Reconstruction of Smooth Skin Surface Based on Arbitrary Distributed Sparse Point Clouds

Abstract

Adaptively reconstruction of the skin surface covering the skeletons of aircrafts, ships, high-speed trains, buildings with steel frames, etc. based on on-site measured point clouds is an important issue in both industry and construction. However, the skeleton has the characteristics of long and narrow shape and variable structure, resulting in a narrow and sparse distribution of measured point cloud on its surface with variable shape in different areas, which brings great challenges to the reconstruction of skin surface. In this paper, a method for accurately reconstructing the skin surface covering the skeleton structures based on the arbitrary distributed sparse on-site measured points of the skeleton surface is proposed. At first, a robust B-spline curve fitting method based on the least square principle is proposed to construct the boundary curves of the point cloud. Then, a Coons-B-spline surface fitting method based on the generated boundary curves is proposed to generate an initial skin surface. Next, the initial skin surface is considered as a curved thin plate with stiffness, and a method of surface deformation considering the target points of deformation and the tensile and shear stiffness of the surface is proposed to obtain the surface with high accuracy and good smoothness. To show the feasibility of the proposed method, simulations and experiments are carried out. It is proved that the proposed method can achieve better accuracy while maintaining smoothness.