We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor extractor, we train an end-to-end convolutional neural network (CNN) to find keypoint locations that are more appropriate to the considered descriptor. For that, we apply geometric and photometric warpings to images to generate a supervisory signal, allowing the optimization of the detector. Experiments demonstrate that our method enhances the Mean Matching Accuracy of numerous descriptors when used in conjunction with our detection method, while outperforming the state-of-the-art keypoint detectors on real images of non-rigid objects by 20 p.p. We also apply our method on the complex real-world task of object retrieval where our detector performs on par with the finest keypoint detectors currently available for this task. The source code and trained models are publicly available at https://github.com/verlab/LearningToDetect_PRL_2023

翻译：我们提出一种新颖的学习型关键点检测方法，旨在增加非刚性图像对应任务中的正确匹配数量。通过利用由匹配标注图像对与指定描述子提取器获得的真实对应关系，我们训练一个端到端的卷积神经网络（CNN）来寻找更适用于该描述子的关键点位置。为此，我们对图像应用几何与光度变换以生成监督信号，从而优化检测器。实验表明，我们的方法与多种描述子结合使用时，可提升其平均匹配准确率，并在非刚性物体的真实图像上超越现有最佳关键点检测器达20个百分点。此外，我们将该方法应用于复杂的现实任务——物体检索中，其性能与当前该任务中最佳的关键点检测器相当。源代码与训练模型已在https://github.com/verlab/LearningToDetect_PRL_2023 公开提供。