Traditional point cloud denoising algorithm can weaken the surface characteristics. In order to solve it, a denoising algorithm for point model surfaces is proposed based on orthogonal projections constraint. Orthogonal projection based on the moving least squares algorithm, according to the relationship between the point model and the projection onto the moving least squares, the moving distance weighting function is constructed. In order to avoid shrinkage, a new curvature weighting function is presented. By applying bilateral filtering algorithm to filter the normal, and in combination with the two weighted factors. The position offset of the point model is obtained. Experimental results show that the algorithm can denoise the noise efficiently while preserving the high-frequency features of the surface, and can avoid shrinkage and point drifting.%传统点云消噪算法会削弱曲面特征.为此,提出一种基于正交投影约束的点模型去噪算法.利用移动最小二乘曲面投影的思想,根据采样点与其在MLS曲面上正交投影点之间的关系构建移动距离权重函数,为防止模型收缩,给出曲率权重函数,通过双边滤波器确定滤波方向,结合移动距离权因子与曲率权因子确定采样点滤波距离.实验结果表明,该算法在消除点云噪声的同时,能保持点云高频结构特征,避免模型的收缩和顶点漂移.
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