In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a lightweight deep descriptor extractor. We train the S-TREK keypoint detector within a framework inspired by reinforcement learning, where we leverage a sequential procedure to maximize a reward directly related to keypoint repeatability. Our descriptor network is trained following a "detect, then describe" approach, where the descriptor loss is evaluated only at those locations where keypoints have been selected by the already trained detector. Extensive experiments on multiple benchmarks confirm the effectiveness of our proposed method, with S-TREK often outperforming other state-of-the-art methods in terms of repeatability and quality of the recovered poses, especially when dealing with in-plane rotations.

翻译：本文提出S-TREK——一种新型局部特征提取器，它结合了通过设计实现平移与旋转双重等变性的深度关键点检测器与轻量级深度描述子提取器。受强化学习启发，我们在框架内训练S-TREK关键点检测器，通过利用序列化过程最大化与关键点重复率直接相关的奖励函数。描述子网络采用“检测-描述”范式训练，仅在已训练检测器选取的关键点位置评估描述子损失。多项基准上的大量实验证实了所提方法的有效性，S-TREK在关键点重复率和恢复位姿质量方面（尤其是处理平面内旋转时）常优于其他最先进方法。