Research On Key Technologies Of Point Cloud Segmentation And Fusion

With the development of economy and the drastical competition in the market, lifecycle ofproduct is shorter and user demands are more personal. Reverse engineering is one of the methodsachieving rapid product development. Point cloud is original result got by reverse engineeringscanning. Point cloud segmentation and fusion is one important mehod for rapid and personal productdevelopment. Segmentation means extracting details from point cloud, and fusion refers to combiningone point cloud with others into new product model. Both technologies are studied. The main researchachievements are as follows:In order to establish the topology relations of local points, a K-nearest neighbors searchingtechnology based on cubes is researched. The layer-by-layer search method and the spatial spheremethod are improved by searching outermost layer directly and reducing the radius of spatial spheredynamically respectlvely. Then a threshold of point number in one cube is applied to synthesize thesetwo improved search methods and ensure the high efficiency for different cube side lengths.The normal vector of point cloud has great influence on point cloud segmentation and fusion. Inorder to get global consistent outward normal vector, a normal adjustment algorithm based onimproved minimum spanning tree and singular handling is proposed to adjust normal correctly andincrease efficiency. Minimum spanning tree is improved in traversal patterns and spread patterns toget higher efficiency. Two singular cases, which mean perpendicular normals and close-by surfaces,are considered. Increasing the neighbors region is used to handle perpendicular normals and removingambiguous neighbors is used to handle close-by surfaces. Three methods, which refer to neighborsincreasing method, normal points removing method and combination method, are given to removeambiguous neighbors. Among these, combination method takes advantages of other two methods andthus achieves best remove effect.For the purpose of getting feature segment line, the Snake model of point cloud is proposed andapplied to extract segmentation boundary of point cloud. The application of balloon force is extendedfrom2D image to3D point cloud in Snake to design external energy. Snake model can convergecorrectly even if the initial contour is far away from the target contour when using balloon force.Substep Snake, subsection Snake and parameter setting are involved to get better convergence effect.The proposed model is insensitive to initial position and can be less affected by noises and otherfeatures. It lays the foundation for further studies. Improved Minimum Spanning Tree realizes extraction of interior points. For proper finish ofimproved Minimum Spanning Tree, segment line is expanded towards both sides to bandedsegmentation boundary before extraction. After extraction, banded segmentation boundary is split tosingle regions. The algorithm can avoid over segmentation or under segmentation and generatesmooth segmentation boundaries. Compared with the Level Set segmentation algorithm, the algorithmof this paper can segment point cloud more efficiently.The transition surface creation algorithm is presented to research point cloud fusion technology.Radial basis function is adopted to construct transition surface. Transition region, the desired part oftransition surface, is generated by the point cloud segmentation algorithm based on Snake model andimpromved Minimum Spanning Tree. Not only is it easy to operate, but also the segmentation result isaccurate. The locally optimal projection algorithm is applied to uniform resample for transition region.Factors affecting transition region are analyzed to get corresponding dealing methods.