Image keypoint descriptions that are discriminative and matchable over large changes in viewpoint are vital for 3D reconstruction. However, descriptions output by learned descriptors are typically not robust to camera rotation. While they can be made more robust by, e.g., data augmentation, this degrades performance on upright images. Another approach is test-time augmentation, which incurs a significant increase in runtime. We instead learn a linear transform in description space that encodes rotations of the input image. We call this linear transform a steerer since it allows us to transform the descriptions as if the image was rotated. From representation theory we know all possible steerers for the rotation group. Steerers can be optimized (A) given a fixed descriptor, (B) jointly with a descriptor or (C) we can optimize a descriptor given a fixed steerer. We perform experiments in all of these three settings and obtain state-of-the-art results on the rotation invariant image matching benchmarks AIMS and Roto-360. We publish code and model weights at github.com/georg-bn/rotation-steerers.

通过学习描述符的线性变换实现对输入图像的旋转编码，从而产生具有大视角变化下鉴别性和匹配性的图像关键点描述符，且在旋转不变图像匹配基准AIMS和Roto-360上取得了最先进的结果。

Steerers：旋转等变关键点描述符的框架