(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

Related tags

Deep LearningCLNet
Overview

CLNet

(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

Citing CLNet

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

@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.},
  year={2021}
}

Setup

Please start by installing the required libraries:

pip install -r requirements.txt

Data Processing

The code of this part is partially borrowed from [OANet]. 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 can be generated by:

cd dump_match
bash yfcc.sh
bash sun3d.sh

The initial matches are generated over SIFT by default. The ones based on ORB and SuperPoint are also available by changing the settings of --suffix and --desc_name.

Pretrained Model

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

python ./test.py --use_ransac True --data_te ./data_dump/yfcc-sift-2000-test.hdf5 --output_dir ./logs/CLNet_yfcc_sift --model_path ./pretrained_models/clnet_yfcc_sift.pth

Train model on YFCC100M

Please run the training script to train our model on YFCC100M after the data processing is done.

python ./train.py --data_tr ./data_dump/yfcc-sift-2000-train.hdf5
--data_te ./data_dump/yfcc-sift-2000-test.hdf5
Owner
Chen Zhao
https://sailor-z.github.io/
Chen Zhao
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