A shape-based segmentation method for mobile laser scanning point clouds
A shape-based segmentation method for mobile laser scanning point clouds
复制标题
一种基于形状的移动激光扫描点云分割方法
DOI：
10.1016/j.isprsjprs.2013.04.002
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发表时间：
2013-07-01
影响因子：
12.7
通讯作者：
Dong, Zhen
中科院分区：
文献类型：
--
作者：
Yang, Bisheng;Dong, Zhen
将城市场景的移动的激光点云分割成对象是后处理的重要步骤（例如，点云（Point Cloud）城市场景中的点云包含大量的物体，这些物体具有明显的尺寸可变性、复杂和不完整的结构以及孔洞或可变的点密度，这给移动的激光点云分割带来了巨大的挑战。本文通过提出一种基于形状的分割方法来解决这些挑战。该方法首先计算每个点的最佳邻域大小，以获得与之相关的几何特征，然后使用支持向量机（SVM）根据几何特征对点云进行分类。其次，定义了一组规则来分割分类点云，并提出了一个相似性准则，以克服过度分割。最后，基于拓扑连通性将分割输出合并为有意义的几何抽象。所提出的方法已被测试的点云的两个城市场景获得不同的移动的激光扫描仪。实验结果表明，该方法对大规模移动的激光点云具有较好的分割精度和计算效率，尤其对柱状物体的分割效果更好。(C)2013年国际摄影测量与遥感学会（ISRS）由Elsevier B.V.发布保留所有权利。
Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.