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Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

Xin Yang, Kwang-Ting Cheng

IEEE Transactions on Visualization and Computer Graphics

|September 11, 2015

Summary

This summary is machine-generated.

A new feature descriptor, Learning-based Local Difference Binary (LLDB), offers efficient and robust performance for mobile augmented reality (AR) systems. LLDB improves matching accuracy and speed on large datasets, overcoming limitations of current AR technology.

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Area of Science:

Computer Vision
Mobile Computing
Augmented Reality

Background:

Mobile augmented reality (AR) systems require efficient, robust, and distinctive feature descriptors for real-time performance and scalability.
Existing descriptors are often too computationally expensive for mobile devices or lack the robustness needed for large-scale matching.
These limitations restrict the capabilities and practical deployment of current mobile AR systems.

Purpose of the Study:

To propose a novel feature descriptor, Learning-based Local Difference Binary (LLDB), optimized for mobile AR applications.
To enhance the efficiency, robustness, and distinctiveness of feature descriptors for improved user experience and system scalability.
To address the performance bottlenecks of existing descriptors in real-time mobile AR scenarios.

Main Methods:

LLDB computes binary strings for image patches using intensity and gradient differences on grid cell pairs.
A modified AdaBoost algorithm is employed to select optimal grid cell pairs, maximizing discriminative power.
The descriptor is designed for rapid computation and matching against large descriptor databases.

Main Results:

LLDB demonstrates extremely fast computation and matching speeds, even with large databases (2.3M descriptors).
The descriptor exhibits high robustness and distinctiveness, leading to improved accuracy in matching.
LLDB achieves comparable descriptor construction efficiency to state-of-the-art methods while offering superior matching accuracy and speed on mobile devices.

Conclusions:

LLDB presents a highly efficient, robust, and distinctive binary descriptor suitable for mobile AR systems.
The proposed method significantly enhances the performance of mobile AR by enabling faster and more accurate feature matching.
LLDB overcomes previous limitations, paving the way for more capable and widely deployable mobile AR applications.