Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240100132-13.doi: 10.11896/jsjkx.240100132

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Research Progress of 3D Point Cloud Data Processing Methods

GUO Zhangxiang, YAN Tianhong, ZHOU Guoqiang   

  1. Sanya Institute of Offshore Oil and Gas,Northeast Petroleum University,Sanya,Hainan 572000,China
    College of Mechanical Science and Engineering,Northeast Petroleum University,Daqing,Heilongjiang 163000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:GUO Zhangxiang,born in 1998, master candidate.His main research interests include three dimensional visual detection of damage evaluation of marine floating platforms.
    YAN Tianhong,born in 1982,associate professor,master's supervisor.Her main research interests include safety evaluation of offshore platform structure and equipment.
  • Supported by:
    Specific Research Fund of the Innovation Platform for Academicians of Hainan Province(YSPTZX202301) and 2022 Sanya Science and Technology Innovation Project(2022KJCX52).

Abstract: Point cloud is one of the important forms to understand 3D scenes,and 3D point cloud has important applications in reverse modeling of offshore platforms,seabed topography mapping,damage measurement of mooring systems of deep-water floating structures,and visualization of submarine pipelines.Based on this,this paper sorts out the point cloud data processing methods and divides them into two categories:traditional processing algorithms and deep learning-based methods.The traditional processing algorithms are introduced and summarized from three aspects:filtering,object recognition,classification and registration.Based on the deep learning method,it is introduced and summarized from three aspects:point cloud,voxelization and multi-view.The advantages and disadvantages of various algorithms are summarized and compared,and the future development trend and direction of 3D point cloud processing technology are prospected.

Key words: 3D point clouds, Data processing, Traditional methods, Deep learning

CLC Number: 

  • TP751.1
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