电力勘测设计Issue(z1)：43-48,6.DOI:10.13500/j.dlkcsj.issn1671-9913.2025.S1.007
深度神经网络在点云地面滤波中的应用
Application of Deep Neural Networks in Point Cloud Ground Filtering
文文 1何杰 1韩云文 2钱俊锦1
作者信息
- 1. 四川中水成勘院测绘工程有限责任公司,四川 成都 610072
- 2. 四川能源发展集团有限责任公司,四川 成都 610095
- 折叠
摘要
Abstract
LiDAR has become a key technology for acquiring high-precision surface information.However,due to the diversity of terrain conditions and the limitations in adaptability of existing algorithms,accurately separating ground points from non-ground points in complex point cloud data—known as point cloud ground filtering—remains challenging.This paper comparatively studies three ground filtering methods:the rule-based PTD and CSF,as well as the deep neural network-based TransGF.The experiment analyzes point cloud data from a study area in Zhenjiang City,Jiangsu Province,which features complex terrain characteristics.The performance of each method is evaluated using metrics such as Type I error,Type II error,Total error,and the Kappa coefficient.The results show that the TransGF algorithm performs optimally across all performance metrics.Particularly in handling terrain features such as steep cliffs,TransGF demonstrates superior precision in retaining ground information while generating smoother digital surface models.关键词
点云/地面滤波/神经网络Key words
point cloud/ground filtering/neural network分类
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文文,何杰,韩云文,钱俊锦..深度神经网络在点云地面滤波中的应用[J].电力勘测设计,2025,(z1):43-48,6.
