Awesome
Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
Project Page | Paper
<br/>Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
Yifan Wang<sup>*</sup>, Xingyi He<sup>*</sup>, Sida Peng, Dongli Tan, Xiaowei Zhou
CVPR 2024
https://github.com/zju3dv/EfficientLoFTR/assets/69951260/40890d21-180e-4e70-aeba-219178b0d824
TODO List
- Inference code and pretrained models
- Code for reproducing the test-set results
- Add options of flash-attention and torch.compiler for better performance
- jupyter notebook demo for matching a pair of images
- Training code
Installation
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 provided by LoFTR
We provide the our pretrained model in download link
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
.
# 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
conda activate eloftr
bash scripts/reproduce_test/indoor_full_time.sh
bash scripts/reproduce_test/indoor_opt_time.sh
Accuracy
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
conda env create -f environment_training.yaml # used a different version of pytorch, maybe slightly different from the inference environment
pip install -r requirements.txt
conda activate eloftr_training
bash scripts/reproduce_train/eloftr_outdoor.sh eloftr_outdoor
Citation
If you find this code useful for your research, please use the following BibTeX entry.
@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}
}