Learning to Make Keypoints Sub-Pixel Accurate

• URL: http://arxiv.org/abs/2407.11668v1
• Date: Tue, 16 Jul 2024 12:39:56 GMT
• Title: Learning to Make Keypoints Sub-Pixel Accurate
• Authors: Shinjeong Kim, Marc Pollefeys, Daniel Barath,
• Abstract summary: This work addresses the challenge of sub-pixel accuracy in detecting 2D local features. We propose a novel network that enhances any detector with sub-pixel precision by learning an offset vector for detected features.
• Score: 80.55676599677824
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 .

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