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An Advanced Approach for Automatic Extraction of Planar Surfaces and their Topology from Point Clouds Ein erweiterter Ansatz zur automatischen Extraktion ebener Flächen und ihrer Topologie aus Punktwolken

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Abstract

Terrestrial laser scanning has become a standard method for a fast and accurate acquisition of 3D objects. While data capture has attained a high level of development, the analysis of point clouds is still characterised by a remarkable amount of manual interaction. In this article an advanced generic approach for the extraction of surface primitives is presented. In a first step the 3D measurement domain is subdivided into volume elements (voxels) and the centre of gravity of the interior laser points is calculated for each voxel as representative geometric position. Normal vectors are determined for each voxel by means of all possible combinations of two vectors to the 26 neighbouring barycentres. If the local surrounding contains plane surface parts, a couple of these normal vectors have similar directions. These vectors will be aggregated (mean direction) and the number of involved vectors (NOV) is stored. For a planar surrounding a clear majority will be obtained. A region growing algorithm extracts plane surfaces by merging adjacent voxels if their main normal directions are similar (homogeneity criterion). If two majorities can be observed it is an edge point, for three main directions a corner point can be assumed. These topological points will be stored as a base for the subsequent 3D modelling process. First experiences with synthetic and real world data of buildings have shown the suitability of this advanced approach and the robustness concerning noise, surface roughness and outliers. A disadvantage may be a certain generalisation effect. German Terrestrisches Laserscanning ist zu einer Standardmethode der Erfassung von 3D-Objekten geworden. Während die Datenerfassung einen hohen Entwicklungsstand erreicht hat, ist die Auswertung der Punktwolken noch durch einen erheblichen Anteil an manuellen Arbeiten gekennzeichnet. In diesem Beitrag wird ein erweiterter generischer Ansatz zur Extraktion von Oberflächenprimitiven vorgestellt. In einem ersten Verfahrensschritt wird der dreidimensionale Messraum in Volumen-Elemente (Voxel) unterteilt und für jedes Voxel der Schwerpunkt der darin gelegenen Laserpunkte als repräsentativer geometrischer Ort berechnet. Für jedes Voxel werden dann Normalenvektoren bestimmt, die sich aus allen Kombinationen zweier Vektoren zu den Schwerpunkten der 26 benachbarten Voxel ergeben. Enthält die lokale Umgebung ebene Oberflächenteile, so werden mehrere Normalenvektoren nahezu in die gleiche Richtung weisen. Diese Vektoren werden unter Berechnung der mittleren Richtung zusammengefasst und die Anzahl der beteiligten Vektoren (NOV) gespeichert. Im Falle einer Ebene als lokale Umgebung wird sich eine deutliche Mehrheit der Normalenrichtungen ergeben. Ein Flächenwachstumsverfahren extrahiert ebene Flächen durch Fusion benachbarter Voxel, deren Haupt-Normalenvektoren nur eine geringe Richtungsabweichung voneinander aufweisen (Homogenitätskriterium). Ergeben sich zwei Hauptrichtungen, so handelt es sich urn einen Punkt auf einer Kante, bei drei Hauptrichtungen kann eine Ecke angenommen werden. Diese topologischen Punkte werden als Basis für die anschließende 3D-Modellierung gespeichert. Erste Erfahrungen mit synthetischen wie auch realen Daten von Gebäuden haben die Eignung dieses Ansatzes sowie seine Robustheit gegenüber Rauschen, Oberflächenrauhigkeit und Ausreißern erwiesen. Ein Nachteil könnte ein gewisser Generalisierungseffekt aufgrund der Voxel-Rasterung sein.

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... Edge-based segmentation methods, e.g., Belton and Lichti [46], extract edges based on changes in local surface properties and group the points inside the boundaries. Several surface-based methods can be found in Schmitt and Vogtle [47] and Pu and Vosselman [48], which use local surface properties as a similarity measure for the grouping of points. Due to the huge volume of data, redundancy, and occlusion, these methods have difficulties in extracting the point clouds of individual buildings from point clouds that include trees and buildings. ...
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