romatch 0.1.1

Robust Dense Feature Matching

RoMa 🏛️:
Robust Dense Feature Matching
⭐CVPR 2024⭐

Johan Edstedt · Qiyu Sun · Georg Bökman · Mårten Wadenbäck · Michael Felsberg

Paper | Project Page

RoMa is the robust dense feature matcher capable of estimating pixel-dense warps and reliable certainties for almost any image pair.

Setup/Install

In your python environment (tested on Linux python 3.12), run:

uv pip install -e .

or

uv sync

You can also install romatch directly as a package from PyPI by

uv pip install romatch

Demo / How to Use

We provide two demos in the demos folder. Here's the gist of it:

from romatch import roma_outdoor
roma_model = roma_outdoor(device=device)
# Match
warp, certainty = roma_model.match(imA_path, imB_path, device=device)
# Sample matches for estimation
matches, certainty = roma_model.sample(warp, certainty)
# Convert to pixel coordinates (RoMa produces matches in [-1,1]x[-1,1])
kptsA, kptsB = roma_model.to_pixel_coordinates(matches, H_A, W_A, H_B, W_B)
# Find a fundamental matrix (or anything else of interest)
F, mask = cv2.findFundamentalMat(
    kptsA.cpu().numpy(), kptsB.cpu().numpy(), ransacReprojThreshold=0.2, method=cv2.USAC_MAGSAC, confidence=0.999999, maxIters=10000
)

New: You can also match arbitrary keypoints with RoMa. See match_keypoints in RegressionMatcher.

Settings

Resolution

By default RoMa uses an initial resolution of (560,560) which is then upsampled to (864,864). You can change this at construction (see roma_outdoor kwargs). You can also change this later, by changing the roma_model.w_resized, roma_model.h_resized, and roma_model.upsample_res.

Sampling

roma_model.sample_thresh controls the thresholding used when sampling matches for estimation. In certain cases a lower or higher threshold may improve results.

Reproducing Results

The experiments in the paper are provided in the experiments folder.

Training

1. First follow the instructions provided here: https://github.com/Parskatt/DKM for downloading and preprocessing datasets.
2. Run the relevant experiment, e.g.,

torchrun --nproc_per_node=4 --nnodes=1 --rdzv_backend=c10d experiments/roma_outdoor.py

Testing

python experiments/roma_outdoor.py --only_test --benchmark mega-1500

License

All our code except DINOv2 is MIT license. DINOv2 has an Apache 2 license DINOv2.

Acknowledgement

Our codebase builds on the code in DKM.

Tiny RoMa

If you find that RoMa is too heavy, you might want to try Tiny RoMa which is built on top of XFeat.

from romatch import tiny_roma_v1_outdoor
tiny_roma_model = tiny_roma_v1_outdoor(device=device)

Mega1500:

	AUC@5	AUC@10	AUC@20
XFeat	46.4	58.9	69.2
XFeat*	51.9	67.2	78.9
Tiny RoMa v1	56.4	69.5	79.5
RoMa	-	-	-

Mega-8-Scenes (See DKM):

	AUC@5	AUC@10	AUC@20
XFeat	-	-	-
XFeat*	50.1	64.4	75.2
Tiny RoMa v1	57.7	70.5	79.6
RoMa	-	-	-

IMC22 :'):

	mAA@10
XFeat	42.1
XFeat*	-
Tiny RoMa v1	42.2
RoMa	-

Reproducibility

There are a few diffs in the current codebase compared to the original repo used to run experiments.

1. The scale_factor used in the match method now is relative to the original training resolution of 560. Previosly it was based on the set coarse resolution (which might or might not be 560).
2. Newer PyTorch, original code used something like 2.1.
3. Stochastic eval: both RANSAC and the chosen correspondences can affect results in Mega1500.
4. Matrix inverse in GP has been replaced with cholesky decomp.

That being said, if diff of results are $>0.5$ there probably is something wrong, please let me know.

BibTeX

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

@article{edstedt2024roma,
title={{RoMa: Robust Dense Feature Matching}},
author={Edstedt, Johan and Sun, Qiyu and Bökman, Georg and Wadenbäck, Mårten and Felsberg, Michael},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2024}
}