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.

翻译：从图像中检测稳健的基点是许多计算机视觉问题的一个组成部分,关键点的特征方向和规模在关键点描述和匹配中起着重要作用。现有的基于学习的基点检测方法依赖于标准的翻译-等效有线电视,但往往无法检测到可靠的基点以对抗几何差异。要学会检测稳健方向的基点,我们采用自监督的学习框架,使用旋转-等效有线电视新闻网。我们提议通过合成转换生成的图像配对,为培训基于直方图的定向地图提供密集的定向调整损失。我们的方法在图像匹配基准和相机设定估计基准方面优于以往的方法。