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 Python library.
Original Citation
If you use these models in academic work, please cite the original authors:
@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.