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
- 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.