# CGR-Net

**Repository Path**: chuyongwei/CGR-Net

## Basic Information

- **Project Name**: CGR-Net
- **Description**: CGR-Net：一个专注于计算机图形与检测的
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-16
- **Last Updated**: 2025-09-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CGR-Net implementation

This paper focuses on establishing correspondences between two images.

If you find this project useful, please cite:

```
@article{yang2024cgr,
 title={CGR-Net: Consistency Guided ResFormer for Two-View Correspondence Learning},
author={Yang, Changcai and Li, Xiaojie and Ma, Jiayi and Zhuang, Fengyuan and Wei, Lifang and Chen, Riqing and Chen, Guodong},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2024},
publisher={IEEE} }
```

## Requirements

Please use Python 3.6, opencv-contrib-python (3.4.0.12) and Pytorch (>= 1.7.0). Other dependencies should be easily installed through pip or conda.


## Example scripts

### Run the demo

For a quick start, clone the repo and download the pretrained model.
```bash
git clone https://github.com/XiaojieLi11/CGR-Net 

```

### Generate training and testing data

First download YFCC100M dataset.
```bash
bash download_data.sh raw_data raw_data_yfcc.tar.gz 0 8
tar -xvf raw_data_yfcc.tar.gz
```

Download SUN3D testing (1.1G) and training (31G) dataset if you need.
```bash
bash download_data.sh raw_sun3d_test raw_sun3d_test.tar.gz 0 2
tar -xvf raw_sun3d_test.tar.gz
bash download_data.sh raw_sun3d_train raw_sun3d_train.tar.gz 0 63
tar -xvf raw_sun3d_train.tar.gz
```

Then generate matches for YFCC100M and SUN3D (only testing) with SIFT.
```bash
cd ../dump_match
python extract_feature.py
python yfcc.py
python extract_feature.py --input_path=../raw_data/sun3d_test
python sun3d.py
```
Generate SUN3D training data if you need by following the same procedure and uncommenting corresponding lines in `sun3d.py`.


### Train model on YFCC100M or SUN3D

After generating dataset for YFCC100M, run the tranining script.
```bash
cd ./core 
python main.py
```

You can change the default settings for network structure and training process in `./core/config.py`.

### Train with your own local feature or data 

The provided models are trained using SIFT. You had better retrain the model if you want to use CGR-Net with your own local feature, such as RootSIFT, SuperPoint and etc. 

You can follow the provided example scirpts in `./dump_match` to generate dataset for your own local feature or data.

## Acknowledgement
This code is borrowed from [OANet](https://github.com/zjhthu/OANet) and [CLNet](https://github.com/sailor-z/CLNet). If using the part of code related to data generation, testing and evaluation, please cite these papers.

```
@inproceedings{zhang2019learning,
  title={Learning two-view correspondences and geometry using order-aware network},
  author={Zhang, Jiahui and Sun, Dawei and Luo, Zixin and Yao, Anbang and Zhou, Lei and Shen, Tianwei and Chen, Yurong and Quan, Long and Liao, Hongen},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={5845--5854},
  year={2019}
}
@inproceedings{zhao2021progressive,
  title={Progressive correspondence pruning by consensus learning},
  author={Zhao, Chen and Ge, Yixiao and Zhu, Feng and Zhao, Rui and Li, Hongsheng and Salzmann, Mathieu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6464--6473},
  year={2021}
}
```