Neighborhood access method and device of grid data structure, equipment and medium

Abstract

The invention discloses a neighborhood access method of a 5D texture grid data structure. The 5D texture grid data structure is used for four-corner grid reconstruction of point cloud data and comprises a plurality of half-side four-corner grids; each half-side four-corner grid comprises four vertexes, half sides and a four-corner surface patch; the vertex stores 3D position coordinates (x, y, z),normal vectors and colors of corresponding points Pij in the point cloud data and pointer information of the half sides taking the points Pij as initial vertexes; and the half sides store pointers orindexes of initial vertexes, end vertexes, adjacent surfaces, upper half sides, lower half sides and opposite half sides; when any vertex Si in the 5D texture grid data structure starts neighborhoodaccess this time by taking the vertex Si as an initial vertex. The method comprises the following steps of: judging whether the initial vertex is a boundary point or not, and under the condition thatthe initial vertex is the boundary point, returning to a null state; and under the condition that the initial vertex is not the boundary point, searching all neighborhood vertexes in the grid data structure with the access radius r by taking the initial vertex as the center so as to establish an access mode between a 3D point and neighborhood points.

Description

technical field [0001] The invention belongs to the technical field of computer graphics and computer vision, in particular to a gridded access method for point cloud data. Background technique [0002] In recent years, with the improvement of computer hardware performance and the reduction of price, it has been widely introduced in the scanning system. The general method is to use the point cloud acquisition device to obtain the point cloud of the object (that is, a collection of points composed of multiple three-dimensional discrete points), and then input the point cloud data into the computer, and execute the corresponding software to perform various processing on the point cloud data, such as computer Computer Aided Verification (CAV), reverse engineering (Reverse Engineering, RE), automatic generation of finite element grids, computer graphics processing and pattern recognition (such as face recognition), etc. [0003] Constructing a point cloud mesh surface is one of...

AI Technical Summary

Problems solved by technology

[0005] At present, traditional 2D vision or 3D visual recognition methods cannot solve the problem of universal and accurate recognition of massive disordered posture workpieces, and it is difficult to meet the disordered sorting needs of e-commerce logistics

When the workpieces to be scanned have a large attitude inclination and are stacked in disorder, such as identifying instant noodles in barrels placed on the side of the turnover box, the traditional 2D method needs to make multiple templates with different viewing angles for matching and positioning. In this way, when facing a large number of workpieces, establish The workload of the template is too large; when the workpieces to be inspected are closely placed, it is difficult for the 3D vision method to segment different objects in the scene, and it is difficult to adapt to the workpieces to be inspected with small changes in object shape, such as identifying closely arranged mobile phone packages in a turnover box box

Only by recognizing the complete shape of the workpiece (including the side and bottom surface), can the workpiece be placed accurately and avoid damage to the workpiece when grabbing the precious workpiece. The traditional 2D / 3D visual recognition methods can only be based on the local information on the upper surface of the workpiece. Recognition, it is difficult to judge the complete shape of the workpiece to be measured, and it is difficult to adapt to accurate stacking

[0006] The above problems are mainly due to the fact that the existing visual recognition methods are limited to the data structure representing three-dimensional objects. Thanks to the matrix form of 2D images, the image processing methods in 2D vision are very mature, but it is difficult for 2D images to represent the three-dimensional objects. All-round information; in 3D vision processing, most of the existing 3D image processing methods are based on disordered point clouds, and it is difficult to achieve ordered neighborhood access similar to 2D, so that 2D texture features cannot be used; 3D images based on ordered point clouds Processing method, since the ordered point cloud can only express the upper surface information of the workpiece, it cannot express the bottom surface and side surface, nor can it realize the expression of the complete shape

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