山东理工大学学报（自然科学版）2025，Vol.39Issue(5)：1-6,6.
基于三维激光扫描仪的建筑物特征检测
3D laser scanner-based building feature detection
摘要
Abstract
Accurate and fast building feature extraction is of great significance for construction of smart cities.Polygonal features and linear features are the most fundamental geometric information,which can accurately reveal building structural characteristics.This study first conducted comparative analysis of surface feature detection using three distinct approaches:Euclidean clustering,random sampling consis-tency algorithm and regional growth method,followed by quantitative performance comparison.For edge characterization,the linear features were detected by normal vector and approximate curvature estimation,respectively,and the effects of them were compared and analyzed.Experiments show that the regional growth method demonstrated superior precision of surface feature extraction compared to other methods,whereas the approximate curvature estimation enhanced continuity of smoothness of linear characteristics.The methods adopted in this study for accurate and efficient detection of building features has considerable application value for subsequent reconstruction of building models.关键词
点云数据/特征检测/区域生长/近似曲率Key words
point cloud data/feature detection/regional growth/approximate curvature分类
天文与地球科学引用本文复制引用
陈修春,徐工..基于三维激光扫描仪的建筑物特征检测[J].山东理工大学学报（自然科学版）,2025,39(5):1-6,6.基金项目
山东省省级大学生创新创业训练计划(S202310433129) （S202310433129）
