  We study the problem of learning to assign a characteristic pose, i.e., scale
and orientation, for an image region of interest. Despite its apparent
simplicity, the problem is non-trivial; it is hard to obtain a large-scale set
of image regions with explicit pose annotations that a model directly learns
from. To tackle the issue, we propose a self-supervised learning framework with
a histogram alignment technique. It generates pairs of image patches by random
rescaling/rotating and then train an estimator to predict their
scale/orientation values so that their relative difference is consistent with
the rescaling/rotating used. The estimator learns to predict a non-parametric
histogram distribution of scale/orientation without any supervision.
Experiments show that it significantly outperforms previous methods in
scale/orientation estimation and also improves image matching and 6 DoF camera
pose estimation by incorporating our patch poses into a matching process.
