点模型的多边滤波器降噪算法

为了更好地去除噪声,并保持模型的突出特征,提出了一种鲁棒的点模型多边滤波器降噪算法,该算法充分考虑了模型表面的法向量、曲率等内蕴几何量和噪声之间的关系。首先通过自适应选取最优邻域控制函数来将滤波窗口限制在顶点法向量相近的区域,以防止滤波后模型的收缩和过光顺;然后运用协方差矩阵分析的方法,在最优邻域内计算出各采样点的法向量和曲率;最后以采样点滤波参考平面为基准,分别平滑采样点的法向量和位置,即先对采样点的法向量进行多边平滑,然后根据新的法向量来多边平滑输出各采样点的位置偏移量,最后在法向方向上移动该顶点,以达到降噪的目的。实验结果表明,多边滤波器在有效地去除噪声的同时,还能较好地保持点模型表面的几何特征。

Multilateral Filter Denoising Algorithm for Point-sampled Models

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.