  We introduce a novel approach for keypoint detection task that combines
handcrafted and learned CNN filters within a shallow multi-scale architecture.
Handcrafted filters provide anchor structures for learned filters, which
localize, score and rank repeatable features. Scale-space representation is
used within the network to extract keypoints at different levels. We design a
loss function to detect robust features that exist across a range of scales and
to maximize the repeatability score. Our Key.Net model is trained on data
synthetically created from ImageNet and evaluated on HPatches benchmark.
Results show that our approach outperforms state-of-the-art detectors in terms
of repeatability, matching performance and complexity.
