Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters Revisited
Axel Barroso-Laguna, Krystian Mikolajczyk
Abstract
We introduce a novel approach for keypoint detection 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 and other benchmarks. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance, and complexity. Key.Net implementations in TensorFlow and PyTorch are available online.