Abstract. A method for learning local affine-covariant regions is presented. We
show that maximizing geometric repeatability does not lead to local regions, a.k.a
features, that are reliably matched and this necessitates descriptor-based learning.
We explore factors that influence such learning and registration: the loss function,
descriptor type, geometric parametrization and the trade-off between matchability
and geometric accuracy and propose a novel hard negative-constant loss function
for learning of affine regions. The affine shape estimator – AffNet – trained with
the hard negative-constant loss outperforms the state-of-the-art in bag-of-words
image retrieval and wide baseline stereo. The proposed training process does not
require precisely geometrically aligned patches. The source codes and trained
weights are available at https://github.com/ducha-aiki/affnet