CAJ | 학술논문

针对目前基于核密度估计的去噪算法在核函数参数的选取上未能充分体现散乱点云数据的表面特征，提出一种改进的去噪算法。以当前点法向量与其邻域内点的法向量构造的差向量作为核函数的参数，引入面积权重进行光顺，通过构造空间单元格的最大连通域剔除离群点，结合K-近邻搜索建立点云之间的拓扑关系，以改进的高斯函数作为核函数计算当前点的影响值。实验结果表明，该算法在有效去除表面噪声和离群点的同时，能够较好保留模型的细节特征。
침대목전기우핵밀도고계적거조산법재핵함수삼수적선취상미능충분체현산란점운수거적표면특정，제출일충개진적거조산법。이당전점법향량여기린역내점적법향량구조적차향량작위핵함수적삼수，인입면적권중진행광순，통과구조공간단원격적최대련통역척제리군점，결합K-근린수색건립점운지간적탁복관계，이개진적고사함수작위핵함수계산당전점적영향치。실험결과표명，해산법재유효거제표면조성화리군점적동시，능구교호보류모형적세절특정。
An improved de-noising algorithm was proposed considering that the current algorithms based on kernel density esti-mation fail to fully reflect the scattered point cloud on the selection of kernel function parameters.Difference vectors constructed with normal vectors of the point and that of its neighborhood were used as the kernel function parameters while introducing area weight to smooth.Maximally connected domain of the space grid was obtained to eliminate outliers,and K-nearest neighborhood search was combined to establish the topological relations,and the improved Gaussian function was taken as kernel function to calculate influence of the point.The experimental results indicate that the algorithm can effectively remove surface noise and out-liers and preserve detail features of model.