Scattered Point Cloud Data Reduction Method Based on Local Surface Variation Factor

Abstract

The invention discloses a local curved surface change factor based scattered point cloud data compaction processing method. The local curved surface change factor based scattered point cloud data compaction processing method comprises the steps of 1 reading measured point cloud data, 2 calculating a central point of a point cloud, 3 searching dynamic K neighborhood points of the central point based on cubic grids and accordingly establishing the topological relation of scattered point cloud, 4 adopting a variance component method to calculate curved surface change factors of a k neighborhood of the central point, 5 determining the compaction rate of each cubic grid in the k neighborhood of the central point and performing even compaction in within a k neighborhood range. The topological relation of the scattered point cloud is established by establishing the dynamic K neighborhood point information of the scattered point cloud. Complicated curvature calculation is replaced by the curved surface change factors. The compaction ratio is adjusted according to the curved surface change factors Xi, even compaction within the k neighborhood range is achieved, the detail characteristic of high curvature can be protected, and planar characteristic of low curvature is also protected when the compaction degree is high. Point cloud data processing and curved surface reconstruction efficiency and accuracy are improved.

Description

technical field [0001] The invention belongs to the technical field of computer three-dimensional modeling in reverse engineering. In order to improve the efficiency of three-dimensional modeling, it mainly aims at the simplified processing of the three-dimensional scattered point cloud data obtained by product digital measurement equipment. Background technique [0002] Optical non-contact measuring equipment has the advantages of fast data acquisition and rich data. It can not only accurately contain all the shape feature data of the measured object, but also include information such as normal direction and reflexive strength. It is more and more widely used in the application neighborhood of reverse engineering. [0003] With the development of optical three-dimensional measurement technology, the resolution of measurement equipment is getting higher and higher, and up to millions of point data can be obtained in one measurement acquisition, but not all point data can be ...

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Problems solved by technology

[0010] On the one hand, in terms of establishing the k-neighborhood information of scattered point clouds, there are mainly three types of methods: one is to use the sampling point set Voronoi diagram to realize the k closest point search, but it is mainly used in two-dimensional scattered point sets, and its calculation The amount is still large, and in the field of 3D applications, it is not suitable for all types of point cloud data; the second is to search the k-neighborhood based on the hierarchical structure of the tree, such as KD-Tree, octree and other methods, but these methods are different in searching When the adjacent leaf nodes of the layer are connected, it will become particularly complicated. If the neighbor points and sampling points are in different hierarchical structures, the search range will be expanded at this time, resulting in a greatly reduced search efficiency; the third is based on small cube grids Search the k neighborhood, use different space division strategies to divide the point cloud space into many cubic grids of the same size, and quickly search the k neighborhood according to a certain search expansion method, and these methods are all searched according to a fixed k value, It is easy to make the search loop fall into an infinite loop, the search efficiency is slow, and at some boundary positions, processing point cloud data with fixed k neighbors will lead to greater errors, which will affect the speed and speed of the overall point cloud data reduction. precision

[0011] On the other hand, in terms of estimating curvature, there are mainly two methods: one method is to establish other models to replace the original 3D point cloud data to estimate curvature, such as quadratic parameter surface model and triangular mesh model in the local coordinate system etc. This method is complex and cannot guarantee the accuracy of the converted model. The other method directly uses the original point cloud model to estimate the curvature, most of which are limited to the conditions of the regular point cloud, and directly estimate the scattered point cloud model. Curvature calculation is complex, involving many large parameters and matrix calculations, which will seriously affect the overall efficiency of point cloud data reduction

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