双层索引驱动的隧洞海量点云高效管理方法

A dual-layer indexing driven management method for massive point clouds of underground tunnels

针对隧洞表观性态监测,三维激光扫描获取的点云具有数据量巨大、非结构化以及狭长线状非均匀分布等特点,给隧洞点云数据处理极大的压力,也制约了隧洞工程点云监测应用的发展。为此,本文结合隧洞工程空间分布特点,提出一种基于双层索引结构的隧洞海量点云管理方法。该方法设计了一种基于Hough变换的隧洞水平中线粗提取方法,指导隧洞点云数据沿水平中线进行点云自动分段;而后利用“自下而上”的归并构建策略建立分段点云八叉树索引。在此基础上,利用非冗余的多层次细节(LOD)建模方法和内外存动态调度技术实现海量点云数据快速可视化。实验结果显示,本文方法有效提高了隧洞点云水平轴线提取效率,基于双层索引结构的隧洞点云管理在点云检索、海量点云数据可视化等方面表现出优异性能。

For the task of tunnel surface monitoring,point clouds obtained by 3D laser scanning suffer from undesirable characteristics such as enormous data volumes,unstructured organization,and narrow linear non-uniform distribution,which imposes significant pressure on tunnel point cloud data processing and constrains the development of tunnel monitoring application.This paper presents a tunnel massive point cloud management method based on a dual-layer indexing structure.A Hough transform-based method for preliminary determination of the horizontal tunnel centerline is designed to guide the automatic segmentation of tunnel point clouds along the centerline.Then,a“bottom-up”merging strategy is suggested for generating local octrees for the segmented point clouds;Based on this,application of the non-redundant Level of Detail(LOD)modeling and the dynamic memory dynamic scheduling enables real-time visualization of massive point clouds.The experimental results show that our new method improves efficiency significantly in extracting the tunnel's horizontal axis,and it is better than traditional approaches in point cloud retrieval and visualization of massive point cloud data.