Efficient Surface Reconstruction using Generalized Coulomb Potentials

We propose a novel, geometrically adaptive method for surface reconstruction from noisy and sparse point clouds, without orientation information. The method employs a fast convection algorithm to attract the evolving surface towards the data points. The force field in which the surface is convected is based on generalized Coulomb potentials evaluated on an adaptive grid (i.e., an octree) using a fast, hierarchical algorithm. Formulating reconstruction as a convection problem in a velocity field generated by Coulomb potentials offers a number of advantages. Unlike methods which compute the distance from the data set to the implicit surface, which are sensitive to noise due to the very reliance on the distance transform, our method is highly resilient to shot noise since global, generalized Coulomb potentials can be used to disregard the presence of outliers due to noise. Coulomb potentials represent longrange interactions that consider all data points at once, and thus they convey globa...

• Type: Conference
• Year: 2007
• Where: VIS
• Authors: Andrei C. Jalba, Jos B. T. M. Roerdink
• Keywords: Adaptive Method, Coulomb Potentials, Fast Convection Algorithm, IEEE Visualization, VIS 2007, Visualization