A building roof reconstruction method based on airborne lidar data

Abstract

The invention discloses a building roof reconstruction method based on airborne LiDAR data. The method comprises the steps of 1, obtaining LiDAR point cloud data, and 2, processing the LiDAR point cloud data; the step 2 comprises the processes of 201, dividing building roof patches to obtain a plurality of roof patches from the building roof, 202, obtaining building roof point cloud data, 203, extracting outer contour points of the building roof and the roof patches, and meanwhile, extracting the outer boundary line of the building roof, 204, obtaining the vector boundaries of the roof patches, including establishing a search information set, carrying out neighborhood search, determining the types of the outer contour points and determining the vector boundaries, and 205, obtaining a vector model of the building roof, namely obtaining the vector model of the building roof after determining the vector boundaries of the plurality of roof patches. The method is simple in steps, reasonable in design and convenient to realize, good in using effect, and capable of completing the reconstruction processes of the building roof simply, conveniently and quickly with high quality.

Description

technical field [0001] The invention relates to a building roof modeling method, in particular to a building roof reconstruction method based on airborne LiDAR data. Background technique [0002] 3D building models are widely used in digital city fields such as urban and regional planning, transportation planning, population estimation and forecasting, pollution analysis, and energy budgeting. Traditional 3D modeling methods mainly use photogrammetry and other surveying and mapping methods for urban modeling, which need to be measured in stereo image pairs. The extracted 3D models have problems such as low precision and poor quality, and are labor-intensive and costly. , long cycle and other shortcomings. [0003] At present, the modeling technology based on airborne Lidar data has made great progress. The ground point cloud is separated by data filtering, and then the vector model is obtained by processing the building point cloud. The main methods include model-driven met...

AI Technical Summary

Problems solved by technology

[0004] First, the model library is incomplete: the architectural style of complex buildings is difficult to estimate, and it is difficult to combine the simple collection models of the model library into a real building model;

[0005] Second, the building itself is difficult to decompose: the algorithm is aimed at point cloud data types, and it is difficult to decompose complex buildings through spatial set analysis;

[0006] Third, when selecting model units, the matching of parameters is difficult to determine: different point cloud data densities are different, and the shape of the intermediate model in the database also has certain repetitions, so it is difficult to determine the selection of model units through parameters

[0008] First, the data requirements are complex: as disclosed in the invention patent application document with the publication number CN102521884A, the 3D roof reconstruction method based on LiDAR data and orthophotos requires both lidar point cloud data and corresponding orthophotos. This raises the threshold for building reconstruction, the orthophoto itself is distorted and the matching processing between different data will bring certain errors;

[0009] Second, when the point cloud density is low, the noise is large, or even the point cloud is missing, the feature line cannot be extracted correctly, resulting in a merge error;

[0011] Fourth, relying too much on the results of the segmentation, the original point cloud data is discarded during the merging process, and it is impossible to judge the wrong segmentation results;

[0012] Fifth, the final result is the model vector of the entire building, not the model vector of each plane, so when the topological relationship is wrong, the vector model is difficult to correct

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