  Detecting robust keypoints from an image is an integral part of many computer
vision problems, and the characteristic orientation and scale of keypoints play
an important role for keypoint description and matching. Existing
learning-based methods for keypoint detection rely on standard
translation-equivariant CNNs but often fail to detect reliable keypoints
against geometric variations. To learn to detect robust oriented keypoints, we
introduce a self-supervised learning framework using rotation-equivariant CNNs.
We propose a dense orientation alignment loss by an image pair generated by
synthetic transformations for training a histogram-based orientation map. Our
method outperforms the previous methods on an image matching benchmark and a
camera pose estimation benchmark.
