Laser & Optoelectronics Progress, Volume. 58, Issue 6, 610008(2021)

Adaptive Point Cloud Reduction Based on Multi Parameter k-Means Clustering

Wang Jianqiang1, Fan Yanguo1, Li Guosheng1, and Yu Dingfeng2、*
Author Affiliations
  • 1College of Ocean and Space Information, China University of Petroleum, Qingdao, Shandong 266580, China
  • 2Institute of Marine Instrumentation, Shandong Academy of Sciences, Qilu University of Technology, Qingdao, Shandong 266061, China
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    References(17)

    [4] Sun P F. Streamlined compression analysis and practice of point cloud data based on coordinate increment Xi'an: Xi'an University of Science and[D]. Technology(2014).

    [7] Miao Y W, Pajarola R, Feng J Q. Curvature-aware adaptive re-sampling for point-sampled geometry[J]. Computer-Aided Design, 41, 395-403(2009).

    [9] Tang Z Y. Point cloud reduction algorithm based on Poisson distribution and K-means clustering[D]. Taiyuan: Taiyuan University of Technology(2019).

    [10] Wang J F, Qin H[J]. Octal tree-based mean clustering point cloud simplification method Automation Application, 2019, 81-82, 99.

    [11] Yan P. Feature extraction and clustering simplification algorithm for 3D laser point cloud Xi'an: Xi'an University of Science and[D]. Technology(2018).

    [13] Chen L, Cai Y, Zhang J S et al. Feature point extraction of scattered point cloud based on multiple parameters hybridization method[J]. Application Research of Computers, 34, 2867-2870(2017).

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    Wang Jianqiang, Fan Yanguo, Li Guosheng, Yu Dingfeng. Adaptive Point Cloud Reduction Based on Multi Parameter k-Means Clustering[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610008

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    Paper Information

    Category: Image Processing

    Received: Jul. 22, 2020

    Accepted: --

    Published Online: Mar. 11, 2021

    The Author Email: Dingfeng Yu (z18010014@s.upc.edu.cn)

    DOI:10.3788/LOP202158.0610008

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