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A new fast filtering algorithm for a 3D point cloud based on RGB-D information

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  • Chaochuan Jia
  • Ting Yang
  • Chuanjiang Wang
  • Binghui Fan
  • Fugui He

Abstract

A point cloud that is obtained by an RGB-D camera will inevitably be affected by outliers that do not belong to the surface of the object, which is due to the different viewing angles, light intensities, and reflective characteristics of the object surface and the limitations of the sensors. An effective and fast outlier removal method based on RGB-D information is proposed in this paper. This method aligns the color image to the depth image, and the color mapping image is converted to an HSV image. Then, the optimal segmentation threshold of the V image that is calculated by using the Otsu algorithm is applied to segment the color mapping image into a binary image, which is used to extract the valid point cloud from the original point cloud with outliers. The robustness of the proposed method to the noise types, light intensity and contrast is evaluated by using several experiments; additionally, the method is compared with other filtering methods and applied to independently developed foot scanning equipment. The experimental results show that the proposed method can remove all type of outliers quickly and effectively.

Suggested Citation

  • Chaochuan Jia & Ting Yang & Chuanjiang Wang & Binghui Fan & Fugui He, 2019. "A new fast filtering algorithm for a 3D point cloud based on RGB-D information," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-21, August.
    • Handle: RePEc:plo:pone00:0220253
    DOI: 10.1371/journal.pone.0220253
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    Cited by:

    1. Yu, Siyi & Li, Hua & Chen, Xiaofeng & Lin, Dongyuan, 2023. "Multistability analysis of quaternion-valued neural networks with cosine activation functions," Applied Mathematics and Computation, Elsevier, vol. 445(C).

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