  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 .
