lyz8023lyp / ncmnet

[CVPR2023] Progressive Neighbor Consistency Mining for Correspondence Pruning

NCMNet

(CVPR 2023) PyTorch implementation of Paper "Progressive Neighbor Consistency Mining for Correspondence Pruning"

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.

Citing NCMNet

If you find the NCMNet code useful, please consider citing:

@inproceedings{liu2023ncmnet,
  title={Progressive Neighbor Consistency Mining for Correspondence Pruning},
  author={Liu, Xin and Yang, Jufeng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision.},
  year={2023}
}

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

Testing and Training Model

We provide a pretrained model on YFCC100M. The results in our paper can be reproduced by running the test script:

cd code 
python main.py --run_mode=test --model_path=../model/yfcc --res_path=../model/yfcc 

Set --use_ransac=True to get results after RANSAC post-processing.

If you want to retrain the model on YFCC100M, run the tranining script.

cd code 
python main.py 

You can also retrain the model on SUN3D by modifying related settings in code\config.py.

Acknowledgement

This code is heavily borrowed from [OANet] [CLNet]. If you use the part of code related to data generation, testing, or evaluation, you should cite these papers:

@inproceedings{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={Proceedings of the IEEE/CVF international conference on computer vision},
  year={2019}
}
@inproceedings{zhao2021clnet,
  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.},
  year={2021}
}