Multilateral Filter Denoising Algorithm for Point-sampled Models
点模型的多边滤波器降噪算法

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.