The Research On Simplification Of Scattered Point Clouds And Triangular Mesh Surface Reconstruction

The entity model building based on the sampling data is an important part in the computer aided design, which plays an important role in many fields, such as manufacturing industry, computer visualization, digital media and medical image. With the improvement of measuring equipment accuracy and the deepening of the research, it is still need to be improved when constructing the model based on the point quickly and efficiently. Dissertation studied the simplification of scattered point cloud and triangular mesh reconstruction. The main research contents are as follows:1. Reviewing the importance of building up the topological relationship of the scattered point cloud, illustrate the principle and feature of related algorithms; Summarize the method of the estimation of normal vector, the characteristics of the algorithm are analyzed and compared qualitatively, and generalize the scope of application of the algorithm.2. Discussing the importance of data simplification; On this basis, Due to geometric feature always being lost excessively in Kim’s simplification process of scattered point cloud, an improved simplification method is proposed. At first, principal curvature of points in point cloud are estimated by the least square parabolic fitting. Then an error metric based on Hausdorff distance of principal curvature is used to keep and extract the feature points. Finally, through testing and analyzing some measured data with different characteristics, the results show that the presented method achieve more reasonable effect of simplification.3. In the part of triangular mesh reconstruction, the author compares the common method in the areas of triangular mesh surface reconstruction. Based on geometrical distribution of the point set, a regional growing triangulation algorithm is presented:Firstly, the data point is divided into three categories:flat point, high curvature point and boundary point. Then according to the order of the simple to numerous logarithmic stronghold to reconstruct. And in order to improve the robustness of the algorithms, in the process of reconstruction introducing the inside edge table, and reasonable principles of filtering adaptively some points. The experimental results also show that the algorithm is effective for scattered points clouds: which can produce quality meshes while preserving features of the geometrical shapes, and rarely appears topology mistakes.