DKM: Dense Kernelized Feature Matching for Geometry Estimation
Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck, Michael Felsberg
CVPR 2023

WARNING: DKM is trained on a specific resolution, and is sensitive to the image resolution used. This means that feeding images of a different resolution (higher or lower), may give significantly worse performance. If you're finding DKM to give poor performance, please contact me, it's probably something to do with the input.

• Code for models can be found in dkm/models
• Code for benchmarks can be found in dkm/benchmarks
• Code for reproducing experiments from our paper can be found in experiments/

Run pip install -e .

A demonstration of our method can be run by:

python demo_match.py

This runs our model trained on mega on two images taken from Sacre Coeur.

See Benchmarks for details.

See Training for details.

Given that the required benchmark or training dataset has been downloaded and unpacked, results can be reproduced by running the experiments in the experiments folder.

We recommend using the excellent Graph-Cut RANSAC algorithm: https://github.com/danini/graph-cut-ransac

We have used code and been inspired by https://github.com/PruneTruong/DenseMatching, https://github.com/zju3dv/LoFTR, and https://github.com/GrumpyZhou/patch2pix. We additionally thank the authors of ECO-TR for providing their benchmark.

If you find our models useful, please consider citing our paper!

@inproceedings{edstedt2023dkm,
title={{DKM}: Dense Kernelized Feature Matching for Geometry Estimation},
author={Edstedt, Johan and Athanasiadis, Ioannis and Wadenbäck, Mårten and Felsberg, Michael},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2023}
}