INTERFRAME CODING OF CANONICAL PATCHES FOR LOW BIT-RATE MOBILE AUGMENTED REALITY

2013 ◽  
Vol 07 (01) ◽  
pp. 5-24 ◽  
Author(s):  
MINA MAKAR ◽  
SAM S. TSAI ◽  
VIJAY CHANDRASEKHAR ◽  
DAVID CHEN ◽  
BERND GIROD

Local features are widely used for content-based image retrieval and augmented reality applications. Typically, feature descriptors are calculated from the gradients of a canonical patch around a repeatable keypoint in the image. In this paper, we propose a temporally coherent keypoint detector and design efficient interframe predictive coding techniques for canonical patches and keypoint locations. In the proposed system, we strive to transmit each patch with as few bits as possible by simply modifying a previously transmitted patch. This enables server-based mobile augmented reality where a continuous stream of salient information, sufficient for image-based retrieval and localization, can be sent over a wireless link at a low bit-rate. Experimental results show that our technique achieves a similar image matching performance at 1/15 of the bit-rate when compared to detecting keypoints independently frame-by-frame and allows performing streaming mobile augmented reality at low bit-rates of about 20–50 kbps, practical for today's wireless links.

2014 ◽  
Vol 23 (8) ◽  
pp. 3352-3367 ◽  
Author(s):  
Mina Makar ◽  
Vijay Chandrasekhar ◽  
Sam S. Tsai ◽  
David Chen ◽  
Bernd Girod

2021 ◽  
Vol 11 (18) ◽  
pp. 8750
Author(s):  
Styliani Verykokou ◽  
Argyro-Maria Boutsi ◽  
Charalabos Ioannidis

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.


Author(s):  
Mina Makar ◽  
Sam S. Tsai ◽  
Vijay Chandrasekhar ◽  
David Chen ◽  
Bernd Girod

2017 ◽  
Vol 51 ◽  
pp. 40-49
Author(s):  
Sai Manoj Prakhya ◽  
Weisi Lin ◽  
Vijay Chandrasekhar ◽  
Bingbing Liu ◽  
Jie Lin

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