北京测绘2026，Vol.40Issue(8)：1171-1174,4.DOI:10.19580/j.cnki.1007-3000.2025090053
一种基于曲面变化度的点云精简算法
A point cloud simplification method based on surface variation
杨学彬 1邓勇 1彭云1
作者信息
- 1. 中国电建集团中南勘测设计研究院有限公司,湖南 长沙 410007
- 折叠
摘要
Abstract
Three-dimensional(3D)laser scanning technology is characterized by high efficiency,high precision,and high resolu-tion,serving as a core technical approach for spatial geographic information data acquisition.However,the point clouds generated by this technology feature massive data volumes,which impose stringent requirements on hardware and software environments during processing and lead to low processing efficiency.To improve the efficiency of point cloud data processing,point clouds are typically subjected to optimization workflows,with the core principle of minimizing point cloud density as much as possible while preserving the key features of the scanned targets.This paper proposed a point cloud simplification algorithm based on surface variation degree.First,the surface variation degree of the input point cloud was calculated.Next,the point cloud was partitioned into gently varying regions and steeply varying regions based on the computed surface variation degree and object feature con-straints.The voxel grid method was adopted for simplification of points in gently varying regions,while the curvature-based sim-plification method was applied to points in steeply varying regions.Experimental results demonstrate that the proposed algorithm can effectively retain the primary features of the scanned objects,significantly reduce point cloud density and the total number of points.It thus provides a valuable reference for the processing and application of 3D laser scanning data.关键词
激光扫描/点云优化/点云精简/体素格法/曲率精简法Key words
laser scanning/point cloud optimization/point cloud simplification/voxel grid method/curvature simplifica-tion method分类
天文与地球科学引用本文复制引用
杨学彬,邓勇,彭云..一种基于曲面变化度的点云精简算法[J].北京测绘,2026,40(8):1171-1174,4.
