  Sparse local feature matching is pivotal for many computer vision and
robotics tasks. To improve their invariance to challenging appearance
conditions and viewing angles, and hence their usefulness, existing
learning-based methods have primarily focused on data augmentation-based
training. In this work, we propose an alternative, complementary approach that
centers on inducing bias in the model architecture itself to generate
`rotation-specific' features using Steerable E2-CNNs, that are then
group-pooled to achieve rotation-invariant local features. We demonstrate that
this high performance, rotation-specific coverage from the steerable CNNs can
be expanded to all rotation angles by combining it with augmentation-trained
standard CNNs which have broader coverage but are often inaccurate, thus
creating a state-of-the-art rotation-robust local feature matcher. We benchmark
our proposed methods against existing techniques on HPatches and a newly
proposed UrbanScenes3D-Air dataset for visual place recognition. Furthermore,
we present a detailed analysis of the performance effects of ensembling, robust
estimation, network architecture variations, and the use of rotation priors.
