Multi-scan Segmentation of Terrestrial Laser Scanning Data

graphical abstract of the Normal Variation Analysis

Normal Variation Analysis

The process of point cloud segmentation groups points with similar attributes such as geometric, colormetric, radiometric, and/or other information to support Terrestrial Laser Scanning (TLS) data processing such as feature extraction, classification, modeling, analysis, and more.

From the Abstract by Erzhuo Che and Michael J. Olsen, Oregon State University

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 of each TLS scan. This 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.

Graph of Norvana Performance with Multiple Threads

Norvana Performance with Multiple Threads

Test results indicate that the proposed method take minutes for point clouds with hundreds of millions of points using parallel programming. This team of researchers at Oregon State University are making impressive strides in automating the processing of massive point clouds.

For the full paper click here.

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