Geometric featuer-based point cloud simplification method

Abstract

The invention provides a geometric feature-based point cloud simplification method. The steps are as follows: by constructing the moving least square of the nearest neighbor point set of 3D (three-dimensional) sample points, the normal of the sample points is calculated, and moreover, by analyzing the neighbor point set according to covariances, the curvature of the sample points is estimated; by analyzing the normal voted tensor of the sample points, the feature edge intensity of the sample points is calculated, and accordingly, point cloud data are decomposed into a strange-edge part and a non-strong-edge part; by utilizing MeanShift clustering, surface area geometric feature similarity clustering is carried out on the non-strong-edge part; according to a curvature threshold and a search radius, the strange-edge part and each cluster are resampled, and thereby curvature-adaptive simplification is completed. According to the curvature threshold and the search radius, the method carries out curvature-adaptive simplification guaranteeing flat area-sampling density on point cloud data. Therefore, the method can be adopted to carry out high-quality simplification keeping feature boundaries and curved surface details on point cloud data.

Description

technical field [0001] The invention relates to the fields of computer graphics, computer vision and reverse engineering, in particular to a point cloud simplification method based on geometric features. Background technique [0002] With the rapid development of 3D scanning acquisition technology, 3D point cloud data model has become a new digital media after one-dimensional sound data, two-dimensional image data and video data. In the fields of reverse engineering, innovative design of industrial products, digital entertainment, film and television animation, physical simulation, protection and restoration of cultural relics, point cloud data model has a wide range of applications, and has produced more and more far-reaching influence. Because the accuracy of 3D scanning equipment is greatly improved, the point cloud data obtained by scanning has extremely high precision, but also contains a lot of redundancy. Redundant point cloud data should be simplified in order to ef...

AI Technical Summary

Problems solved by technology

These point-based simplification algorithms are not based on the similarity of the geometric features of the surface area of ​​the point cloud data when clustering or dividing, but according to the spatial relationship of the sampling points, that is, ignoring the intrinsic geometric features of the anisotropy of the surface area; such clustering Classes inevitably lead to similar surface areas being divided into different clusters, which affects the simplification process and increases the error of the simplified point set

In the process of simplification, these algorithms do not distinguish the characteristic edges of sampling points, so they cannot guarantee that the simplified model retains sufficient sampling density on the characteristic edges.

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