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Parskatt / RoMa

[CVPR 2024] RoMa: Robust Dense Feature Matching; RoMa is the robust dense feature matcher capable of estimating pixel-dense warps and reliable certainties for almost any image pair.
https://parskatt.github.io/RoMa/
MIT License
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3d-reconstruction dense-matching feature-matching image-matching

readme

RoMa đŸ›ïž:
Robust Dense Feature Matching
⭐CVPR 2024⭐

Johan Edstedt · Qiyu Sun · Georg Bökman · MÄrten WadenbÀck · Michael Felsberg

Paper | Project Page


example
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 3.10), run:

pip install -e .

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 -

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 WadenbÀck, MÄrten and Felsberg, Michael},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2024}
}