Open Access Paper
12 November 2024 Interactable mobile computing framework for augmented reality glasses
Heming Zhang, Ran Lee, Jun Wu, Kai Yan, Yu Pang
Author Affiliations +
Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 1339508 (2024) https://doi.org/10.1117/12.3049317
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
Augmented Reality (AR) enhances user interaction with digital content through real-world overlays, finding applications across various fields. AR glasses, serving as an excellent AR platform, have been developed in both optical see-through and video see-through types. Professional video see-through devices and advanced optical see-through devices with vision systems can perform environment recognition and hand detection but are often bulky and heavy for prolonged wear. Conversely, lightweight optical see-through AR glasses, which lack embedded systems and have limited sensors, serve primarily as displays. While they offer the advantage of reduced weight, they lack advanced interaction capabilities. In this research, we utilize an Android mobile phone as the computing unit and present an interactable framework for AR glasses with limited sensors. This framework supports head motion estimation, hand gesture detection and tracking, providing a robust AR experience without the need for high-end hardware. It has been tested on lightweight optical see-through AR glasses only equipped with an Inertial Measurement Unit (IMU) and single camera. Our solution offers a cost-effective and portable approach, enhancing data visualization and virtual object operation.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Heming Zhang, Ran Lee, Jun Wu, Kai Yan, and Yu Pang "Interactable mobile computing framework for augmented reality glasses", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 1339508 (12 November 2024); https://doi.org/10.1117/12.3049317
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KEYWORDS
Augmented reality

Autoregressive models

Glasses

Cameras

Head

Motion estimation

Education and training

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