基于区域聚类分割的点云特征线提取

Feature Line Extraction from a Point Cloud Based on Region Clustering Segmentation

提出一种非结构化点云特征线提取方法,其过程主要分为区域分割和特征检测两个阶段。在区域分割阶段,引入社会粒子群优化模糊C-均值聚类算法对点云数据进行区域聚类,得到边界清晰的各个分区,便于后续边界特征的提取;在特征检测阶段,对各个分区进行局部径向基函数曲面重构,以获取各个分区内采样点的曲率信息。提出基于平均曲率计算的局部特征权值,并通过局部特征权值和曲率极值法对特征点进行双重检测。并通过建立特征点的最小生成树构建特征曲线。对不同点云模型进行特征线提取实验,结果表明,本文方法既能够提取点云模型中的显著特征和尖锐特征,也能够很好地提取特征强度变化的曲线特征。

This study presents a novel methodology to extract feature lines from unorganized point clouds. In this study, the extraction of feature lines from point clouds is divided into two stages： region segmentation and feature detection. In the region segmentation stage, the social particle swarm optimization fuzzy C-means clustering algorithm is introduced to cluster the point cloud data; further, each partition is obtained with a clear boundary, which is beneficial to extract the boundary features. In the feature detection stage, local surface reconstruction that is based on the radial basis function is conducted for each partition. Additionally, the curvature values of the sampling points are calculated according to the established local implicit surface; further, local feature weights that are based on the mean curvature are proposed. The feature points can be identified based on the local feature weights using the curvature extremum method. Finally, the feature lines can be generated by establishing the minimum spanning tree of the feature points. Different point cloud models are selected to perform the feature line extraction experiments. The experimental results exhibit that the proposed method can extract significant and sharp features from the point cloud models along with the curve features with intensity variations.