  This paper introduces a new algorithm for unsupervised learning of keypoint
detectors and descriptors, which demonstrates fast convergence and good
performance across different datasets. The training procedure uses homographic
transformation of images. The proposed model learns to detect points and
generate descriptors on pairs of transformed images, which are easy for it to
distinguish and repeatedly detect. The trained model follows SuperPoint
architecture for ease of comparison, and demonstrates similar performance on
natural images from HPatches dataset, and better performance on retina images
from Fundus Image Registration Dataset, which contain low number of corner-like
features. For HPatches and other datasets, coverage was also computed to
provide better estimation of model quality.
