Point cloud segmentation groups points with similar attributes with respect to geometric, colormetric, radiometric, and/or other information to support Terrestrial Laser Scanning (TLS) data processing such as feature extraction, classification, modeling, analysis, and so forth. In this paper we propose a segmentation method consisting of two main steps. First, a novel feature extraction approach, NORmal VAriation ANAlysis (Norvana), eliminates some noise points and extracts edge points without requiring a general (and error prone) normal estimation at each point. Second, region growing groups the points on a smooth surface to obtain the segmentation result. For efficiency, both steps exploit the angular grid structure storing each TLS scan that is often neglected in many segmentation algorithms, which are primarily developed for unorganized point clouds. Additionally, unlike the existing methods exploiting the angular grid structure that can only be applied to a single scan, the proposed method is able to segment multiple registered scans simultaneously. The algorithm also takes advantage of parallel programming for efficiency. In the experiment, both qualitative and quantitative evaluations are performed through two datasets whilst the robustness and efficiency of the proposed method are analyzed and discussed.

点云分段将具有关于几何，色度，辐射度和/或其他信息的相似属性的点分组，以支持陆地激光扫描（TLS）数据处理，例如特征提取，分类，建模，分析等。在本文中，我们提出了一种由两个主要步骤组成的分割方法。首先，一种新颖的特征提取方法，NORmal VAriation ANAlysis（Norvana），消除了一些噪声点并提取了边缘点，而无需在每个点进行一般（且容易出错）的法线估计。其次，区域增长对光滑表面上的点进行分组以获得分割结果。为了提高效率，这两个步骤都利用了存储每个TLS扫描的角度网格结构，该结构在许多分割算法中经常被忽略，这些分割算法主要是针对无组织的点云而开发的。另外，与现有的利用仅可应用于单次扫描的角网格结构的方法不同，所提出的方法能够同时分割多个配准的扫描。该算法还利用并行编程来提高效率。在实验中