PTCNet

PyTorch implementation of Paper "PTCNet: Pure Transformer Network for Two-View Correspondence Pruning"

Part of the code is borrowed or ported from OANet and CLNet. Please also cite these works if you find the corresponding code useful.

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.

Download the YFCC100M dataset and the SUN3D dataset from the OANet repository.

The results in our paper can be reproduced by running the test script:

cd core 
python main.py --run_mode=test --model_path=../model-yfcc-sift/

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 core 
python main.py

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