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Learning of Low-Level Feature Keypoints for Accurate and Robust Detection
Joint learning of feature descriptor and detector has offered promising 3D reconstruction results; however, they often lack the low-level feature awareness, which causes low accuracy in matched keypoint locations. The others employed fixed operations to select the keypoints, but the selected keypoints may not correspond to the descriptor matching. To address these problems, we propose the supervised learning of keypoint detection with low-level features. Our detector is a single CNN layer extended from the descriptor backbone, which can be jointly learned with the descriptor for maximizing the descriptor matching. This results in a state-of-the-art 3D reconstruction, especially on improving reprojection error, and the highest accuracy in keypoint detection and matching on benchmark datasets. We also present a dedicated study on evaluation metrics to measure the accuracy of keypoint detection and matching.