Code-of-GANet

code for "Learning for mismatch removal via graph attention networks"

Authors: Xingyu Jiang, Yang Wang, Aoxiang Fan and Jiayi Ma

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.

Run the code

Test pretrained model

We provide the model trained on YFCC100M and SUN3D described in our paper. Run the test script to get results in our paper.

bash test.sh

Test model on your own data-- see './Data'

python main4OwnData.py --model_path="./log/main.py/test/" 

Test or train model on public YFCC100M and SUN3D dataset

The HDF5 file is provide by the repo zjhthu/OANet, Please follow their way to generate the training/valid/testing set. After generating dataset for YFCC100M/SUN3D, run the following

Test model on YFCC100M

python main.py --use_ransac=False --data_te='/data/yfcc-sift-2000-test.hdf5' --run_mode='test'

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

Test model on SUN3D

python main.py --use_ransac=False --data_te='/data/sun3d-sift-2000-test.hdf5' --run_mode='test'

Train model on YFCC100M

python main.py --run_mode= 'train'

You can train the fundamental estimation model by setting --use_fundamental=True --geo_loss_margin=0.03 and use side information by setting --use_ratio=2 --use_mutual=2