LoFTR: Detector-Free Local Feature Matching with Transformers

Project Page | Paper

LoFTR: Detector-Free Local Feature Matching with Transformers
Jiaming Sun^*, Zehong Shen^*, Yu'ang Wang^*, Hujun Bao, Xiaowei Zhou
CVPR 2021

Installation

# For full pytorch-lightning trainer features
conda env create -f environment.yaml
conda activate loftr

# For the LoFTR matcher only
pip install torch einops yacs kornia

We provide the download link to

• the scannet-1500-testset (~1GB).
• the megadepth-1500-testset (~600MB).
• 4 pretrained models of indoor-ds, indoor-ot, outdoor-ds and outdoor-ot (each ~45MB).

By now, the LoFTR-DS model is ready to go!

[Requirements for LoFTR-OT]

We use the code from SuperGluePretrainedNetwork for optimal transport. However, we can't provide the code directly due to its LICENSE. We recommend downloading it instead.

cd src/loftr/utils  
wget https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/superglue.py 

Run the code

Match image pairs with LoFTR

[code snippets]

from src.loftr import LoFTR, default_cfg

# Initialize LoFTR
matcher = LoFTR(config=default_cfg)
matcher.load_state_dict(torch.load("weights/indoor_ds.ckpt")['state_dict'])
matcher = matcher.eval().cuda()

# Inference
with torch.no_grad():
    matcher(batch)    # batch = {'image0': img0, 'image1': img1}
    mkpts0 = batch['mkpts0_f'].cpu().numpy()
    mkpts1 = batch['mkpts1_f'].cpu().numpy()

An example is in the notebooks/demo_single_pair.ipynb.

Reproduce the testing results with pytorch-lightning

conda activate loftr
# with shell script
bash ./scripts/reproduce_test/indoor_ds.sh

# or
python test.py configs/data/scannet_test_1500.py configs/loftr/loftr_ds.py --ckpt_path weights/indoor_ds.ckpt --profiler_name inference --gpus=1 --accelerator="ddp"

For visualizing the dump results, please refer to notebooks/visualize_dump_results.ipynb.

Reproduce the training phase with pytorch-lightning

The code is coming soon, stay tuned!

Code release ETA

The entire codebase for data pre-processing, training and validation is under major refactoring and will be released around June. Please subscribe to this discussion thread if you wish to be notified of the code release. In the meanwhile, discussions about the paper are welcomed in the discussion panel.

Citation

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

@article{sun2021loftr,
  title={{LoFTR}: Detector-Free Local Feature Matching with Transformers},
  author={Sun, Jiaming and Shen, Zehong and Wang, Yuang and Bao, Hujun and Zhou, Xiaowei},
  journal={CVPR},
  year={2021}
}

Copyright

This work is affiliated with ZJU-SenseTime Joint Lab of 3D Vision, and its intellectual property belongs to SenseTime Group Ltd.

Copyright SenseTime. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.