DHMNet：

DHMNet implementation：

Pytorch implementation of DHMNet

Requirements:

Please use Python 3.6, opencv-contrib-python (3.4.0.12) and Pytorch (>= 1.1.0). Other dependencies should be easily installed through pip or conda.

Explanation:

If you need YFCC100M and SUN3D datasets, You can visit the code at

https://github.com/zjhthu/OANet.git.

We have uploaded the main code on 'core' folder.

Preparing Data：

Please follow their instructions to download the training and testing data.

bash download_data.sh raw_data raw_data_yfcc.tar.gz 0 8 ## YFCC100M
tar -xvf raw_data_yfcc.tar.gz

bash download_data.sh raw_sun3d_test raw_sun3d_test.tar.gz 0 2 ## SUN3D
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

After downloading the datasets, the initial matches for YFCC100M and SUN3D can be generated as following. Here we provide descriptors for SIFT (default), ORB, and SuperPoint.

cd dump_match
python extract_feature.py
python yfcc.py
python extract_feature.py --input_path=../raw_data/sun3d_test
python sun3d.py

Citing DHMNet

If you find the DHMNet code useful, please consider citing

@article{chen2024dhm,
  title={DHM-Net: Deep Hypergraph Modeling for Robust Feature Matching},
  author={Chen, Shunxing and Xiao, Guobao and Guo, Junwen and Wu, Qiangqiang and Ma, Jiayi},
  journal={IEEE Transactions on Image Processing},
  year={2024},
  publisher={IEEE}
}