ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation

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ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
Title:
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
Journal Title:
IEEE Transactions on Instrumentation and Measurement
Publication Date:
28 April 2023
Citation:
Zhao, X., Wu, X., Chen, W., Chen, P. C. Y., Xu, Q., & Li, Z. (2023). ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation. IEEE Transactions on Instrumentation and Measurement, 72, 1–16. https://doi.org/10.1109/tim.2023.3271000
Abstract:
Image keypoints and descriptors play a crucial role in many visual measurement tasks. In recent years, deep neural networks have been widely used to improve the performance of keypoint and descriptor extraction. However, the conventional convolution operations do not provide the geometric invariance required for the descriptor. To address this issue, we propose the Sparse Deformable Descriptor Head (SDDH), which learns the deformable positions of supporting features for each keypoint and constructs deformable descriptors. Furthermore, SDDH extracts descriptors at sparse keypoints instead of a dense descriptor map, which enables efficient extraction of descriptors with strong expressiveness. In addition, we relax the neural reprojection error (NRE) loss from dense to sparse to train the extracted sparse descriptors. Experimental results show that the proposed network is both efficient and powerful in various visual measurement tasks, including image matching, 3D reconstruction, and visual relocalization.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Robotics Horizontal Technology Coordinating Office (HTCO)
Grant Reference no. : C221518005

This work was supported by the National Nature Science Foundation of China under Grant No. 61620106012, the Key Research and Development Program of Zhejiang Province under Grant No. 2020C01109
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1557-9662
0018-9456
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