---
license: cc-by-nc-sa-4.0
language:
- en
---
<h1 align="center">🦄 UniCorrn: Unified Correspondence Transformer Across 2D and 3D <br> CVPR 2026</h1>

<div align="center">

[Prajnan Goswami<sup>1*</sup>](https://prajnancv.github.io),
[Tianye Ding<sup>1*</sup>](https://jerrygcding.github.io/),
[Feng Liu<sup>2</sup>](https://scholar.google.com/citations?&user=uiqXutMAAAAJ),
[Huaizu Jiang<sup>1</sup>](https://jianghz.me/)\
<sup>1</sup> [Visual Intelligence Lab, Northeastern University](https://github.com/neu-vi), <sup>2</sup> [Adobe Research](https://research.adobe.com)\
<sup>*</sup> Equal Contribution

<a href="https://arxiv.org/abs/2605.04044"><img src="https://img.shields.io/badge/arXiv-2605.04044-b31b1b" alt="arXiv"></a>
<a href="https://neu-vi.github.io/UniCorrn/"><img src="https://img.shields.io/badge/Project_Page-green" alt="Project Page"></a>

</div>


## Overview

UniCorrn is the first correspondence model with shared weights that unifies 2D-2D, 2D-3D, and 3D-3D geometric matching with an end-to-end transformer architecture.

This model space contains the large-scale pretrained weights from Stage 1 and Stage 2 training, as well as a modified version of the CroCoV2 weights adapted to match our model attributes.

## Quick Start

Check out our [Github Repo](https://github.com/neu-vi/UniCorrn).


## Citation
If you find this repository useful in your research, please consider giving a star ⭐ and a citation
```bibtex
@inproceddings{goswami2026unicorrn,
  title={UniCorrn: Unified Correspondence Transformer Across 2D and 3D},
  author={Goswami, Prajnan and Ding, Tianye and Liu, Feng and Jiang, Huaizu},
  booktitle={CVPR},
  year={2026}
}
```