Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors

  1. Get@NRC: Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1117/1.3599868
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Journal titleOptical Engineering
Article number77003
SubjectCluster expansion; Complex geometries; Complex objects; Data segmentation; Flat surfaces; Hierarchical clustering algorithms; Hierarchical data clustering; Local density; Local surfaces; Planar alignment; Planar region; Planarity; Range data; Range scans; Reduced data; Scene reconstruction; Segmentation algorithms; Sparse data; Spatial informations; Surface curvatures; Three-dimensional data; Three-dimensional point clouds; Three-dimensional scanning; Two stage; Virtual reality environments; Virtual reality models; Clustering algorithms; Reverse engineering; Virtual reality; Three dimensional
AbstractA range scan of a buildings interior typically produces an immense cloud of colorized three-dimensional data that represents diverse surfaces ranging from simple planes to complex objects. To create a virtual reality model of the preexisting room, it is necessary to segment the data into meaningful clusters. Unfortunately, segmentation algorithms based solely on surface curvature have difficulty in handling such diverse interior geometries, occluded boundaries, and closely placed objects with similar curvature properties. The proposed two stage hierarchical clustering algorithm overcomes many of these challenges by exploiting the registered color and spatial information simultaneously. Large planar regions are initially identified using constraints that combine color (hue) and a measure of local planarity called planar alignment factor. This stage assigns 72 to 84 of the sampled points to clusters representing flat surfaces such as walls, ceilings, or floors. The significantly reduced data points are clustered further using local surface normal and hue deviation information. A local density driven investigation distance (fixed density distance) is used for normal computation and cluster expansion. The methodology is tested on colorized range data of a typical room interior. The combined approach enabled the successful segmentation of planar and complex geometries in both dense and sparse data regions. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).
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AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Research in Construction (IRC-IRC)
Peer reviewedYes
NPARC number21271177
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Record identifier2540b962-d252-4cfe-aa23-de5527c4247c
Record created2014-03-24
Record modified2016-05-09
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