复杂曲面零件散乱点云特征点提取

复杂曲面零件的几何特征提取对加工质量的检测及逆向重构具有重要意义。提出了一种复杂曲面零件散乱点云特征点提取方法。首先,提出了基于高斯权重的邻域主成分分析的方法,通过估计每一点邻域的局部变化程度对点云模型进行初始标记;然后,采用基于标记的自动识别法实现特征点的提取;最后,通过特征点聚类的方式去除了特征点集中的异常点,完善了提取的点云特征。该方法直接操作于散乱点云,无需任何的拓扑连接信息。试验结果表明:该方法简单、有效,不需要过多地人为调节参数,特征点提取完整。

Extraction of Feature Point From Scattered Points in Complex Curved Part

Geometrical feature extraction of complex curved parts is of great significance to the detection of machining quality and the reverse reconstruction of CAD model.A method for extracting feature points from scattered points of complex surface parts is proposed in this paper.Firstly,a method of neighborhood principal component analysis based on Gauss weight is proposed,estimating the degree of local change of each point neighborhood to initialize labels.Subsequently,feature points are extracted by automatic recognition based on labels.Finally,the anomaly points of the feature points are removed by the method of feature points clustering.The algorithm directly operates on scattered point clouds,without any topological connection information.The experimental results show that the algorithm is simple and effective,without too much manual adjustment parameters,and the extracted feature points are complete.