The Research Of Three Surface Reconstruction Methods Based On Point Clouds

In industrial design and manufacture field, it always needs to digitalize the existing objects or parts, and build their mathematical models: firstly, using the 3D scanner to sample the object, and obtain a large number of points with varieties types of data, namely point cloud model. It is a geometric model using a point as a primitive and has many excellent properties such as simple structure, compact in space and efficient in representing large scale geometry models with rich details. However, point cloud model usually can not be used directly; designers use different methods to reconstruct surface models for different purposes. This process is well known as Reverse Engineering. With the development of the technology, more and more 3D scanners with high capacity come out, all these lead to many new point cloud models, some of which have large amount of data and high density. All these require more robust and efficient surface reconstruction algorithm.This thesis focuses on surface reconstruction from point cloud model, and have discussed and extended three common methods: Moving Least Squares method (MLS), Radial Basis Functions method (RBF) and Multi-level Partition of Unity method (MPU). All these methods have different applied areas and resulting surface, since they use different approaches to process the point cloud model. Contributions of this thesis mainly include following aspects:1. Illustrating and optimizing three methods: The paper has illustrated the basic ideas and mathematical models of the three methods, and also given some optimization approaches for them.2. Extended resulting surface gained from the above methods to edit operations: Having advance discussion of the three methods about edit operations on surface such as Boolean based on MPU method and Blend Operation based on RBF method.3. Comparing the three Methods: analyzing the performance and comparing the results of the three methods, all these make the designers more clearly about the strengths and shortcomings among them.