| --- |
| license: apache-2.0 |
| tags: |
| - computer-vision |
| - feature-matching |
| - onnx |
| - efficient-loftr |
| pipeline_tag: image-feature-extraction |
| --- |
| |
| # EfficientLoFTR ONNX Weights |
|
|
| This repository contains the ONNX-optimized weights for **EfficientLoFTR**, a model used for finding matching points between pairs of images. |
|
|
| By converting the original PyTorch model weights into the ONNX format, these files allow you to run fast feature-matching inference on both CPU and GPU without needing to install the heavy PyTorch framework. |
|
|
| ## Available Files |
| * **`eloftr_outdoor_full.onnx`**: The standard version of the model, optimized for the best matching quality. |
| * **`eloftr_outdoor_opt.onnx`**: An efficiency-focused version of the model, optimized for faster inference speed. |
|
|
| --- |
|
|
| ## How to Use |
|
|
| The easiest way to load and use these files is through the **[spatialhub](https://github.com/pankajkaushik12/spatialhub)** Python library. |
|
|
|
|
| ## Original Citation |
| If you use these models in academic work, please cite the original authors: |
| ```bibtex |
| @inproceedings{wang2022efficientloftr, |
| title={EfficientLoFTR: Semi-Dense Local Feature Matching with Sparse Transformers}, |
| author={Wang, Yanzhao and Geng, Yuwei and Jiang, Zheng and Zhao, Yihong and Jin, Shisheng and Lin, Siyu and Han, Feng}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2022} |
| } |
| ``` |
|
|
| **Note**: This repository provides pre-converted weights for inference purposes. |
