【摘要】 针对三维点云去噪和简化很难用同一参数的问题,提出一种基于扩展的曲面变化度局部离群系数(ESVLOF)的散乱点云去噪与简化的统一算法。通过对ESVLOF定义的分析,给出了其性质。利用ESVLOF去噪过程中计算的曲面变化度和预设的相似度系数,构造出随曲面变化度增大而减小的参数γ,并将其作为点云简化的局部阈值,在点云去噪的同时进行点云简化。仿真结果显示,该方法能够保留原始数据的几何特征,与传统的三维点云预处理相比,效率提高近一倍。更多还原

【Abstract】 Since it is difficult to denoise and simplify a three dimensional point cloud data by a same parameter,a new unified algorithm based on the Extended Surface Variation based Local Outlier Factor( ESVLOF) for denoising and simplification of scattered point cloud was proposed. Through the analysis of the definition of ESVLOF,its properties were given. With the help of the surface variability computed in denoising process and the default similarity coefficient,the parameter γ which decreased with the increase of surface variation was constructed. Then the parameter γ was used as local threshold for denoising and simplifying point cloud. The simulation results show that this method can preserve the geometric characteristics of the original data. Compared with traditional 3D point-cloud preprocessing,the efficiency of this method is nearly doubled.更多还原