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.

该研究介绍了 S-TREK，一种新颖的局部特征提取器，它将深度关键点检测器（通过设计具有平移和旋转等价性）与轻量级的深度描述器提取器相结合。我们通过受强化学习启发的框架来训练 S-TREK 关键点检测器，利用顺序操作来最大化与关键点可重复性直接相关的奖励。我们的描述符网络通过 “检测，然后描述” 方法进行训练，在仅对已训练的检测器选择的位置评估描述符损失。多个基准测试的大量实验证实了我们提出方法的有效性，S-TREK 在重复性和恢复位姿质量方面通常优于其他最先进的方法，特别是在处理平面内旋转时。