  Interest point detection and local feature description are fundamental steps
in many computer vision applications. Classical methods for these tasks are
based on a detect-then-describe paradigm where separate handcrafted methods are
used to first identify repeatable keypoints and then represent them with a
local descriptor. Neural networks trained with metric learning losses have
recently caught up with these techniques, focusing on learning repeatable
saliency maps for keypoint detection and learning descriptors at the detected
keypoint locations. In this work, we argue that salient regions are not
necessarily discriminative, and therefore can harm the performance of the
description. Furthermore, we claim that descriptors should be learned only in
regions for which matching can be performed with high confidence. We thus
propose to jointly learn keypoint detection and description together with a
predictor of the local descriptor discriminativeness. This allows us to avoid
ambiguous areas and leads to reliable keypoint detections and descriptions. Our
detection-and-description approach, trained with self-supervision, can
simultaneously output sparse, repeatable and reliable keypoints that
outperforms state-of-the-art detectors and descriptors on the HPatches dataset.
It also establishes a record on the recently released Aachen Day-Night
localization dataset.
