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Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

机译：基于离散曲线的3D点云数据特征提取

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摘要

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.
机译：从3D点云数据中可靠地提取特征是许多应用领域中的重要问题，例如逆向工程，对象识别，工业检查和自主导航。本文提出了一种基于离散曲线从3D点云数据中提取几何特征的新方法。我们从3D点云数据中提取离散曲线，并研究在离散曲线的几何特征上弦长，角度变化和主曲率的行为。然后，定义相应的相似性指标。基于相似性指标，可以从离散曲线中提取几何特征，这也是3D点云数据的几何特征。相似度指标的阈值取自[0,1]，表征了相对关系，使阈值设置更加容易，合理。实验结果表明，该方法是有效且可靠的。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第3期|290740.1-290740.19|共19页
  • 作者

    Yi An; Zhuohan Li; Cheng Shao;

  • 作者单位

    School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China;

    School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China;

    School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China;

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