A sharpness dependent filter for mesh smoothing

Abstract

In this paper, we propose a sharpness dependent filter design based on the fairing of surface normal, whereby the filtering algorithm automatically selects a filter. This may be a mean-filter, a min-filter, or a filter ranked between these two, depending on the local sharpness value and the sharpness dependent weighting function selected. To recover the original shape of a noisy model, the algorithm selects a mean-filter for flat regions and a min-filter for distinguished sharp regions. The selected sharpness dependent weighting function has a Gaussian, Laplacian, or El Fallah Ford form that approximately fits the sharpness distribution found in all tested noisy models. We use a sharpness factor in the weighting function to control the degree of feature preserving. The appropriate sharpness factor can be obtained by sharpness analysis based on the Bayesian classification. Our experiment results demonstrate that the proposed sharpness dependent filter is superior to other approaches for smoothing a polygon mesh, as well as for preserving its sharp features.