PATS: Patch Area Transportation with Subdivision for Local Feature Matching

Project Page | Paper

PATS: Patch Area Transportation with Subdivision for Local Feature Matching
Junjie Ni* Yijin Li*, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, Guofeng Zhang
CVPR 2023

TODO List

• Training script

Download Link

We provide the download link to

• Pretrained models trained on MegaDepth and ScanNet, which are labeled as outdoor and indoor, respectively.
• MegaDepth pairs and scenes (placed in a folder named megadepth_parameters).
• The demo data, which is a sequence of images captured from near to far.

Run PATS

Installation

conda env create -f environment.yaml
cd setup
python setup.py install
cd ..

Prepare the data and pretrained model

Download from the above link, and place the data and model weights as below:

pats
├── data
│   ├── MegaDepth_v1
│   ├── megadepth_parameters 
│   ├── ScanNet
│   ├── yfcc100M
│   └── demo
└── weights
    ├── indoor_coarse.pt
    ├── indoor_fine.pt
    ├── indoor_third.pt
    ├── outdoor_coarse.pt
    ├── outdoor_fine.pt
    └── outdoor_third.pt

Evaluate on MegaDepth/YFCC/ScanNet dataset

python evaluate.py configs/test_megadepth.yaml
python evaluate.py configs/test_yfcc.yaml
python evaluate.py configs/test_scannet.yaml

Run the demo

python demo.py configs/test_demo.yaml

Citation

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

@inproceedings{pats2023,
  title={PATS: Patch Area Transportation with Subdivision for Local Feature Matching},
  author={Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, Guofeng Zhang},
  booktitle={The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
  year={2023}
}

Acknowledgements

We would like to thank the authors of SuperGlue and LoFTR for open-sourcing their projects.