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

Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters