  The performance of local feature descriptors degrades in the presence of
large rotation variations. To address this issue, we present an efficient
approach to learning rotation invariant descriptors. Specifically, we propose
Rotated Kernel Fusion (RKF) which imposes rotations on the convolution kernel
to improve the inherent nature of CNN. Since RKF can be processed by the
subsequent re-parameterization, no extra computational costs will be introduced
in the inference stage. Moreover, we present Multi-oriented Feature Aggregation
(MOFA) which aggregates features extracted from multiple rotated versions of
the input image and can provide auxiliary knowledge for the training of RKF by
leveraging the distillation strategy. We refer to the distilled RKF model as
DRKF. Besides the evaluation on a rotation-augmented version of the public
dataset HPatches, we also contribute a new dataset named DiverseBEV which is
collected during the drone's flight and consists of bird's eye view images with
large viewpoint changes and camera rotations. Extensive experiments show that
our method can outperform other state-of-the-art techniques when exposed to
large rotation variations.
