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

Kuldeep K. Sareen; George K. Knopf; Roberto Canas

doi:10.1117/1.3599868

1 July 2011 Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors

Kuldeep K. Sareen, George K. Knopf, Roberto Canas

Author Affiliations +

Optical Engineering, Vol. 50, Issue 7, 077003 (July 2011). https://doi.org/10.1117/1.3599868

Abstract

A range scan of a building's 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)

Citation Download Citation

Kuldeep K. Sareen, George K. Knopf, and Roberto Canas "Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors," Optical Engineering 50(7), 077003 (1 July 2011). https://doi.org/10.1117/1.3599868

Published: 1 July 2011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available