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Three-dimensional object recognition using an extensible local surface descriptor

机译：使用可扩展局部表面描述符的三维物体识别

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摘要

We present an extensible local feature descriptor that can encode both geometric and photometric information. We first construct a unique and stable local reference frame (LRF) using the sphere neighboring points of a feature point. Then, all the neighboring points are transformed with the LRF to keep invariance to transformations. The sphere neighboring region is divided into several sphere shells. In each sphere shell, we calculate the cosine values of the point with the x-axis and z-axis. These two values are then mapped into two one-dimensional (1-D) histograms, respectively. Finally, all of the 1-D histograms are concatenated to form the signature of position angles histogram (SPAH) feature. The SPAH feature can easily be extended to a color SPAH (CSPAH) by adding another 1 -D histogram generated by the photometric information of each point in each shell. The SPAH and CSPAH were rigorously tested on several common datasets. The experimental results show that both feature descriptors were highly descriptive and robust under Gaussian noise and varying mesh decimations. Moreover, we tested our SPAH- and CSPAH-based three-dimensional object recognition algorithms on four standard datasets. The experimental results show that our algorithms outperformed the State-Of-the-art algorithms On these datasets. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：我们提出了一个可扩展的局部特征描述符，它可以对几何和光度信息进行编码。我们首先使用特征点的球体相邻点构造一个唯一且稳定的局部参考系（LRF）。然后，使用LRF对所有相邻点进行变换，以保持变换的不变性。球体邻近区域分为几个球体壳。在每个球壳中，我们使用x轴和z轴计算该点的余弦值。然后将这两个值分别映射到两个一维（1-D）直方图中。最后，将所有一维直方图连接起来以形成位置角直方图（SPAH）特征的签名。通过添加由每个壳中每个点的光度信息生成的另一个一维直方图，可以轻松地将SPAH功能扩展为彩色SPAH（CSPAH）。 SPAH和CSPAH已在几个常用数据集上经过严格测试。实验结果表明，在高斯噪声和变化的网格抽取下，两个特征描述符都具有很高的描述性和鲁棒性。此外，我们在四个标准数据集上测试了基于SPAH和CSPAH的三维对象识别算法。实验结果表明，在这些数据集上，我们的算法优于最新算法。 ©2017光电仪器工程师协会（SPIE）

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  • 来源
    《Optical engineering》 |2017年第12期|123109.1-123109.13|共13页
  • 作者

    Rongrong Lu; Feng Zhu; Qingxiao Wu; Yingming Hao;

  • 作者单位

    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China,University of Chinese Academy of Sciences, Beijing, China,Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China,The Key Laboratory of Image Understanding and Computer Vision, Liaoning Province, Shenyang, China;

    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China,Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China,The Key Laboratory of Image Understanding and Computer Vision, Liaoning Province, Shenyang, China;

    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China,Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China,The Key Laboratory of Image Understanding and Computer Vision, Liaoning Province, Shenyang, China;

    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China,Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China,The Key Laboratory of Image Understanding and Computer Vision, Liaoning Province, Shenyang, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    three-dimensional object recognition; local feature; local reference frame; photometric information; Paper 171168P received Jul. 26, 2017; accepted for publication Dec. 1, 2017; published online Dec. 27, 2017;

    机译：三维物体识别局部特征本地参考系;光度信息;171168P号文件于2017年7月26日收到;接受于2017年12月1日发布;在线发布于2017年12月27日;

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