Rapid clustering from colorized 3D point cloud data for reconstructing building interiors

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ConferenceISOT 2010 International Symposium on Optomechatronic Technologies: 25 October 2010, Toronto, ON
Pages16; # of pages: 6
AbstractThis paper introduces a quick and effective segmentation technique for large volumes of colorized range scans from unknown building interiors and labeling clusters of points that represent distinct surfaces and objects in the scene. Rather than computing geometric parameters, the proposed technique uses a robust Hue, Saturation and Value (HSV) color model as an effective means of identifying rough clusters (objects) that are further refined by eliminating spurious and outlier points through region growth and a fixed distance neighbors (FDNs) analysis. The results demonstrate that the proposed method is effective in identifying continuous clusters and can extract meaningful object clusters, even from geometrically similar regions.
Publication date
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number53602
NPARC number20374253
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Record identifierdb406f02-b814-49a9-9121-bcc7f30358fe
Record created2012-07-23
Record modified2016-05-09
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