Point cloud-based method and system of extracting building feature lines

Abstract

The invention provides a point cloud-based method and system of extracting building feature lines. The method comprises: extracting a plurality of planes from massive point cloud plane data; extracting two planes from the plurality of planes, and judging whether the two planes intersect; calculating and extracting an intersection line of the two planes if the two planes intersect; and judging whether two other intersecting planes exist in the plurality of planes, if yes, returning to the step of calculating and extracting an intersection line of the two planes, and otherwise, ending a process.According to the point cloud-based method of extracting the building feature lines provided by the invention, a shared nearest neighbor clustering algorithm is used for plane dividing of a building point cloud of three-dimensional ground laser scanning, the number of clusters does not need to be input in a clustering process, a method of plane intersection is used at the same time to extract building edge line features, a plane intersection distinguishing method is relatively simple, and edge line point coordinate calculation is accurate.

Description

technical field [0001] The invention belongs to the technical field of extracting decoration feature lines, in particular to a method and system for extracting building feature lines based on point clouds. Background technique [0002] The existing point cloud feature line extraction methods mainly include the following categories: [0003] Gumhold et al. first expressed the spatial position relationship between point clouds by constructing a Riemann tree, using the eigenvalues ​​of the covariance matrix of the local neighborhood coordinate vectors as the basis for measuring that a point belongs to a feature point, and using the obtained weight as the minimum generated The basis of the tree; then connect the feature points by optimizing the minimum spanning tree of the feature point set to generate the feature polyline. Based on the normal clustering method, Demarsi et al. proposed a closed feature line extraction method for 3D point cloud data. First, the point cloud data...

AI Technical Summary

Problems solved by technology

Interpolate the feature lines to fill in the feature points on the ridge line in the point cloud. In this process, the feature information of each data point needs to be calculated. The calculation data is large, and the obtained feature points need to be sorted to confirm the connection relationship, and the calculation is complicated. , not suitable for the extraction of feature lines of urban point cloud building point cloud

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