S-TREK: Sequential Translation and Rotation Equivariant Keypoints for
  local feature extraction
Emanuele Santellani Christian Sormann Mattia Rossi Andreas Kuhn Friedrich Fraundorfer
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.