From laser point clouds to surfaces: Statistical nonparametric methods for three-dimensional reconstruction

This paper presents a new method for the reconstruction of three-dimensional objects surveyed with a terrestrial laser scanner. The method is a 2.5D surface modelling technique which is based on the application of statistical nonparametric regression methods for point cloud regularization and mesh smoothing, specifically the kernel-smoothing techniques. The proposed algorithm was tested in a theoretical model-simulations being carried out with the aim of evaluating the ability of the method to filter random noise and oscillations related to the acquisition of data during the fieldwork-and the results were satisfactory. The method was then applied, as a reverse engineering tool, to real-world data in the field of naval construction. A precise solution to the problem of obtaining realistic surfaces and sections of large industrial objects from laser 3D point clouds is provided, which has proved to be efficient in terms of computational time.