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A platform for research: civil engineering, architecture and urbanism
Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids
Abstract 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.
Highlights Automatic extraction of walls in indoor point clouds of buildings. Horizontal segmentation of point clouds to minimize the impact of outliers. Computing ground wall constellation from 2D grid representations of layers. Wall structure clustering using continuous segments.
Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids
Abstract 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.
Highlights Automatic extraction of walls in indoor point clouds of buildings. Horizontal segmentation of point clouds to minimize the impact of outliers. Computing ground wall constellation from 2D grid representations of layers. Wall structure clustering using continuous segments.
Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids
Fotsing, Cedrique (author) / Hahn, Philipp (author) / Cunningham, Douglas (author) / Bobda, Christophe (author)
2022-06-24
Article (Journal)
Electronic Resource
English
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