Johan Edstedt · Qiyu Sun · Georg Bökman · Mårten Wadenbäck · Michael Felsberg
RoMa is the robust dense feature matcher capable of estimating pixel-dense warps and reliable certainties for almost any image pair.
In your python environment (tested on Linux python 3.10), run:
pip install -e .
We provide two demos in the demos folder. Here's the gist of it:
from roma 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. A demo for this will be added soon.
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.
roma_model.sample_thresh controls the thresholding used when sampling matches for estimation. In certain cases a lower or higher threshold may improve results.
The experiments in the paper are provided in the experiments folder.
- First follow the instructions provided here: https://github.com/Parskatt/DKM for downloading and preprocessing datasets.
- Run the relevant experiment, e.g.,
torchrun --nproc_per_node=4 --nnodes=1 --rdzv_backend=c10d experiments/roma_outdoor.py
python experiments/roma_outdoor.py --only_test --benchmark mega-1500
All our code except DINOv2 is MIT license. DINOv2 has an Apache 2 license DINOv2.
Our codebase builds on the code in DKM.
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}
}