Update README.md

README.md

@@ -1,8 +1,13 @@
 # RoMa: Revisiting Robust Losses for Dense Feature Matching
+### [Project Page (TODO)](https://parskatt.github.io/RoMa) | [Paper](https://arxiv.org/abs/2305.15404)
+<br/>
+
+> RoMa: Revisiting Robust Lossses for Dense Feature Matching  
+> [Johan Edstedt](https://scholar.google.com/citations?user=Ul-vMR0AAAAJ), [Qiyu Sun](https://scholar.google.com/citations?user=HS2WuHkAAAAJ), [Georg Bökman](https://scholar.google.com/citations?user=FUE3Wd0AAAAJ), [Mårten Wadenbäck](https://scholar.google.com/citations?user=6WRQpCQAAAAJ), [Michael Felsberg](https://scholar.google.com/citations?&user=lkWfR08AAAAJ)  
+> Arxiv 2023
 
 **NOTE!!! Very early code, there might be bugs**
 
-The experiments in the paper are provided in the [experiments folder](experiments).
 The codebase is in the [roma folder](roma).
 
 ## Setup/Install
@@ -10,17 +15,47 @@ 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
+)
+```
+## Reproducing Results
+The experiments in the paper are provided in the [experiments folder](experiments).
 
-## Training
+### 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
+### Testing
 ```bash
 python experiments/roma_outdoor.py --only_test --benchmark mega-1500
 ```
 
 ## Acknowledgement
-Our codebase builds on the code in [DKM](https://github.com/Parskatt/DKM).
+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}: Revisiting Robust Lossses for 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}
+}
+```