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

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...

Full description

Bibliographic Details
Main Authors: Styliani Verykokou, Argyro-Maria Boutsi, Charalabos Ioannidis
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/18/8750
id doaj-6b8bb86ed6164f75abd4cdcfef3d57c2
record_format Article
spelling doaj-6b8bb86ed6164f75abd4cdcfef3d57c22021-09-25T23:42:23ZengMDPI AGApplied Sciences2076-34172021-09-01118750875010.3390/app11188750Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data RenderingStyliani Verykokou0Argyro-Maria Boutsi1Charalabos Ioannidis2Laboratory of Photogrammetry, Zografou Campus, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, GreeceLaboratory of Photogrammetry, Zografou Campus, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, GreeceLaboratory of Photogrammetry, Zografou Campus, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, GreeceMobile 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.https://www.mdpi.com/2076-3417/11/18/8750mobile augmented realitypattern recognitionvertex-based renderinggeometric instancingcamera pose estimation3D rendering
collection DOAJ
language English
format Article
sources DOAJ
author Styliani Verykokou
Argyro-Maria Boutsi
Charalabos Ioannidis
spellingShingle Styliani Verykokou
Argyro-Maria Boutsi
Charalabos Ioannidis
Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering
Applied Sciences
mobile augmented reality
pattern recognition
vertex-based rendering
geometric instancing
camera pose estimation
3D rendering
author_facet Styliani Verykokou
Argyro-Maria Boutsi
Charalabos Ioannidis
author_sort Styliani Verykokou
title Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering
title_short Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering
title_full Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering
title_fullStr Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering
title_full_unstemmed Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering
title_sort mobile augmented reality for low-end devices based on planar surface recognition and optimized vertex data rendering
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-09-01
description 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.
topic mobile augmented reality
pattern recognition
vertex-based rendering
geometric instancing
camera pose estimation
3D rendering
url https://www.mdpi.com/2076-3417/11/18/8750
work_keys_str_mv AT stylianiverykokou mobileaugmentedrealityforlowenddevicesbasedonplanarsurfacerecognitionandoptimizedvertexdatarendering
AT argyromariaboutsi mobileaugmentedrealityforlowenddevicesbasedonplanarsurfacerecognitionandoptimizedvertexdatarendering
AT charalabosioannidis mobileaugmentedrealityforlowenddevicesbasedonplanarsurfacerecognitionandoptimizedvertexdatarendering
_version_ 1717368190910070784