Processing Algorithms On Scattered Point Cloud

With the development of three-dimensional laser scanning technology, people can quickly and accurately get sample points from the surface of object; but these data points are very large, so it is an enormous challenge to deal with these points real-time and efficiently. Processing the points cloud accurately and efficiently and generating a realistic physical model ultimately will be a research focus. Dissertation studied the scattered point cloud processing and related technologies in this background, and the main research contents are as follow:1. Space bounding box of side length blocking strategy estimation methods are improving. First of all, using a given side length for the first time to divide the grid and calculate the blackbody ratio; and then comprehensive consider the scope of data set, total number of points, k-nearest neighbors points and blackbody ratio for the second division, for a variety of point cloud data making estimate of side length more reasonable, the calculation more efficient.2. Analyzing the methods of data simplification base on point distance and curvature, and then proposes a new algorithm for data simplification base on vector angle over considering the efficient and accuracy of data simplification; Firstly, calculate the center of gravity of sample point and the neighborhood points, and calculate the vectors of sample point to the neighborhood points and the vector of sample point to the center of gravity of points, then calculate the angle between vectors and take the average of these angles as the angle of sample point. According to the angle of the sample point to identify the feature point; The experiment result illustrate that the algorithm can discriminate the feature points and boundary points, keep the geometrical features and boundary points of the surface, and make data simplification more efficient.3. Analyzing methods of data extraction from scattered data, and then proposing an algorithm for data extraction according to the characteristic of boundary points and nearest neighborhood points; Firstly, calculating the center of gravity of nearest neighborhood points, and then identifying the boundary points according to the ratio between the distance of the sample point to the center of gravity point and the distance of the sample point to the farest point of nearest neighborhood points.The experiment result illustrate that the algorithm can extract the boundary points more efficient and exactly.