Multi-scan segmentation of terrestrial laser scanning data based on normal variation analysis

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. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.