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基于学习的关键点检测方法，旨在提升非刚体图像对应任务中的正确匹配数量。通过利用匹配标注图像对与特定描述符提取器所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），以寻找对于给定描述符更为合适的关键点位置。为此，我们对图像施加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，当我们的检测方法与多种描述符结合使用时，能够提升其平均匹配精度，同时在真实非刚体图像上以20个百分点的优势超越现有最佳的关键点检测器。我们还将该方法应用于物体检索这一复杂的实际任务中，在此任务上，我们的检测器性能与当前可用的最佳关键点检测器相当。源代码与训练模型已公开于 https://github.com/verlab/LearningToDetect_PRL_2023