A small-scale point cloud noise denoising method based on threshold segmentation

Abstract

A small-scale point cloud noise denoising method based on threshold segmentation belongs to the field of computer vision and reverse engineering, and relates to a small-scale point cloud noise denoising method based on threshold segmentation. The method adopts the way of laser combined with binocular vision, and uses binocular camera to capture the point cloud data which represents the surface information of the measured object. According to the principal component analysis, the coordinates of the point cloud data are transformed, and the two principal component directions of the point cloud data are obtained respectively. Then, according to the direction of the point cloud data, several grids are divided, and the median value of Z coordinates of all points in the grid is solved. Finally,the segmentation threshold is set to filter the data larger than the threshold value, and the data smaller than the threshold value is filtered bilaterally, so that the noise of the small-scale pointcloud can be removed. On the basis of bilateral filtering, the invention applies the threshold segmentation method to improve the limitation of the existing small-scale point cloud noise denoising, and overcomes the problem that the traditional filtering method cannot effectively remove the boundary noise points.

Description

technical field [0001] The invention belongs to the field of computer vision and reverse engineering, and relates to a small-scale point cloud noise denoising method based on threshold segmentation. Background technique [0002] With the continuous development of the aviation industry, the production requirements for large-scale aviation parts are getting higher and higher. Because the reverse engineering technology has the characteristics of simple operation, real-time acquisition, and convenient analysis, it is imminent to develop reverse modeling technology for aircraft parts. [0003] As the first step of reverse engineering, point cloud acquisition plays a vital role. In the process of acquiring point cloud data by the laser scanning system, the errors caused by the measured object itself and the measurement environment will generate impulse noise points. These noises can generally be divided into two types: large-scale noise and small-scale noise. The large-scale nois...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the limitations of the existing small-scale point cloud noise denoising method in the point cloud data processing process, the present invention invented a small-scale point cloud noise denoising method based on threshold segmentation, using laser combined with binocular vision , the purpose of which is that traditional filtering algorithms are easily affected by neighborhood points. When there are many and dense noise points near the subject point cloud, the filtering effect is not very obvious, especially for large-scale part line laser scanning point clouds, outside the boundary of the measured part In the case of some noise points

This method overcomes the problems that the traditional filtering method cannot effectively remove these noise points, and it is difficult to apply to the field measurement process. The method has broad application prospects

Images