DKM
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[CVPR 2023] DKM: Dense Kernelized Feature Matching for Geometry Estimation
DKM - Deep Kernelized Dense Geometric Matching
Contains code for Deep Kernelized Dense Geometric Matching
⚡ DKMv2 is out, with improved result on ScanNet1500 and MegaDepth1500!
Megadepth1500
@5 | @10 | @20 | |
---|---|---|---|
DKMv1 | 54.5 | 70.7 | 82.3 |
DKMv2 | 56.8 | 72.3 | 83.2 |
ScanNet1500
@5 | @10 | @20 | |
---|---|---|---|
DKMv1 | 24.8 | 44.4 | 61.9 |
DKMv2 | 28.2 | 49.2 | 66.6 |
TODO
- [ ] Provide updated training and higher resolution options for DKMv2
Navigating the Code
- 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/
Install
Run pip install -e .
Demo
A demonstration of our method can be run by:
python demo_match.py
This runs our model trained on mega on two images I took recently in the wild.
Benchmarks
See Benchmarks for details.
Training
See Training for details.
Reproducing Results
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.
Acknowledgements
We have used code and been inspired by https://github.com/PruneTruong/DenseMatching, https://github.com/zju3dv/LoFTR, and https://github.com/GrumpyZhou/patch2pix
BibTeX
If you find our models useful, please consider citing our paper!
@article{edstedt2022deep,
title={Deep Kernelized Dense Geometric Matching},
author={Edstedt, Johan and Wadenb{\"a}ck, M{\aa}rten and Felsberg, Michael},
journal={arXiv preprint arXiv:2202.00667},
year={2022}
}