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  4. DPDM: Feature-Based Pose Refinement with Deep Pose and Deep Match for Monocular Visual Odometry
 
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DPDM: Feature-Based Pose Refinement with Deep Pose and Deep Match for Monocular Visual Odometry

Journal
Proceedings - International Conference on Image Processing, ICIP
ISBN
9781728198354
Date Issued
2023-01-01
Author(s)
Huang, Li Yang
Huang, Shao Syuan
SHAO-YI CHIEN  
DOI
10.1109/ICIP49359.2023.10221966
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/638642
URL
https://api.elsevier.com/content/abstract/scopus_id/85180755016
Abstract
In recent years, the metaverse has been a popular topic, and it drives many consumer electronics like AR/VR HMDs (Head Mounted Displays) and smart glasses. In these mobile devices, a critical technology is visual odometry (VO), which provides on-device motion tracking so that the user can interact with and move freely in the virtual information. In this work, we propose a novel hybrid monocular visual odometry framework named DPDM (Deep Pose and Deep Match), which properly integrates deep learning into geometry-based methods. We revisit the traditional feature-based optimization and improve it by replacing its crucial components with deep prediction. With the powerful high-level information extraction ability of deep neural networks, DPDM can obtain robust and accurate results through a simple frame-to-frame sparse feature-based pose refinement module. Experiments show that DPDM can outperform traditional VO and pure learning-based VO. Compared to state-of-the-art hybrid VO, DPDM can achieve competitive performance and higher FPS (Frames Per Second).
Subjects
augmented reality | computer vision | deep learning | mobile devices | visual odometry
Type
conference paper

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