Abstract
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.
Highlights
Augmented Reality (AR) technology is an information-delivery paradigm that bridges physical and digital objects intuitively and seamlessly
The results obtained by profiling CPU usage, timing, frame rate and memory allocation separately on the native side during pattern recognition and on the Java side for the 3D graphics engine are reported and discussed
The size of the pattern images used in the experiments is within the range 0.07–0.25 MP, while the size of the camera frames in the test devices is 0.92 MP for the low-end device (720 × 1280 pixels) and 2.07 MP for the mid-range device (1080 × 1920 pixels), both corresponding to a frame rate of 30 FPS
Summary
Augmented Reality (AR) technology is an information-delivery paradigm that bridges physical and digital objects intuitively and seamlessly. Combined with adaptive data transmissions MEC technology, reduces the end-to-end latency while multiple users share AR and Mixed Reality (MR) content through the ShareAR application [6] Such multiuser collaboration interfaces may be the response for open museums that seek a balance between interactivity and distance through AR technology [7]. InvercARgill is a MAR urban tour guide that retrieves on the fly location-based information and navigates the user to cultural attractions and heritage activities [8] Both applications leverage commercial AR Software Development Kits (SDKs) for pose estimation, tracking and rendering, i.e., ARCore [9] and Wikitude [10], respectively. The current advances in photogrammetry, range sensors and fast surface modelling enrich the CH datasets with high-resolution, geometrically accurate and realistic 3D reconstructions Their large size and complex structure dictate their integration into MAR applications, where visual fidelity is encountered as a crucial factor.
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