Point Clouds Data Organization And Visualization Of Three-dimensional Laser Scanning

Three-dimensional laser scanning system obtain large point clouds data, so as to the system runs slowly, This dissertation studies the 3D laser scanning technology, and proposes the indexing method for point cloud data, using SQL SERVER 2008 Spatial as a platform environment, B tree and Hilbert curve is used to index point cloud data, which solve the database management and operational issues of massive point cloud data.The grid index based on linear quadtree is used to index the point cloud data, extracting the point cloud on the block. The index method based on octree and B tree is used to organize the point cloud data. The space partition of three dimensional object and sub-regional search algorithms are achieved. The hierarchical visualization methods of point cloud data of blocks organization is proposed.The theory of space transformation is leaded into, and realize the transformation of the point cloud data between different dimensions of spatial data and different reference systems, which provides a theoretical basis of spatial transformation for the algorithm research of feature extraction. Finally,3D laser scan data organization and visualization system is achieved, Verify the efficiency and feasibility of three kinds of indexing methods.