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

Screened Poisson Surface Reconstruction has a good performance among the state-of-art surface recon-struction 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 equa-tion. 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. Ex-periments show that such an estimator that makes use of both global and local points distribution can effectively remove noise and outliers in the reconstruction. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study improved the Screened Poisson Surface Reconstruction algorithm by using an adaptive bandwidth Gaussian kernel density estimator, which effectively removes noise and outliers in the reconstruction process.