Robust resistance to noise and outliers: Screened Poisson Surface Reconstruction using adaptive kernel density estimation

Abstract

Screened Poisson Surface Reconstruction has a good performance among the state-of-art surface reconstruction algorithms in obtaining a triangle mesh from oriented points. In order to better deal with nonuniform point clouds, Screened Poisson Surface Reconstruction uses B-spline functions with a fixed support for kernel density estimation to construct a vector field for solving the screened Poisson equation. In this paper, an adaptive bandwidth Gaussian kernel density estimator is applied, which reduces the bandwidth where the density is low, and increases the bandwidth where the density is high. Experiments show that such an estimator that makes use of both global and local points distribution can effectively remove noise and outliers in the reconstruction.