Calculating method for three-dimension scanning point cloud real-time normal vectors

Abstract

The invention discloses a calculating method for three-dimension scanning point cloud real-time normal vectors. The method includes the steps that (1) a kinect camera is used for performing real object scanning to read point cloud data, and a KD tree is used for searching for neighborhood points among points in point cloud; (2) according to a principal component analysis (PCA), the searched neighborhood points are subjected to fitting to form a plane, and normal vectors of the fitting plane serve as the normal vectors of all points of the point cloud data; (3) normal vector weighted mean of a neighborhood point of each data point of the point cloud data within radius r can be figured out through a weighted mean algorithm; (4) normal vector evaluation confidence coefficient of each point is set, and the evaluation is performed by means of the normal vector weighted mean of each neighborhood point of each data point of the step (3); (5) a threshold value a of the normal vector confidence coefficient of each point is set, the normal vector confidence coefficient of each point is judged, and the normal vectors of the points are corrected. By means of the calculating method for the three-dimension scanning point cloud real-time normal vectors, overhead time of calculation of point cloud data normal vector estimation can be reduced, the correction function on the normal vectors of the points can be achieved, reorientation calculation of the point cloud normal vectors can be avoided, and the calculating complexity is reduced.

Description

technical field [0001] The invention relates to a method for calculating a real-time normal vector in a three-dimensional scanning point cloud, belonging to the technical field of computer three-dimensional modeling. Background technique [0002] In reverse engineering, the point cloud data on the surface of the object is mainly obtained through a 3D scanner, and these point cloud data are input into files for storage, which is called a 3D point cloud model. Usually, a 3D point cloud model cannot be directly applied to 3D modeling. The 3D point cloud model should be converted into a surface model, that is, to realize the surface reconstruction of the 3D point cloud model. [0003] A complete surface model is reconstructed from the point cloud model, and the calculation of the point cloud normal vector is an important step in the current surface reconstruction method. The existing point cloud normal vector calculation methods can be divided into three categories: [0004] ...

AI Technical Summary

Problems solved by technology

In 2012, S.Holzer, R.B.Rusu and M.Dixon proposed a real-time normal vector calculation method based on ordered point clouds at the International Conference on Intelligent Robots and systems. This method does not deal with The wrong calculation of the normal vector of some points of the boundary

It was also retrieved that in 2009, Radu Bogdan Rusu proposed in his doctoral thesis Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments, using the centroid of the point cloud as the viewpoint and resetting the normal vector of the point cloud. However, this When the method measures the surface normal vector of a complex model with sharp features, local normal vector errors may occur

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