Range segmentation of large building exteriors: A hierarchical robust approach

Hesami, R, BabHadiashar, A and Hoseinnezhad, R 2010, 'Range segmentation of large building exteriors: A hierarchical robust approach', Computer Vision and Image Understanding (CVIU), vol. 114, no. 4, pp. 475-490.

Document type: Journal Article
Collection: Journal Articles

Title Range segmentation of large building exteriors: A hierarchical robust approach
Author(s) Hesami, R
BabHadiashar, A
Hoseinnezhad, R
Year 2010
Journal name Computer Vision and Image Understanding (CVIU)
Volume number 114
Issue number 4
Start page 475
End page 490
Total pages 16
Publisher Academic Press
Abstract There are three main challenging issues associated with processing range data of large-scale outdoor scene: (a) significant disparity in the size of features, (b) existence of complex and multiple structures; and (c) high uncertainty in data due to the construction error or moving objects. Existing range segmentation methods in computer vision literature have been generally developed for laboratory-sized objects or shapes with simple geometric features and do not address these issues. This paper studies the main problems involved in segmenting the range data of large building exteriors and presents a robust hierarchical segmentation strategy to extract fine as well as large details from such data. The proposed method employs a high breakdown robust estimator in a coarse-to-fine approach to deal with the existing discrepancies in size and sampling rates of various features of large outdoor objects. The segmentation algorithm is tested on several outdoor range datasets obtained by different laser rangescanners. The results show that the proposed method is an accurate and computationally cost-effective tool that facilitates automatic generation of 3D models of large-scale objects in general and building exteriors in particular.
Keyword(s) Large-scale range data
Range segmentation
Robust estimation
Historical building exteriors
DOI - identifier 10.1016/j.cviu.2009.12.004
Copyright notice © 2009 Elsevier Inc. All rights reserved.
ISSN 1077-3142
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
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