City-level three-dimensional building model indexing method

Abstract

The invention discloses a city-level three-dimensional building model indexing method. The method comprises the steps that 1, meshing is performed on a city geographic range according to a unit mesh with a fixed size, and a first-level mesh index is established; 2, coordinate points of each three-dimensional building model in a horizontal plane in each mesh are acquired; and 3, a KD tree is constructed in each mesh, and a second-level KD tree index is established, wherein nodes of the KD trees are the coordinates points of the three-dimensional building models in the horizontal plane in the meshes. Through the method, a needed building model is quickly retrieved through rough positioning and precise positioning in a city-level database by establishing the first-level mesh index and the second-level KD tree index, and therefore good user experience is obtained.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional geographic information, and in particular relates to an indexing method of a three-dimensional building model. Background technique [0002] With the rapid development of mobile communication technology, 3D graphics rendering technology and geographic information technology, GIS research is developing towards mobile 3D and has become a hot spot in GIS research. In urban life, many activities of people are inseparable from the location information of mobile devices, and obtain other spatial geographic information needed, including the data of buildings in the city. [0003] In the city-level geographic information database, not only the data volume of a single building model is relatively large, but also the number of 3D building models is relatively large, usually reaching millions. It takes a lot of time to find the currently needed model from many building models. Then it is necessar...

AI Technical Summary

Problems solved by technology

The R tree allows nodes to overlap each other, which makes the R tree maintain a high space utilization rate and maintain the balance of the tree; but too much overlap may cause a decrease in query efficiency

A-tree combines the characteristics of raster files and R-tree index, quickly and roughly query through small grids, and then use the idea of ​​approximate R-tree index; grid index is object-oriented to the spatial position and distribution, and belongs to raster index, with ideas Simple, efficient and concise, and easy to implement; but when the amount of data is relatively large, the indexing efficiency is too low

The KD tree is one of the most prominent multi-dimensional data structures, and it is also a binary search tree that stores k-dimensional space points. The search and insertion of the KD tree are simple; but the secondary storage effect is poor, and it may cause children below the deletion point when deleting. tree reorganization

[0005] Therefore, the existing spatial indexing methods cannot meet the requirements for efficient retrieval of target building models in city-level databases

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