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.

本文提出了一种使用旋转卷积神经网络的自监督学习框架，通过生成的图像对和直方图来训练密集方向对准损失，从而学习检测具有鲁棒性的定向关键点，并在图像匹配和相机姿态估计基准测试中表现出优异的性能。

自监督等变学习用于定向关键点检测

Self-Supervised Equivariant Learning for Oriented Keypoint Detection

Cross-view geo-localization is the problem of estimating the position and
orientation (latitude, longitude and azimuth angle) of a camera at ground level
given a large-scale database of geo-tagged aerial (e.g., satellite) images.
Existing approaches treat the task as a pure location estimation problem by
learning discriminative feature descriptors, but neglect orientation alignment.
It is well-recognized that knowing the orientation between ground and aerial
images can significantly reduce matching ambiguity between these two views,
especially when the ground-level images have a limited Field of View (FoV)
instead of a full field-of-view panorama. Therefore, we design a Dynamic
Similarity Matching network to estimate cross-view orientation alignment during
localization. In particular, we address the cross-view domain gap by applying a
polar transform to the aerial images to approximately align the images up to an
unknown azimuth angle. Then, a two-stream convolutional network is used to
learn deep features from the ground and polar-transformed aerial images.
Finally, we obtain the orientation by computing the correlation between
cross-view features, which also provides a more accurate measure of feature
similarity, improving location recall. Experiments on standard datasets
demonstrate that our method significantly improves state-of-the-art
performance. Remarkably, we improve the top-1 location recall rate on the CVUSA
dataset by a factor of 1.5x for panoramas with known orientation, by a factor
of 3.3x for panoramas with unknown orientation, and by a factor of 6x for
180-degree FoV images with unknown orientation.

本文提出了一种动态相似度匹配网络，通过使用极坐标变换来大致对齐空中图像，学习地面和变换后空中图像的深度特征，并通过计算跨视图特征之间的相关性来获得定位时的方向，从而提高了十字视图地理定位准确性。