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# Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
### [Project Page](https://zju3dv.github.io/efficientloftr) | [Paper](https://zju3dv.github.io/efficientloftr/files/EfficientLoFTR.pdf)
<br/>
> Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
> [Yifan Wang](https://github.com/wyf2020)<sup>\*</sup>, [Xingyi He](https://github.com/hxy-123)<sup>\*</sup>, [Sida Peng](https://pengsida.net), [Dongli Tan](https://github.com/Cuistiano), [Xiaowei Zhou](http://xzhou.me)
> CVPR 2024
https://github.com/zju3dv/EfficientLoFTR/assets/69951260/40890d21-180e-4e70-aeba-219178b0d824
## TODO List
- [x] Inference code and pretrained models
- [x] Code for reproducing the test-set results
- [ ] Add options of flash-attention and torch.compiler for better performance
- [x] jupyter notebook demo for matching a pair of images
- [ ] Training code
## Installation
```shell
conda env create -f environment.yaml
conda activate eloftr
pip install torch==2.0.0+cu118 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```
The test and training can be downloaded by [download link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf?usp=sharing) provided by LoFTR
We provide the our pretrained model in [download link](https://drive.google.com/drive/folders/1GOw6iVqsB-f1vmG6rNmdCcgwfB4VZ7_Q?usp=sharing)
## Reproduce the testing results with pytorch-lightning
You need to setup the testing subsets of ScanNet and MegaDepth first. We create symlinks from the previously downloaded datasets to `data/{{dataset}}/test`.
```shell
# set up symlinks
ln -s /path/to/scannet-1500-testset/* /path/to/EfficientLoFTR/data/scannet/test
ln -s /path/to/megadepth-1500-testset/* /path/to/EfficientLoFTR/data/megadepth/test
```
### Inference time
```shell
conda activate eloftr
bash scripts/reproduce_test/indoor_full_time.sh
bash scripts/reproduce_test/indoor_opt_time.sh
```
### Accuracy
```shell
conda activate eloftr
bash scripts/reproduce_test/outdoor_full_auc.sh
bash scripts/reproduce_test/outdoor_opt_auc.sh
bash scripts/reproduce_test/indoor_full_auc.sh
bash scripts/reproduce_test/indoor_opt_auc.sh
```
## Training
The Training code is coming soon, please stay tuned!
## Citation
If you find this code useful for your research, please use the following BibTeX entry.
```bibtex
@inproceedings{wang2024eloftr,
title={{Efficient LoFTR}: Semi-Dense Local Feature Matching with Sparse-Like Speed},
author={Wang, Yifan and He, Xingyi and Peng, Sida and Tan, Dongli and Zhou, Xiaowei},
booktitle={CVPR},
year={2024}
}
```
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