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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}
}