Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network

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Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network
Title:
Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network
Journal Title:
IEEE Transactions on Circuits and Systems for Video Technology
Publication Date:
24 March 2021
Citation:
Zhao, X., Liu, J., Wu, X., Chen, W., Guo, F., & Li, Z. (2021). Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. doi:10.1109/tcsvt.2021.3068761
Abstract:
Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sight of other available sensors such as inertial measurement unit (IMU) in the above applications. In this paper, we demonstrate that the motion estimation from IMU integration can be used to exploit the spatial distribution prior of keypoints between images. To this end, a probabilistic perspective of attention formulation is proposed to integrate the spatial distribution prior into the attentional graph neural network naturally. With the assistance of spatial distribution prior, the effort of the network for modeling the hidden features can be reduced. Furthermore, we present a projection loss for the proposed keypoints matching network, which gives a smooth edge between matching and un-matching keypoints. Image matching experiments on visual SLAM datasets indicate the effectiveness and efficiency of the presented method.
License type:
Publisher Copyright
Funding Info:
This work was supported by the National Nature Science Foundation of China.
Description:
© 2021 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:
1051-8215
1558-2205
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