LDB: An ultra-fast feature for scalable Augmented Reality on mobile devices

Abstract

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile Augmented Reality (AR) system. However, existing descriptors are either too compute-expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient, robust and distinctive binary descriptor, called Local Difference Binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. A multiple gridding strategy is applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that LDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Comparing to the state-of-the-art binary descriptor BRIEF, primarily designed for speed, LDB has similar computational efficiency, while achieves a greater accuracy and 5x faster matching speed when matching over a large database with 1.7M+ descriptors.