Point Cloud Boundary Feature Recognition Method Constrained by Local Sample Projection Contour

Abstract

The invention provides a method for identifying the boundary feature of a sampling point set on a physical surface based on the contour shape of a local sample projection, which belongs to the field of digital design and manufacturing, and is characterized in that: normal estimation is performed on each sample point in the sampling point set and all Add a mark to the sample point, through which it can be distinguished whether the sample point is a boundary sample point state or a non-boundary sample point state, and the marks of all sample points are initially set to the boundary sample point state; for each sample point in the sampling point set, if its If it is marked as a boundary sample point, then construct a local sample projection contour along the normal direction of the sample point, and verify whether the projection point along the normal direction of the sample point falls into the contour set formed by the concave and convex points of the local sample projection, if If it does not fall into the set, the mark of the sample point is modified to be a non-boundary point state, and the subset formed by the sample points marked as the state of the boundary sample point is output. The invention can quickly identify the boundary sample points of the object surface sampling point set.

Description

technical field [0001] The invention provides a boundary feature recognition method of a sampling point set on a physical surface based on the contour shape of a local sample projection, which can be used to identify the boundary features of sampling data on the surface of a physical object, and belongs to the field of digital design and manufacturing. Background technique [0002] The boundary characteristics of the sample point set on the surface of the physical object refer to the set of sample points distributed on the edge of the non-closed point set and the set of edge sample points located in the holes inside the point set. Since the sampling data on the surface of the physical object is a set of scattered points, there is no topological information among the points in the point set, so the identification of the boundary features of the sampling data is essentially to judge the sample through the geometric information of the sample points in the point set and their nei...

AI Technical Summary

Problems solved by technology

However, this algorithm needs to extract the k-nearest neighbor points of the point and calculate the included angle, which requires a large amount of calculation. At the same time, the creation of R*-tree is very complicated, which increases the time complexity of the algorithm.

In "Segmentation of awrap-around modeling using an active contour" (Computer-Aided Design, 1997, 29(4): 299-320), Milroy et al. used a quadratic polynomial surface in the local coordinate system to estimate the curvature value of the point cloud data and find the curvature extreme point , to extract the boundary points, although the curvature extremum method can extract high-precision boundary feature points for irregular point cloud data, but this method needs to calculate the curvature value of each data point, the calculation process is very complicated, and The resulting curvature value is directly affected by the method of its estimation, and may sometimes be quite different from the real curvature

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