Point cloud surface reconstruction method and system based on circumscribed ball strategy region growth

Abstract

The invention discloses a point cloud surface reconstruction method and system based on circumscribed sphere strategy region growing, and the method comprises the steps: firstly selecting three pieces of point cloud data from a point cloud data set, and connecting the three pieces of point cloud data into a triangle, thereby forming a seed triangle; secondly, based on region growing search of a circumscribed ball, performing region growing on the basis of the seed triangle, and forming an optimal triangle; after the seed triangle and the optimal triangle are obtained, determining the connection direction of the optimal triangle and the seed triangle to ensure the ductility and smoothness of the reconstructed surface; after selection of the extension direction is completed, storing information of edges of a triangle, storing the information of the edges forming the triangle by constructing a data structure of an edge list, it is guaranteed that all point clouds meeting conditions are triangularized, extended points do not need to be further extended, and the reconstructed point clouds are prevented from being reconstructed again; and finally, circulating until no extensible optimal triangle exists, and finishing point cloud surface reconstruction.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a point cloud surface reconstruction method and system based on circumscribed ball strategy area growth. Background technique [0002] The use of point cloud in 3D vision technology is becoming more and more extensive. Point cloud data acquisition tools are developing rapidly, data acquisition costs are reduced and efficiency is improved, which makes point cloud technology widely used in machine vision, cultural relics restoration, automatic driving, drones and other fields. . The main work of point cloud registration is to rotate and translate point clouds in different coordinate systems, and finally integrate them into a complete point cloud in the same coordinate system. According to the nature of the target point cloud and the source point cloud, it is generally divided into non-rigid body registration and rigid body registration. Non-rigid body registrat...

AI Technical Summary

Problems solved by technology

The point cloud data collected by 3D laser point cloud is usually in a scattered state in practical applications, without obvious rules. If the original data is directly used for surface reconstruction, it will take up a lot of resources and storage space, and the reconstruction effect is not good.

[0004] However, due to instrument measurement, environmental factors, and human operation, the original point cloud data set has different point cloud quality and cannot be avoided. There may be noise, holes, sharp bulges, and uneven point distribution.

The disorder between points is difficult to find an effective topological connection method to restore the surface shape of the original object. A correct topological connection relationship can show the surface configuration and topology of the original object, and a large mass point cloud The time complexity and space complexity of the data are relatively large, and a large amount of computer resources will be consumed in the process of surface reconstruction

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