New algorithms based on data reorganization for 3D point cloud data partition

With the development of 3-D imaging techniques, three dimensional point cloud partition becomes one of the key research fields. In this paper, two data partition algorithms are proposed. Each algorithm includes two parts: data re-organization and data classification. Two methods for data re-organization are proposed: dimension reduction and triangle mesh reconstruction. The algorithm of data classification is based on edge detection of depth data. The edge detection algorithms of gray images are improved for depth data partition. As to the triangulation method, the data partition is realized by region growing. The simulation result shows that the two methods can achieve point cloud data partition of standard template and real scene. The result of standard template shows the total error rates of the two algorithms are both less than 3%.