Learning to Make Keypoints Sub-Pixel Accurate
<|reference_start|>Learning to Make Keypoints Sub-Pixel Accurate: This work addresses the challenge of sub-pixel accuracy in detecting 2D local features, a cornerstone problem in computer vision. Despite the advancements brought by neural network-based methods like SuperPoint and ALIKED, these modern approaches lag behind classical ones such as SIFT in keypoint localization accuracy due to their lack of sub-pixel precision. We propose a novel network that enhances any detector with sub-pixel precision by learning an offset vector for detected features, thereby eliminating the need for designing specialized sub-pixel accurate detectors. This optimization directly minimizes test-time evaluation metrics like relative pose error. Through extensive testing with both nearest neighbors matching and the recent LightGlue matcher across various real-world datasets, our method consistently outperforms existing methods in accuracy. Moreover, it adds only around 7 ms to the time of a particular detector. The code is available at https://github.com/KimSinjeong/keypt2subpx .<|reference_end|>
@article{kim2024learning,
    title={Learning to Make Keypoints Sub-Pixel Accurate},
    author={Shinjeong Kim, Marc Pollefeys, and Daniel Barath},
    journal={arXiv preprint arXiv:2407.11668},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11668},
    primaryClass={cs.CV}
}