@inproceedings{
ni2024etoefficient,
title={{ETO}:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses},
author={Junjie Ni and Guofeng Zhang and Guanglin Li and Yijin Li and Xinyang Liu and Zhaoyang Huang and Hujun Bao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3xHCaDdYcc}
}
We tackle the efficiency problem of learning local feature matching.Recent advancements have given rise to purely CNN-based and transformer-based approaches, each augmented with deep learning techniques. While CNN-based methods often excel in matching speed, transformer-based methods tend to provide more accurate matches. We propose an efficient transformer-based network architecture for local feature matching.This technique is built on constructing multiple homography hypotheses to approximate the continuous correspondence in the real world and uni-directional cross-attention to accelerate the refinement. On the YFCC100M dataset, our matching accuracy is competitive with LoFTR, a state-of-the-art transformer-based architecture, while the inference speed is boosted to 4 times, even outperforming the CNN-based methods.Comprehensive evaluations on other open datasets such as Megadepth, ScanNet, and HPatches demonstrate our method's efficacy, highlighting its potential to significantly enhance a wide array of downstream applications.
ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
Junjie Ni Guofeng Zhang Guanglin Li Yijin Li Xinyang Liu Zhaoyang Huang Hujun Bao