Self-Supervised Equivariant Learning for Oriented Keypoint Detection

• URL: http://arxiv.org/abs/2204.08613v1
• Date: Tue, 19 Apr 2022 02:26:07 GMT
• Title: Self-Supervised Equivariant Learning for Oriented Keypoint Detection
• Authors: Jongmin Lee, Byungjin Kim, Minsu Cho
• Abstract summary: We introduce a self-supervised learning framework using rotation-equivariant CNNs to learn to detect robust oriented keypoints. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark.
• Score: 35.94215211409985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but often fail to detect reliable keypoints against geometric variations. To learn to detect robust oriented keypoints, we introduce a self-supervised learning framework using rotation-equivariant CNNs. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark.

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