GabbySuwichaya / lgl-feature-matching

Lifelong Graph Learning (CVPR 2022) [Feature Matching]

Feature matching with FGN

This repo contains the source code for the feature matching application (Sec. 7) in "Lifelong Graph Learning." Chen Wang, Yuheng Qiu, Dasong Gao, Sebastian Scherer. CVPR 2022.

Usage

Dependencies

• Python >= 3.5
• PyTorch >= 1.7
• OpenCV >= 3.4
• NumPy
• TensorBoard
• Matplotlib
• ArgParse
• tqdm

Data

The TartanAir dataset is required for both training and testing. The dataset should be aranged as follows:

$DATASET_ROOT/
└── tartanair/
    ├── abandonedfactory_night/
    └── ...

Commandline

Training and evaluates the method with default setting:

$ python train.py --data-root <DATASET_ROOT> --method <FGN/GAT>

• --method option is used to switch between FGN-based (ours) and GAT-based (SuperGlue) graph matcher
• Considering the gigantic volume of TartanAir, evaluation will happen every 5000 training steps by default (can be overriden by --eval-freq). Results will be logged to the console.
• If --log-dir is specified, TensorBoard will be activated to show visualization and evaluation results instead (under "TEXT" tab).
• Detailed description of settings can be viewed by $ python train.py -h.

Citation

@inproceedings{wang2022lifelong,
  title={Lifelong graph learning},
  author={Wang, Chen and Qiu, Yuheng and Gao, Dasong and Scherer, Sebastian},
  booktitle={2022 Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}