Method for indexing scattered point cloud space of historic building

Abstract

The invention relates to the field of point cloud indexing, in particular to a method for indexing scattered point cloud space of a historic building. The method is characterized by including steps of acquiring a data construction point set; gridding the point set, setting grid index digit groups, and calculating value of each unit of the grid index digit groups according to point coordinates in grids; merging the grids into an area or dividing an area by an octree, setting area digit groups, storing ID (identification) of each area and IDs of the areas of all adjacent six sides into the corresponding digit group, constructing a K-D tree in each area, traversing grid index digit groups, and calculating IDs the areas corresponding to the grid in the area digit groups; looking for the grids and the areas where inquired points locate according to coordinates of the inquired points, indexing in the areas and the adjacent areas by means of the K-D tree and obtaining the point nearest to the inquired point. The method solves the problems of single resolution ratio and data redundancy, and guarantees inquiry efficiency of the K-D tree.

Description

technical field [0001] The invention relates to the field of point cloud indexing, in particular to a method for spatial indexing of scattered point clouds of ancient buildings. Background technique [0002] Spatial index refers to a data structure arranged in a certain order according to the position and shape of spatial objects or a certain spatial relationship between spatial objects, which contains the summary information of spatial objects. Point cloud data of ancient buildings has the characteristics of large data volume (massiveness), fine data expression (high spatial resolution), and no topological relationship (scattering) between spatial three-dimensional points. Neighborhood search, so data organization and indexing must be carried out to improve the speed of subsequent neighborhood retrieval and query operations. [0003] For point cloud data, octree and K-D tree indexes are often used. The octree algorithm is simple to implement, but it is more suitable for ev...

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Problems solved by technology

[0004] In view of the above technical problems, the present invention designs and develops a method for spatial indexing of scattered point clouds of ancient buildings. The purpose is to solve the problems of single resolution and data redundancy while maintaining the advantages of simple grid indexing algorithm and high query efficiency. , reduce the amount of data in each query, and ensure the query efficiency of the K-D tree

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