Object surface sampling point set boundary characteristic identification method based on local sample projective contour shape

Abstract

The invention provides an object surface sampling point set boundary characteristic identification method based on a local sample projective contour shape, and belongs to the field of digital design and manufacturing. The method is characterized in that each sample point of a sampling point set is subjected to normal estimation, all sample points are added with a label at the same time; through the labels, a sample point can be judged whether the sample point belongs to a boundary sample point state or a non-boundary sample point state, the labels of all sample points are initially set to be in a boundary sample point state; for each sample point of the sampling point set, if the labels of the sample points are in a boundary sample state, a local sample projective contour is established for the sample points along the normal direction of the sample points; then the projective points of the sample points along the normal direction are verified to judge whether the projective points fall into the contour set composed of recessed points and protruded points of local sample projection, if the projective points of the sample points do not fall into the set, the labels of the sample points are changed into a non-boundary sample point state, and finally the sub-set composed of sample points labeled as a boundary sample point state is output. The provided method can rapidly recognize the boundary sample points of an 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

But the algorithm needs to extract points k The calculation of the adjacent points and the included angle requires a large amount of calculation. At the same time, the creation of the R*-tree is very complicated, which increases the time complexity of the algorithm.

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

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