DKM: Dense Kernelized Feature Matching for Geometry Estimation

Project Page | Paper

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.

How to Use?

Our model produces a dense (for all pixels) warp and certainty.

Warp: [B,H,W,4] for all images in batch of size B, for each pixel HxW, we ouput the input and matching coordinate in the normalized grids [-1,1]x[-1,1].

Certainty: [B,H,W] a number in each pixel indicating the matchability of the pixel.

See demo for two demos of DKM.

See api.md for API.

Qualitative Results

https://user-images.githubusercontent.com/22053118/223748279-0f0c21b4-376a-440a-81f5-7f9a5d87483f.mp4

https://user-images.githubusercontent.com/22053118/223748512-1bca4a17-cffa-491d-a448-96aac1353ce9.mp4

https://user-images.githubusercontent.com/22053118/223748518-4d475d9f-a933-4581-97ed-6e9413c4caca.mp4

https://user-images.githubusercontent.com/22053118/223748522-39c20631-aa16-4954-9c27-95763b38f2ce.mp4

Benchmark Results

Megadepth1500

	@5	@10	@20
DKMv1	54.5	70.7	82.3
DKMv2	56.8	72.3	83.2
DKMv3 (paper)	60.5	74.9	85.1
DKMv3 (this repo)	60.0	74.6	84.9

Megadepth 8 Scenes

	@5	@10	@20
DKMv3 (paper)	60.5	74.5	84.2
DKMv3 (this repo)	60.4	74.6	84.3

ScanNet1500

	@5	@10	@20
DKMv1	24.8	44.4	61.9
DKMv2	28.2	49.2	66.6
DKMv3 (paper)	29.4	50.7	68.3
DKMv3 (this repo)	29.8	50.8	68.3

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 taken from Sacre Coeur.

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.

Using DKM matches for estimation

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

	@5	@10	@20
DKMv3 (RANSAC)	60.5	74.9	85.1
DKMv3 (GC-RANSAC)	65.5	78.0	86.7

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. We additionally thank the authors of ECO-TR for providing their benchmark.

BibTeX

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}
}