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。

S-TREK: 顺序平移旋转等变特征点用于局部特征提取

S-TREK: Sequential Translation and Rotation Equivariant Keypoints for  local feature extraction

Dense pixel matching is important for many computer vision tasks such as
disparity and flow estimation. We present a robust, unified descriptor network
that considers a large context region with high spatial variance. Our network
has a very large receptive field and avoids striding layers to maintain spatial
resolution. These properties are achieved by creating a novel neural network
layer that consists of multiple, parallel, stacked dilated convolutions (SDC).
Several of these layers are combined to form our SDC descriptor network. In our
experiments, we show that our SDC features outperform state-of-the-art feature
descriptors in terms of accuracy and robustness. In addition, we demonstrate
the superior performance of SDC in state-of-the-art stereo matching, optical
flow and scene flow algorithms on several famous public benchmarks.

该论文通过创建一种多个平行堆叠 (即分离卷积核) 的卷积神经网络层，在充分考虑空间变异的大范围内容下避免了分层，并达到很大感受野，创新性地构建了一个鲁棒、统一的描述符网络，从而在密集像素匹配方面优于现有的最先进的特征描述符，表现出更高的准确性和鲁棒性，并在多个著名的公共基准测试中在立体匹配、光流和场景流算法方面展现了卓越的性能。