SuperPoint: Self-Supervised Interest Point Detection and Description. This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Ma, Jiayi; Jiang, Xingyu; Fan, Aoxiang; Jiang, Junjun; Yan, Junchi: Image matching from handcrafted to deep features: a survey (2021)
- Qin, Zixuan; Yin, Mengxiao; Li, Guiqing; Yang, Feng: SP-Flow: self-supervised optical flow correspondence point prediction for real-time SLAM (2020)
- Zhang, Weichuan; Sun, Changming: Corner detection using multi-directional structure tensor with multiple scales (2020)
- Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, Krystian Mikolajczyk: Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters (2019) arXiv