Surface reconstruction from point cloud using a semi-Lagrangian scheme with local interpolator

TL;DR

This paper introduces a semi-Lagrangian level set method with local interpolation for efficient surface reconstruction from point clouds, improving accuracy and computational speed without prior connectivity information.

Contribution

It presents a novel coupling of semi-Lagrangian schemes with local interpolators for surface reconstruction, enhancing efficiency and accuracy over traditional global methods.

Findings

Effective in 2D and 3D reconstructions

Faster computation with parallel algorithms

Improved accuracy using local interpolators

Abstract

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 whose main novelty consists in its coupling with a local interpolator instead of a global one, with the aim of saving computational costs. In particular, we resort to a multi-linear interpolator and to a Weighted Essentially Non-oscillatory one, to improve the accuracy of the reconstruction. 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. A preprocessing of the point cloud data is also proposed to set the parameters of the method. Numerical tests in two and three dimensions are presented to evaluate the quality of the approximated solution and the efficiency of the algorithm in terms of computational time.