Laser Point Cloud 3D Visual Intelligent System Based on B / S Architecture

Abstract

In the mid-1990s, the ground laser scanning technology based on B / S architecture began to be widely used in modeling various complex scenes and spatial entities. The massive point cloud data based on the B / S architecture collected by this technology is an important source of information in the three-dimensional space coordinate system, and plays an important role in marine survey, geographic information system, and digital city construction. To this end, how to use the existing computer processing capabilities to efficiently organize and index massive point cloud data and more quickly and accurately complete the three-dimensional visualization modeling of point cloud data has become an important research topic. This paper proposes a "Hilbert-Improved Quadtree" structure to organize point cloud data. In this structure, the node order of the improved quadtree is changed, so that the node order obtained by traversing the quadtree in the middle order completely conforms to the characteristics of the Hilbert curve. Reorganizing the point cloud data based on the B / S architecture in this order can effectively reduce the number of I / O interactions performed by the computer when reading massive point cloud data based on the B / S architecture, and improve the spatial index efficiency of the point cloud data; At the same time, the Hilbert curve is used to reorganize the quadtree to convert single-resolution data into multi-resolution data. At the end of this paper, how to use existing computer resources to more efficiently process massive point cloud data based on B / S architecture Outlook.