# <p align="center"> <h1 align="center"> <ins>RoMa</ins> đïž:<br> Robust Dense Feature Matching <br> âCVPR 2024â</h1> <p align="center"> <a href="https://scholar.google.com/citations?user=Ul-vMR0AAAAJ">Johan Edstedt</a> · <a href="https://scholar.google.com/citations?user=HS2WuHkAAAAJ">Qiyu Sun</a> · <a href="https://scholar.google.com/citations?user=FUE3Wd0AAAAJ">Georg Bökman</a> · <a href="https://scholar.google.com/citations?user=6WRQpCQAAAAJ">MĂ„rten WadenbĂ€ck</a> · <a href="https://scholar.google.com/citations?user=lkWfR08AAAAJ">Michael Felsberg</a> </p> <h2 align="center"><p> <a href="https://arxiv.org/abs/2305.15404" align="center">Paper</a> | <a href="https://parskatt.github.io/RoMa" align="center">Project Page</a> </p></h2> <div align="center"></div> </p> <br/> <p align="center"> <img src="https://github.com/Parskatt/RoMa/assets/22053118/15d8fea7-aa6d-479f-8a93-350d950d006b" alt="example" width=80%> <br> <em>RoMa is the robust dense feature matcher capable of estimating pixel-dense warps and reliable certainties for almost any image pair.</em> </p> ## Setup/Install In your python environment (tested on Linux python 3.10), run: ```bash pip install -e . ``` ## Demo / How to Use We provide two demos in the [demos folder](demo). Here's the gist of it: ```python 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. ## 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](experiments). ### 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., ```bash torchrun --nproc_per_node=4 --nnodes=1 --rdzv_backend=c10d experiments/roma_outdoor.py ``` ### Testing ```bash 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](https://github.com/facebookresearch/dinov2/blob/main/LICENSE). ## Acknowledgement Our codebase builds on the code in [DKM](https://github.com/Parskatt/DKM). ## 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 WadenbĂ€ck, MĂ„rten and Felsberg, Michael}, journal={IEEE Conference on Computer Vision and Pattern Recognition}, year={2024} } ```