# RoMaV2

**Repository Path**: assets01/RoMaV2

## Basic Information

- **Project Name**: RoMaV2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-08-13
- **Last Updated**: 2026-08-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">
  <h1 align="center"> <ins>RoMa v2</ins> 🤖: Harder Better Faster Denser Feature Matching
  <h2 align="center">
    <a href="https://scholar.google.com/citations?user=Ul-vMR0AAAAJ">Johan Edstedt</a>
    ·
    <a href="https://scholar.google.com/citations?user=-vJPE04AAAAJ">David Nordström</a>
    ·
    <a href="https://scholar.google.com/citations?user=mvY4rdIAAAAJ">Yushan Zhang</a>
    ·
    <a href="https://scholar.google.com/citations?user=FUE3Wd0AAAAJ">Georg Bökman</a>
    ·
    <a href="https://scholar.google.com/citations?user=dsEPAvUAAAAJ">Jonathan Astermark</a>
    ·
    <a href="https://scholar.google.com/citations?user=vHeD0TYAAAAJ">Viktor Larsson</a>
    ·
    <a href="https://scholar.google.com/citations?user=9j-6i_oAAAAJ&hl">Anders Heyden</a>
    ·
    <a href="https://scholar.google.com/citations?user=P_w6UgMAAAAJ&hl">Fredrik Kahl</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/2511.15706" align="center">Paper</a> | 
    <a href="TBD" align="center">Project Page</a>
  </p></h2>
  <div align="center"></div>
</p>
<br/>
<p align="center">
    <img src="assets/qualitative.png" alt="example" width=80%>
</p>

## How to Use
```python
from romav2 import RoMaV2

# load pretrained model
model = RoMaV2()
# Match densely for any image-like pair of inputs
preds = model.match(img_A_path, img_B_path)

# you can also run the forward method directly as 
# preds = model(img_A, img_B)

# Sample 5000 matches for estimation
matches, overlaps, precision_AB, precision_BA = model.sample(preds, 5000)

# Convert to pixel coordinates (RoMaV2 produces matches in [-1,1]x[-1,1])
kptsA, kptsB = 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
)
```
We additionally provide two demos in the [demos folder](demo), which might help in understanding.


## Setup/Install
In your python environment (tested on Linux python 3.12), run:
```bash
uv pip install -e .
```
or 
```bash
uv sync
```

## Benchmarks
If you do not already have MegaDepth and ScanNet, you can the following to download them:
```bash
source scripts/eval_prep.sh
```
### Mega-1500
```bash
uv run tests/test_mega1500.py
```
### ScanNet-1500
```bash
uv run tests/test_scannet1500.py
```
### Expected Results
Experiments on ScanNet-1500 and MegaDepth-1500 are provided in the [tests folder](tests).
Running these gave me `ScanNet-1500: [34.0, 56.5, 73.9]`, and `Mega-1500: [62.8, 76,8, 86.5]`, which are similar to the results of the paper.


## Fused local correlation kernel
Include the `--extra fused-local-corr` flag as:
```bash
uv sync --extra fused-local-corr
```
or 
```bash
uv pip install romav2[fused-local-corr]
```
or
```bash
uv add romav2[fused-local-corr]
```

## Settings
By twiddling with some different settings you may reach better results on your task of interest.
Some important ones, which we enable setting to some reasonable defaults through `model.apply_setting`, are:

`model.H_lr, model.W_lr`: height and width for the image pair.

`model.H_hr, model.W_hr`: height and width for a high resolution version of the image pair (used for upsampling as in RoMa)

`model.bidirectional`: Useful for getting more diverse matches, and for estimating the covariance matrix in both directions.

`model.threshold`: Value between [0,1]. Used to set overlap prediction above it to 1. Useful for Mega1500.

`model.balanced_sampling`: Diverse sampling, same as RoMa. Typically helps to get better RANSAC estimates.

## License
All our code except DINOv3 is MIT license.
DINOv3 has a custom license, see [DINOv3](https://github.com/facebookresearch/dinov3/tree/main?tab=License-1-ov-file#readme).

## Acknowledgement
Our codebase builds mainly on the code in [RoMa](https://github.com/Parskatt/RoMa).
We were additionally inspired by [UFM](https://github.com/UniFlowMatch/UFM) and [MapAnything](https://github.com/facebookresearch/map-anything), particularly for the datasets used to train the models.

## BibTeX
If you find our models useful, please consider citing our paper!
```
@article{edstedt2025romav2,
  title={{RoMa v2: Harder Better Faster Denser Feature Matching}},
  author={Johan Edstedt, David Nordström, Yushan Zhang, Georg Bökman, Jonathan Astermark, Viktor Larsson, Anders Heyden, Fredrik Kahl, Mårten Wadenbäck, Michael Felsberg},
  journal={arXiv preprint arXiv:2511.15706},
  year={2025}
}
```