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Article

Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering

Laboratory of Photogrammetry, Zografou Campus, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, Greece
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Academic Editor: Fotis Liarokapis
Appl. Sci. 2021, 11(18), 8750; https://doi.org/10.3390/app11188750
Received: 23 July 2021 / Revised: 2 September 2021 / Accepted: 17 September 2021 / Published: 20 September 2021
(This article belongs to the Special Issue Extended Reality: From Theory to Applications)
Mobile Augmented Reality (MAR) is designed to keep pace with high-end mobile computing and their powerful sensors. This evolution excludes users with low-end devices and network constraints. This article presents ModAR, a hybrid Android prototype that expands the MAR experience to the aforementioned target group. It combines feature-based image matching and pose estimation with fast rendering of 3D textured models. Planar objects of the real environment are used as pattern images for overlaying users’ meshes or the app’s default ones. Since ModAR is based on the OpenCV C++ library at Android NDK and OpenGL ES 2.0 graphics API, there are no dependencies on additional software, operating system version or model-specific hardware. The developed 3D graphics engine implements optimized vertex-data rendering with a combination of data grouping, synchronization, sub-texture compression and instancing for limited CPU/GPU resources and a single-threaded approach. It achieves up to 3× speed-up compared to standard index rendering, and AR overlay of a 50 K vertices 3D model in less than 30 s. Several deployment scenarios on pose estimation demonstrate that the oriented FAST detector with an upper threshold of features per frame combined with the ORB descriptor yield best results in terms of robustness and efficiency, achieving a 90% reduction of image matching time compared to the time required by the AGAST detector and the BRISK descriptor, corresponding to pattern recognition accuracy of above 90% for a wide range of scale changes, regardless of any in-plane rotations and partial occlusions of the pattern. View Full-Text
Keywords: mobile augmented reality; pattern recognition; vertex-based rendering; geometric instancing; camera pose estimation; 3D rendering; ORB; BRISK; OpenCV; OpenGL ES mobile augmented reality; pattern recognition; vertex-based rendering; geometric instancing; camera pose estimation; 3D rendering; ORB; BRISK; OpenCV; OpenGL ES
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MDPI and ACS Style

Verykokou, S.; Boutsi, A.-M.; Ioannidis, C. Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering. Appl. Sci. 2021, 11, 8750. https://doi.org/10.3390/app11188750

AMA Style

Verykokou S, Boutsi A-M, Ioannidis C. Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering. Applied Sciences. 2021; 11(18):8750. https://doi.org/10.3390/app11188750

Chicago/Turabian Style

Verykokou, Styliani, Argyro-Maria Boutsi, and Charalabos Ioannidis. 2021. "Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering" Applied Sciences 11, no. 18: 8750. https://doi.org/10.3390/app11188750

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