南京信息工程大学学报2025，Vol.17Issue(2)：181-190,10.DOI:10.13878/j.cnki.jnuist.20240513002
基于Alpha Shapes轮廓点云识别算法的洞室表面形变区域提取方法
Extraction of cavern surface deformation regions based on Alpha Shapes contour point cloud recognition algorithm
摘要
Abstract
Aiming at the extraction of cavern surface deformation from three-dimensional laser scanning dense point clouds,we propose a method integrating the Multiscale Model-to-Model Cloud Comparison(M3C2)with an im-proved Alpha Shapes algorithm.First,the two-phase surface point cloud data are registered,and the improved Alpha Shapes algorithm is used to identify the outer contour point clouds.After the fine registration of these two-phase outer contour point clouds,the M3C2 algorithm calculates the deformation value of each point,and finally the continuous deformation regions are extracted through distance clustering.Experimental results show that the proposed method ef-fectively eliminates the points at small furrows as well as those affected by mixed pixels.Specifically,the removal rates of point clouds in the two phases within 10 m from the scanner to the cavern section are 14.17%and 13.52%,respectively,which are 6.25%and 6.42%within 70 m.This method accurately and efficiently extracts the cavern surface deformation regions with more than twice the registration error.关键词
洞室变形监测/轮廓点云识别/Alpha Shapes算法/M3C2算法Key words
cavern deformation monitoring/contour point cloud recognition/Alpha Shapes algorithm/multiscale model-to-model cloud comparison(M3C2)algorithm分类
信息技术与安全科学引用本文复制引用
张雨婷,郑德华,李思远..基于Alpha Shapes轮廓点云识别算法的洞室表面形变区域提取方法[J].南京信息工程大学学报,2025,17(2):181-190,10.基金项目
钱投科创项目(QT202208A001) （QT202208A001）
