In order to remove the noise efficiently and preserve the sharp features of the models

a denoising algorithm of a robust multilateral filter for point-sampled models is presented.The algorithm takes into account the relationship between noise and underlying geometric information

such as normal and curvature.First

by choosing a control function for a local adaptive optimal neighborhood

the filter window is set in the region with similar normals to avoid the problem of shrinkage and over-smoothing.Second

normals and curvatures of vertices in the optimal neighborhood are estimated by covariance matrix analysis.Third

based on the filter reference plane

normals and positions of surface points are smoothed respectively

i.e.

the normals of surface points are calculated firstly by using multilateral filter

then

by applying multilateral filter again

the position offsets of sampling points are obtained

finally

each point is moved in the direction of normals being smoothed.Experiments show that the multilateral filter can remove the noise efficiently while preserving the geometric features of the surface.