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

机译:学习优化的本地差异二进制文件,以在移动设备上实现可扩展的增强现实

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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 computationally 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 Learning -based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LLDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.
机译:特征描述符的效率,鲁棒性和独特性对于移动增强现实(AR)系统的用户体验和可伸缩性至关重要。但是,现有的描述符要么在计算上过于昂贵,以至于无法在诸如智能手机或平板电脑之类的移动设备上实现实时性能,要么不够健壮且与众不同,无法从大型数据库中识别出正确的匹配项。结果,当前的移动AR系统仍然仅具有有限的能力,这在实践中极大地限制了它们的部署。在本文中,我们提出了一种高效,健壮和独特的二进制描述符,称为基于学习的本地差异二进制(LLDB)。 LLDB使用简单的强度和梯度差测试在图像块中的成对网格单元上直接为图像图像块计算二进制字符串。为了选择一组优化的网格单元对,我们从图像补丁中密集采样网格单元,然后利用改进的AdaBoost算法自动提取一小批关键单元,目的是最大化不匹配之间的汉明距离,同时将匹配之间的汉明距离最小化。 。实验结果表明,LLDB具有很高的鲁棒性和独特性,因此它可以非常快速地计算并与大型数据库匹配。与主要为速度而设计的最新二进制描述符相比,LLDB在描述符构造方面具有相似的效率,而在大型数据库上与移动设备上的2.3M描述符进行匹配时,则可实现更高的准确性和更快的匹配速度。

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