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 .

翻译：本研究针对计算机视觉领域的一个基础性挑战——实现二维局部特征检测的亚像素级精度。尽管基于神经网络的方法（如SuperPoint和ALIKED）取得了显著进展，但由于缺乏亚像素级定位能力，这些现代方法在关键点定位精度上仍落后于SIFT等经典方法。我们提出了一种新颖的网络架构，通过学习检测特征的偏移向量，可为任意检测器赋予亚像素级精度，从而无需专门设计具有亚像素精度的检测器。该优化方法直接最小化相对位姿误差等测试时评估指标。通过在多个真实数据集上，结合最近邻匹配与最新的LightGlue匹配器进行广泛测试，我们的方法在精度上持续超越现有方法。此外，该方法仅使特定检测器的运行时间增加约7毫秒。代码已开源：https://github.com/KimSinjeong/keypt2subpx。