Point-cloud paintbrush selection system and point-cloud paintbrush selection method

Abstract

A point-cloud paintbrush selection system and a point-cloud paintbrush selection method are applied to a computer device. The method comprises the following steps: reading a point-cloud file from a database, converting the 3D coordinates in the point-cloud file into pixels and drawing a 3D point-cloud image; converting the world coordinate of each pixel in the 3D point-cloud image into a retinal coordinate to convert the 3D point-cloud image into a planar point-cloud image; dragging a paintbrush in a model space system in a point-cloud area needing to be selected by a user to form a paintbrush coverage area; acquiring the coordinate values of a group of pixels in the paintbrush coverage area from the model space system, and acquiring the contour coordinate value of the paintbrush coverage area; calculating and comparing the coordinate of each pixel in the planar point-cloud image and the contour coordinate value of the paintbrush coverage area to judge whether each pixel is in the paintbrush coverage area; and converting the color of each pixel in the paintbrush coverage area into a specific color. By implementing the system and the method of the invention, a point-cloud area in a 3D point-cloud image of a target product can be quickly and effectively selected with use of a paintbrush of a model space system and processed.

Description

technical field [0001] The present invention relates to a point cloud processing system and method, in particular to a point cloud brush quick selection system and method. Background technique [0002] The data scanned by scanning equipment is generally 3D point cloud data, and because of the influence of scanning equipment, lighting, environment, products and other factors, the scanned product image often has many noise points, resulting in unclear outline of the picture. In this way, the detection and processing of the image is very difficult, and it will also lead to low accuracy and large errors of the data obtained from the detection and processing. In the point cloud processing system, if there is no method to freely select the point area, the point cloud image cannot be detected and processed well, so the image processing is meaningless. In the existing point cloud processing system, only a large-scale search of the point cloud can be performed to select the point cl...

AI Technical Summary

Problems solved by technology

[0002] The data scanned by scanning equipment is generally 3D point cloud data, and because of the influence of scanning equipment, lighting, environment, products and other factors, the scanned product image often has many noise points, resulting in unclear outline of the picture

In this way, the detection and processing of the image is very difficult, and it will also lead to low precision and large errors in the data detected and processed.

In the point cloud processing system, if there is no method to freely select the point area, the point cloud image cannot be detected and processed well, so the image processing is meaningless

In the existing point cloud processing system, only a large-scale search of the point cloud can be performed to select the point cloud for rough image processing; when processing noise points in a small-scale point cloud area, it is easy to remove the points that need to be kept, which violates the purpose of perfecting the image; and Only the point cloud area outside the overall outline of the image can be processed

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