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Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids

  • The quality of 3D models of existing buildings reconstructed from point clouds is strongly related to the segmentation process used to detect structural elements. A new wall detection method in the indoor point clouds of buildings is presented in this study. The point clouds are segmented into horizontal layers, and a concept of continuous segments in a 2D grid representation is used to extract the footprints of the wall structures, and 2D blocks are projected into 3D space to obtain the wall segments in the initial 3D point cloud. The results obtained from the execution of the proposed method demonstrate that wall blocks in indoor point clouds are detected independently of their shape. Executing the proposed method on a set of 9 in-door point clouds revealed better performance in terms of result quality and execution time compared to RANSAC. The robustness of the method can be improved by adding a classification step to eliminate non-consistent blocks.

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Metadaten
Author: Cedrique FotsingORCiD, Philipp Hahn, Douglas W. CunninghamORCiD, Christophe Bobda
DOI:https://doi.org/10.1016/j.autcon.2022.104462
ISSN:0926-5805
Title of the source (English):Automation in Construction
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2022
Tag:Point cloud; Segmentation; Wall detection
Issue number:141
Article number:104462
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Praktische Informatik / Graphische Systeme
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