煤田地质与勘探2025，Vol.53Issue(5)：54-64,11.DOI:10.12363/issn.1001-1986.24.10.0655
巷道点云分类去噪及三维重建技术
Classification-based point cloud denoising and 3D reconstruction of roadways
摘要
Abstract
[Objective]The point cloud denoising and 3D reconstruction of roadways serve as a key step in the digital modeling and analysis of roadways.However,the conventional algorithm based on single filtering fails to effectively re-move the noise at varying scales from point clouds.Meanwhile,the existing 3D reconstruction algorithms suffer from low modeling accuracy and high susceptibility to distortion.These necessitate developing methods and technologies to obtain high-quality point cloud data and construct high-accuracy 3D models for roadways.[Methods]This study pro-posed an adaptive classification-based point cloud denoising algorithm using neighborhood radius(R),minimum neigh-borhood point number(Imin),spatial threshold(σc),and feature preservation factor(σs).Accordingly,this study designed a deep-learning implicit surface reconstruction method based on signed distance functions(SDFs).By integrating a mean value method,an improved density-based spatial clustering of applications with noise(DBSCAN)algorithm,and an im-proved bilateral filtering algorithm,this study constructed a technical framework for classification processing.The integ-ration algorithm could automatically analyze the noise types of point cloud data and then efficiently remove noise at dif-ferent scales via an adaptive mechanism,thus achieving in-depth cleaning of main point cloud data.Then,the local re-gional features of the point clouds of a roadway were extracted using PointNet++,and local contextual information was learned using an introduced implicit neural network.As a result,the global SDF model was created.Finally,this study constructed a fine-scale 3D roadway model by combining the marching cubes algorithm.[Results and Conclusions]Based on the experimental scene of the 1∶1 simulated roadway of the Zhangji coal mine in Anhui Province,this study explored the point cloud denoising and 3D reconstruction roadways in a multi-dimensional space.The results indicate that the integration algorithm developed in this study could adjust the denoising strategy dynamically according to the roadway scene and noise categories.This algorithm delivered adaptive optimization performance,yielding types Ⅰ andⅡ errors of 1.54%and 5.37%,respectively.Therefore,it can effectively remove large-scale,small-scale,and repetitive noise while preserving the features of main point cloud data.The reconstruction algorithm could reduce holes effect-ively while maintaining the accuracy and smoothness of the roadway model.Furthermore,it enabled the accurate charac-terization of the structural details of complex locations,with average,standard,and root-mean-square errors of the recon-structed roadway model of 0.037 m,0.040 m,and 0.041 m,respectively.Therefore,the reconstructed model can meet the high-accuracy requirements of intelligent mine construction.This study will provide high-quality 3D data for the di-gital transformation and upgrading of mines,along with their intelligent and precise mining.关键词
智能化矿山/三维激光/三维建模/点云去噪/巷道建模/空间测量Key words
intelligent mine/3D laser/3D modeling/point cloud denoising/roadway modeling/spatial measurement分类
矿山工程引用本文复制引用
陈登红,庞宁,聂闻,封居强,阚吉亮,张进京..巷道点云分类去噪及三维重建技术[J].煤田地质与勘探,2025,53(5):54-64,11.基金项目
国家自然科学基金面上项目(51974008) （51974008）
淮北股份有限公司-安徽理工大学研究生联合培养示范基地项目(2022lhpysfjd037) （2022lhpysfjd037）
智能采矿工程新建专业质量提升项目(2022xjzlts008) （2022xjzlts008）
国家重点研发计划项目(2021YFC3001304) （2021YFC3001304）
矿业工程专业学位现代采矿技术教学案例库项目(2022zyxwjxalk087) （2022zyxwjxalk087）
