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在计算机辅助设计与逆向工程应用中,针对缺乏拓扑连接关系的点云数据,提出了基于经验模态分解(EMD)的点云数据平滑与增强算法。首先,以点云模型的拉普拉斯矩阵坐标与法向的内积作为EMD输入信号,提取点云模型输入信号的极值点作为插值节点计算信号的上下包络;然后,为实现特征保持的EMD信号分解,通过检测点云数据上特征点,并在计算信号上下包络的过程中作为约束,克服传统EMD算法无法保持特征的局限;最后,迭代地从输入信号中减去上下包络的均值得到内蕴模态函数(IMF)和余量,并通过设计滤波器实现了点云数据平滑和增强。实验结果表明,本文算法有效地将EMD推广到三维散乱点云数据中,扩大EMD在三维几何中的应用范围,并在点云数据平滑和增强方面取得了很好的效果。
In computer aided design and reverse engineering applications, a point cloud data smoothing and enhancement algorithm based on Empirical Mode Decomposition (EMD) is proposed for point cloud data lacking in topological connection. First, take the Laplacian matrix coordinate and the normal inner product of the point cloud model as the EMD input signal, extract the extremum points of the point cloud model input signal as interpolation nodes to calculate the upper and lower envelopes of the signal; and then, to achieve the feature retention EMD signal decomposition, by detecting point cloud data feature points, and in the calculation of the signal envelope up and down as a constraint, to overcome the traditional EMD algorithm can not maintain the characteristics of the limitations; Finally, from the input signal to subtract the upper and lower envelope The mean of IMFs and margins are obtained and the smoothing and enhancement of point cloud data is achieved through the design of filter. The experimental results show that the proposed algorithm effectively extends EMD into 3D scattered point cloud data, expands the application range of EMD in 3D geometry, and achieves good results in the smoothing and enhancement of point cloud data.