摘要
点云是理解三维场景的重要形式之一,3D点云在海洋平台逆向建模、海底地形测绘、深水浮式结构系泊系统损伤测量及海底管线可视化等方面都有着重要应用。基于此,文中梳理了点云数据处理方法,将其分为传统处理算法和基于深度学习方法两大类;传统处理算法从滤波、对象识别与分类和配准3方面进行了介绍总结;基于深度学习方法从点云、体素化和多视图3方面进行了介绍总结。对各种算法的优缺点进行了归纳对比,并展望了3D点云处理技术未来的发展趋势与方向。
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.
作者
郭张翔
闫天红
周国强
GUO Zhangxiang;YAN Tianhong;ZHOU Guoqiang(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)
出处
《计算机科学》
CSCD
北大核心
2024年第S02期196-208,共13页
Computer Science
基金
海南省院士创新平台科研项目(YSPTZX202301)
2022年三亚市科技创新专项(2022KJCX52)。
关键词
3D点云
数据处理
传统方法
深度学习
3D point clouds
Data processing
Traditional methods
Deep learning
