGlue.

The script will generate an image with the matching keypoints for each pair, under ./output_sift-lg.

Use on your own images

To use on your images you have three options:

1. create a directory with sub-directories, with two images per sub-directory, just like ./assets/example_pairs. Then use as python main_matcher.py --input path/to/dir
2. create a file with pairs of paths, separate by a space, just like assets/example_pairs_paths.txt. Then use as python main_matcher.py --input path/to/file.txt
3. import the matcher package into a script/notebook and use from there, as in the example above

Keypoint Extraction and Description

To extract keypoints and descriptions (when available) from a single image, use the extract() method.

from matching import get_matcher

device = 'cuda' # 'cpu'
matcher = get_matcher('superglue', device=device)  # Choose any of our ~30+ matchers listed below
img_size = 512 # optional

img = matcher.load_image('assets/example_pairs/outdoor/montmartre_close.jpg', resize=img_size)

result = matcher.extract(img)
# result.keys() = ['all_kpts0', 'all_desc0']
plot_kpts(img, result)

As with matching, you can also run extraction from the command line

python main_extractor.py --matcher sift-lg --device cpu --out_dir output_sift-lg --n_kpts 2048

Available Models

You can choose any of the following methods (input to get_matcher()):

Dense: roma, tiny-roma, dust3r, mast3r

Semi-dense: loftr, eloftr, se2loftr, aspanformer, matchformer, xfeat-star

Sparse: [sift, superpoint, disk, aliked, dedode, doghardnet, gim, xfeat]-lg, dedode, steerers, dedode-kornia, [sift, orb, doghardnet]-nn, patch2pix, superglue, r2d2, d2net, gim-dkm, xfeat, omniglue, [dedode, xfeat, aliked]-subpx

Tip

You can pass a list of matchers, i.e. get_matcher([xfeat, tiny-roma]) to run both matchers and concatenate their keypoints.

All the matchers can run on GPU, and most of them can run both on GPU or CPU. A few can't run on CPU.

Model Details

Important

Check the LICENSE of each model/original code base before use in your application. Some are heavily restricted.

Model	Code	Paper	GPU Runtime (s/img)	CPU Runtime (s/img)
Keypt2Subpx* (ECCV '24)	Official	arxiv	0.055 /0.164 / 0.033 / 0.291	--
MASt3R (ArXiv '24)	Official	arxiv	0.699	--
Efficient-LoFTR (CVPR '24)	Official	pdf	0.1026	2.117
OmniGlue (CVPR '24)	Official	arxiv	❌	6.351
xFeat (CVPR '24)	Official	arxiv	0.027	0.048
GIM (ICLR '24)	Official	arxiv	0.077 (+LG) / 1.627 (+DKMv3)	5.321 (+LG) / 20.301 (+DKMv3)
RoMa / Tiny-RoMa (CVPR '24)	Official	arxiv	0.453 / 0.0456	18.950
DUSt3R (CVPR '24)	Official	arxiv	3.639	26.813
DeDoDe (3DV '24)	Official	arxiv	0.311 (+MNN)/ 0.218 (+LG)	❌
Steerers (CVPR '24)	Official	arxiv	0.150	❌
LightGlue* (ICCV '23)	Official	arxiv	0.417 / 0.093 / 0.184 / 0.128	2.828 / 8.852 / 8.100 / 8.128
SE2-LoFTR (CVPRW '22)	Official	arxiv	0.133	2.378
Aspanformer (ECCV '22)	Official	arxiv	0.384	11.73
Matchformer (ACCV '22)	Official	arxiv	0.232	6.101
LoFTR (CVPR '21)	Official / Kornia	arxiv	0.722	2.36
Patch2Pix (CVPR '21)	Official / IMT	arxiv	0.145	4.97
SuperGlue (CVPR '20)	Official / IMT	arxiv	0.0894	2.178
R2D2 (NeurIPS '19)	Official / IMT	arxiv	0.429	6.79
D2Net (CVPR '19)	Official / IMT	arxiv	0.600	1.324
SIFT-NN (IJCV '04)	OpenCV	pdf	0.124	0.117
ORB-NN (ICCV '11)	OpenCV	ResearchGate	0.088	0.092
DoGHardNet (NeurIPS '17)	IMT / Kornia	arxiv	2.697 (+NN) / 0.526 (+LG)	2.438(+NN) / 4.528 (+LG)

Our implementation of Patch2Pix (+ Patch2PixSuperGlue), R2D2, and D2Net are based on the Image Matching Toolbox (IMT). LoFTR and DeDoDe-Lightglue are from Kornia. Other models are based on the offical repos above.

Runtime benchmark is the average of 5 iterations over the 5 pairs of examples in the assets/example_pairs folder at image size 512x512. Benchmark is done using benchmark.py on an NVIDIA RTX A4000 GPU. Results rounded to the hundredths place.

* LightGlue model runtimes are listed in the order: SIFT, SuperPoint, Disk, ALIKED

* Keypt2Subpx model runtimes are listed in the order: superpoint-lg, aliked-lg, xfeat, dedode

Adding a new method

See CONTRIBUTING.md for details.

Note

This repo is optimized usability, but necessarily for speed. The idea is to use this repo to find the matcher that best suits your needs, and then use the original code to get the best out of it.

Acknowledgements

Special thanks to the authors of the respective works that are included in this repo (see their papers above). Additional thanks to @GrumpyZhou for developing and maintaining the Image Matching Toolbox, which we have wrapped in this repo, and the maintainers of Kornia.

Cite

This repo was created as part of the EarthMatch paper. Please consider citing EarthMatch work if this repo is helpful to you!

@InProceedings{Berton_2024_EarthMatch,
    author    = {Berton, Gabriele and Goletto, Gabriele and Trivigno, Gabriele and Stoken, Alex and Caputo, Barbara and Masone, Carlo},
    title     = {EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2024},
}