Three-dimensional scanning point cloud compressing method

Abstract

This invention relates to a method for reducing dot clouds of 3-D scanning, which carries out grid division to the dot-clouds, and the grid is the smallest exterior square body of a dot-cloud, the side length is L, then divides it into sub-grids with the side length of DalphaL3 / n along the directions of three coordinate axises, and alpha is the dilution ratio decided by the design, n is the dot number of the cloud, then dot set of the neighbour domain of Pi in the adjacent sub-grid of each dot KNB9Pi)={X1, X2apostrophe, Xn} is taken,takes the least square approximation plane of KNB9Pi) as the micro-cut plane S(Pi), takes Pi as the core of KNB(Pi), sets the distance from jth adjacent point of Pi to S(Pi) dj, and the distance to Pi lambdaj, then the function is fj(Pi)=dj / lambdaj, the curvature function of Pi is f(pi)=1 / kSigmafj(Pi) so as to obtain the curvature of every data point Pi, finally acquires the mean curvature in every small grid and gets the difference of the curvature of a point and the mean curvature and points smaller than the needed accuracy are selected as the points of the grid and others are deleted.

Description

technical field The present invention relates to a method for streamlining point clouds of three-dimensional scanning, in particular to a method for streamlining point clouds based on a curvature-based three-dimensional grid method. Background technique The accuracy of 3D scanning equipment is getting higher and higher, and the speed of data capture is increasing sharply. Using non-contact point cloud mapping method to obtain point cloud is faster, and there are many measurement data points, which can generate thousands of data points per second. Clearly expressing objects has become more and more widely used. Although non-contact optical scanners can obtain very dense point clouds, not all data points can be used for surface reconstruction. These redundant point cloud data will lead to low efficiency of computer operation, storage and operation, it will take more time to generate surface model, the robustness of program algorithm will deteriorate, so its use is also chall...

AI Technical Summary

Problems solved by technology

This method is simple and efficient, but since the size of the bounding box is arbitrarily specified by the user, the accuracy between the constructed model and the original point cloud data cannot be guaranteed

In 2001, Sun W, Bradley et al improved this method by using local surface interpolation to automatically determine the size of the bounding box, but the disadvantage is that this method can only be applied to simple surface data and the efficiency is relatively low, and the applicability is not high

The algorithm is driven by error, without specifying the number of data points in advance, and can directly simplify the measurement data of the unknown surface equation, but the algorithm does not have self-organization characteristics, and cannot deal with internal feature points, and the final simplified data is also some scattered data rather than regular grid data

Images