MCNet

Pytorch implementation of MCNet for IEEE/CAA JAS 2023 paper "MCNet: Multiscale Clustering Network for Two-View Geometry Learning and Feature Matching", by Gang Wang and Yufei Chen.

The pretrained models can be downloaded and saved in the 'model' folder, including 'yfcc-sift', 'yfcc-superpoint', 'sun3d-sift', and 'sun3d-superpoint'.

If you find this project useful, please cite:

@article{wang2023mcnet,
  title={MCNet: Multiscale Clustering Network for Two-View Geometry Learning and Feature Matching},
  author={Wang, Gang and Chen, Yufei},
  journal={IEEE/CAA Journal of Automatica Sinica},
  volume={10},
  number={6},
  pages={1507--1509},
  year={2023},
  publisher={IEEE}
}

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.

Acknowledgement

This code is heavily borrowed from OANet. If you use the part of code related to data generation, testing and evaluation, you should cite this paper and follow its license.

@article{zhang2019oanet,
  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},
  journal={International Conference on Computer Vision (ICCV)},
  year={2019}
}