Extracting Quadric Surface In The Processing Of Point-sampled Models

With the proliferation of scanning devices and the rapid development of computer science,data acquisition becomes more and more convenient.Point-sampled models are very suitable to represent and process large 3D models because of its easy data structure and flexible expression.As a consequence,the point-sampled model becomes research focus in the fields of Computer Graphics and Computer Aided Design.Extraction of quadric surfaces,as an important part of digital geometry processing,is receiving more and more attention in the latest years.Especially in reverse engineering, is a key technique because most of product surfaces can be expressed by quadric surfaces.This thesis focuses on quadric surface extraction from point-sampled models.Main work in this thesis can be summarized as follows:1.This thesis first introduces the research significance and summarizes typical algo-rithms.Then we present the definition and categories of surface extraction, and introduce the algorithm based on Least-Squares in detail.2.This thesis introduces the categories of quadric surface and two kinds of recognition methods.Then two robust statistics methods,e.g.Random Sample Consensus(RANSAC) algorithm and method based on Locally Optimal Projection(LOP) operator are presented.3.A new algorithm based on both RANSAC algorithm and Gaussian image are pro-posed to extract cylinders and cones from point-clouds with noise and outliers.The method recognizes the inliers of cylinders and cones based on RANSAC algorithm.Then the in-liers are smoothed to reduce the outliers and noise using LOP operator.Finally,based on Gaussian image method, the Gaussian image of a cylinder or a cone is obtained and then plane cluster analysis is applied to determine the surface type and compute the direction and position of the rotational axis. Results show the robustness even in the presence of many outliers and a high degree of noise.