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

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.

dataset link：https://pan.baidu.com/s/1fyvZqsojIZvZd60PCjONvg?pwd=2525

We use depth maps provided in the original MegaDepth dataset as well as undistorted images, corresponding camera intrinsics and extrinsics preprocessed by D2-Net. You can download them separately from the following links.

We symlink the datasets to the data directory under the main project directory.

# megadepth
# -- # train and test dataset (train and test share the same dataset)
ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/project/data/megadepth/train
ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/project/data/megadepth/test
# -- # dataset indices
ln -s /path/to/megadepth_indices/* /path/to/project/data/megadepth/index

#SynLoc
ln -s /path/to/SynLoc_train/* /path/to/project/data/SynLoc/train

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.

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

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 RoMa.