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Laser & Optoelectronics Progress, Volume. 55, Issue 1, 11008(2018)

Point Cloud Denoising and Simplification Algorithm Based on Method Library

Li Renzhong*, Yang Man, Ran Yuan, Zhang Huanhuan, Jing Junfeng, and Li Pengfei
Author Affiliations
  • School of Electronics and Information, Xi''an Polytechnic University, Xi''an, Shaanxi, 710048, China
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    AbstractGet PDF(in Chinese)
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    Figures&Tables (9)
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    References (15)
    Cited By (17)
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    Figures & Tables(9)
    Flow chart of the proposed algorithm

    Fig. 1. Flow chart of the proposed algorithm

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    Principle sketch map of the statistical filter

    Fig. 2. Principle sketch map of the statistical filter

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    Principle sketch map of the radius filter

    Fig. 3. Principle sketch map of the radius filter

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    Schematic of k-neighbors voxel grid simplification[13]

    Fig. 4. Schematic of k-neighbors voxel grid simplification[13]

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    Schematic of triangular mesh reconstruction[13]

    Fig. 5. Schematic of triangular mesh reconstruction[13]

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    Test results of 45° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction

    Fig. 6. Test results of 45° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction

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    Test results of 180° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction

    Fig. 7. Test results of 180° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction

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    Test results of self point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction

    Fig. 8. Test results of self point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction

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    • Table 1. Running time of each program module and variations of the point number of the point cloud

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      Table 1. Running time of each program module and variations of the point number of the point cloud

      IndexPoint cloudOriginalpoint cloudLarge scaledenoisingSmall scaledenoisingUniformsimplificationReconstruction
      Point number45° human27911252782605439773977
      180° human30424299102896053145314
      Self12902612458212391161686168
      Running time /ms45° human-613+72085629921856
      180° human-523+908123927952620
      Self-1621791614952783425
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    Li Renzhong, Yang Man, Ran Yuan, Zhang Huanhuan, Jing Junfeng, Li Pengfei. Point Cloud Denoising and Simplification Algorithm Based on Method Library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11008

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    Paper Information

    Category: Image Processing

    Received: Jul. 4, 2017

    Accepted: --

    Published Online: Sep. 10, 2018

    The Author Email: Li Renzhong (lirenzhong@xpu.edu.cn)

    DOI:10.3788/LOP55.011008

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology