project page

README.md

@@ -1,82 +1 @@
-# 
-<p align="center">
-  <h1 align="center"> <ins>RoMa</ins> 🏛️:<br> Robust Dense Feature Matching</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">Mårten Wadenbäck</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 (TODO)</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.
-
-## 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{edstedt2023roma,
-title={{RoMa: Robust Dense Feature Matching}},
-author={Edstedt, Johan and Sun, Qiyu and Bökman, Georg and Wadenbäck, Mårten and Felsberg, Michael},
-journal={arXiv preprint arXiv:2305.15404},
-year={2023}
-}
-```
+# RoMa Project Page