Three-dimensional space index method aiming at massive laser radar point cloud models

Abstract

The invention discloses a three-dimensional space index method aiming at massive laser radar point cloud models. Aiming at characteristics of massive data and high spatial resolution of point cloud models, the method indexes data to a single three-dimensional space point by adopting a multi-level hybrid index strategy; aiming at an integrity characteristic of point cloud models, the method continuously subdivides massive cloud points from a minimum bounding rectangle (MBB) of an overall point cloud model by adopting a cotree index, and three-dimensional points in cotree index nodes are distributed uniformly in space; and aiming at a scattered characteristic and the data post-processing requirement of large-scale point cloud models, the method indexes a single three-dimensional space point by adopting a three-dimensional K-D tree, and the quick inquiry and processing of a single point coordinate and the attribute data of the single point coordinate can be achieved. On the basis of above integrated space index construction, the method adopts a three-dimensional R tree to manage the MBBs of a plurality of point cloud models in three-dimensional scenes, and a multi-level hybrid space index mode is formed finally.

Description

technical field [0001] The invention relates to a three-dimensional space indexing method, in particular to a three-dimensional space indexing method for massive laser radar point cloud models. Background technique [0002] The point cloud model is a three-dimensional spatial data model based on scattered point sets, which itself contains various characteristics of the point cloud. [0003] Generally speaking, the point cloud model is a complete target point cloud model obtained by processing the multi-view scanning point cloud through deduplication, multi-temporal registration and fusion. It is the primary product obtained after processing multiple single-site clouds. The point cloud model has the characteristics of large amount of data (massiveness), fine data expression (high spatial resolution), relatively balanced spatial distribution (integrity), and no topological relationship between spatial three-dimensional points (scattered). [0004] Both K-D tree and Octree are...

AI Technical Summary

Problems solved by technology

However, for massive point cloud model data, the 3D K-D tree needs to be indexed to a single 3D point or a small number of 3D point sets, and its tree depth often exceeds 10 layers, or even reaches more than 20 layers. In this case, the retrieval of spatial index The efficiency will be greatly reduced, and the purpose of efficiently retrieving massive spatial data will not be achieved

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