Surface Reconstruction From Point Cloud using a Semi-Lagrangian Scheme with Local Interpolator

We propose a level set method to reconstruct unknown surfaces from point clouds, without assuming that the connections between points are known. We consider a variational formulation with a curvature constraint that minimizes the surface area weighted by the distance of the surface from the point cloud. More precisely we solve an equivalent advection–diffusion equation that governs the evolution of an initial surface described implicitly by a level set function. Among all the possible representations, we aim to compute the signed distance function at least in the vicinity of the reconstructed surface. The numerical method for the approximation of the solution is based on a semi-Lagrangian scheme coupled with a local interpolator. In particular, we resort to a multi-linear interpolator and to a Weighted Essentially Non-oscillatory one, to improve the accuracy of the reconstruction. An analysis of the parameters employed in the model is given, focusing in particular on the effect of the curvature regularization, and on the presence of noisy data. Special attention has been paid to the localization of the method and to the development of fast algorithms that run in parallel, resulting in faster reconstruction and thus the opportunity to easily improve the resolution. Numerical tests in two and three dimensions are presented to evaluate the quality of the reconstruction and the efficiency of the algorithm in terms of computational time.