LGFCTR

Code for "LGFCTR: Local and Global Feature Convolutional Transformer for Image Matching"

Data preparation

MegaDepth dataset

Since the preprocessed undistorted MegaDepth dataset provided in D2-Net has been not available, we use the original MegaDepth dataset

MegaDepth indices

Download and unzip MegaDepth indices following LoFTR

Build dataset symlinks

Symlink the datasets and indices to the data directory following LoFTR

Pretrained model

We provide the outdoor weights of LGFCTR in the Google Drive .

Requirements

Following LoFTR

Please follow LoFTR to prepare the environment.

Pip by yourselves

In addition, you can also install the requirements by yourselves. More specifically, we use pytorch==1.9.1+cu111, pytorch-lightning==1.3.5, opencv-python==4.5.5.64, torchmetrics==0.6.0 and kornia==0.6.11. Other requirements can be installed easily by pip.

Pip with our enviornment.yaml

conda env create -f environment.yaml
conda activate lgfctr

Reproduce

Training

You can reproduce the training by

sh scripts/reproduce_train/outdoor_ds.sh

Evaluation

You can reproduce the evaluation on MegaDepth dataset by

sh scripts/reproduce_test/outdoor_ds.sh

Demos

Visualize a single pair of images

We provide a demo for visualizing a single pair of images. You can specify img_path0 and img_path1 for your images, save_dir for your save directory, topk for the number of matches shown, img_resize for resized longer dimension, and is_original for outputing the original images.

cd vis
python vis_single_pair.py --img_path0 your_img_path0 --img_path1 your_img_path1 --save_dir your_save_dir --topk 1000 --img_resize 640 --is_original True

Visualize multi-scale attention weights

We provide a demo for visualizing multi-scale attention weights of a single pair of images. Besides arguments mentioned above, you can specify dpi for the dpi of outputs, and change the Line 41 to specify which index of resolutions and CTR for visualizations.

python vis_attention.py

Acknowledgements

This repository was developed from LoFTR, and we are grateful for their implementations.

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@article{zhong2023lgfctr,
  title={LGFCTR: Local and Global Feature Convolutional Transformer for Image Matching},
  author={Zhong, Wenhao and Jiang, Jie},
  journal={arXiv preprint arXiv:2311.17571},
  year={2023}
}