Point cloud feature point detection method and point cloud feature extraction method

Abstract

The invention provides a point cloud feature point detection method and a point cloud feature extraction method. The inspection method includes: establishing a local spherical coordinate system with a data point in the point cloud data as the center of the sphere, and finding k neighborhoods through a neighbor algorithm; connecting the center of the sphere with each nearest neighbor point respectively to obtain k line segment groups; Sort the k line segment groups according to the size of the horizontal projection angle of the line segment groups, and check each line segment group by the Laplacian operator to determine whether the data point is a feature point; process each data point in the point cloud data according to the above steps, get All feature points of the point cloud data. The point cloud feature extraction method includes the steps of: determining feature points according to the above-mentioned detection method, and recording the regional connection information of each feature point; Polygon to achieve region segmentation. The number of characteristic points marked by the invention is small, the extraction of characteristic points is orderly, and the reconstruction of characteristic lines is simple and convenient.

Description

technical field [0001] The invention relates to point cloud model storage, in particular, to a point cloud feature point detection method and a point cloud feature extraction method. Background technique [0002] 3D laser scanning technology is widely used in mechanical design, cultural relic protection, medicine, architectural measurement and other fields. More and more physical samples and CAD models are stored in point cloud models. Point cloud models often only have spatial coordinate information, without geometric attributes such as surface normal and curvature, and topological structures such as triangular meshes and model parameters. There are various geometric structures such as feature points, characteristic curves, and surfaces in the model. Separating various geometric structures cannot reasonably segment the surface slices of the point cloud, and it is difficult to directly reconstruct the physical sample model from the point cloud data as a whole. Even so, it al...

AI Technical Summary

Problems solved by technology

Feature point detection involves point geometric attribute estimation. Since there is no topological structure and other factors, point cloud geometric attribute estimation is by finding the k-nearest neighbor point of a given point, fitting the nearest neighbor point as a paraboloid, and approximating the point curvature with the paraboloid curvature, or Use methods such as PCA to estimate the normal direction of the point by generating a covariance matrix, or estimate the dihedral angle by generating a triangular mesh or Voronoi mesh of the point cloud to help feature point detection. The geometric quantities estimated by these methods are not high in accuracy. Sharp feature errors are especially noticeable

In addition, the comparison between point geometric quantities is carried out between any two points in the neighborhood. As long as the deviation is greater than a given threshold, the point is considered as a feature point. The number of marked feature points is large, which brings difficulties to feature line reconstruction.

[0004] Feature line reconstruction is to use the extracted feature points, regional clustering of non-feature points and connection information between feature points and regions to connect feature points into curve segments. The order of feature points is very critical in the reconstruction stage. Before the feature line is unknown, any Feature points may be located on multiple feature lines. Existing methods connect feature lines by comparing distances or generating minimum spanning tree paths. Errors may occur in feature line connections

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