A local-optimizing surface reconstruction method based on point cloud data

Abstract

Abstract Surface reconstruction based on discrete point cloud data is a frequently encountered problem in the field of CAD/CAGD and computer graphics. In this paper, a local-optimizing surface reconstruction method based on discrete point cloud data is proposed. Firstly, the moving least square method is used to fit the discrete point cloud data to generate a gridded point clouds. Then the global interpolation method is used to generate the initial NURBS surface based on the gridded point clouds. Additionally, the knots are inserted into the regions with excessive errors. Ultimately, a multi-objective optimization function is established to optimize the regions with excessive errors and their neighbourhoods simultaneously. Compared with the existing methods, the proposed method can improve the fitting accuracy of the reconstructed surface locally and has higher efficiency.