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

• Inference code and pretrained models (DS and OT) (2021-4-7)
• Code for reproducing the test-set results (2021-4-7)
• Webcam demo to reproduce the result shown in the GIF above (2021-4-13)
• Training code and training data preparation (expected 2021-6-10)

Discussions about the paper are welcomed in the discussion panel.

🤔 FAQ

1. Undistorted images from D2Net are not available anymore.
   For a temporal alternative, please use the undistorted images provided by the MegaDepth_v1 (should be downloaded along with the required depth files). We numerically compared these images and only found very subtle difference.

🚩 Updates

• Check out QuadTreeAttention, a new attention machanism that improves the efficiency and performance of LoFTR with less demanding GPU requirements for training.
• ✅ Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo

Want to run LoFTR with custom image pairs without configuring your own GPU environment? Try the Colab demo:

LoFTR is integrated into kornia library since version 0.5.11.

pip install kornia

Then you can import it as

from kornia.feature import LoFTR

See tutorial on using LoFTR from kornia here.