zjn0613/adamatcher

Code for CVPR2023 paper:Adaptive Assignment for Geometry Aware Local Feature Matching

AdaMatcher: Adaptive Assignment for Geometry Aware Local Feature Matching

Paper

Adaptive Assignment for Geometry Aware Local Feature Matching Dihe Huang^*, Ying Chen^*, Yong Liu, Jianlin Liu, Shang Xu, Wenlong Wu, Yikang Ding, Fan Tang, Chengjie Wang CVPR 2023

Installation

For environment and data setup, please refer to LoFTR.

Run AdaMatcher

Download Pretrained model

We have provide pretrained model in megadepth dataset, you can download it from weights.

Download Datasets

You need to setup the testing subsets of ScanNet, MegaDepth and YFCC first from driven.

For the data utilized for training, we use the same training data as LoFTR does.

Megadepth validation

For different scales, you need edit megadepth_test_scale_1000.

# with shell script
bash ./scripts/reproduce_test/outdoor_ada_scale.sh

Reproduce the testing results for yfcc datasets

# with shell script
bash ./scripts/reproduce_test/yfcc100m.sh

Training

We train AdaMatcher on the MegaDepth datasets following LoFTR. And the results can be reproduced when training with 32gpus. Please run the following commands:

sh scripts/reproduce_train/outdoor_ada.sh

Acknowledgement

This repository is developed from LoFTR, and we are grateful to its authors for their implementation.

Citation

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

@article{Huang2023adamatcher,
  title={Adaptive Assignment for Geometry Aware Local Feature Matching},
  author={Dihe Huang, Ying Chen, Yong Liu, Jianlin Liu, Shang Xu, Wenlong Wu, Yikang Ding, Fan Tang, Chengjie Wang},
  journal={{CVPR}},
  year={2023}
}