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Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts


Mura, Claudio; Mattausch, Oliver; Villanueva, Alberto Jaspe; Gobbetti, Enrico; Pajarola, R (2014). Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts. Computers & Graphics, 44:20-32.

Abstract

We present a robust approach for reconstructing the main architectural structure of complex indoor environments given a set of cluttered 3D input range scans. Our method uses an efficient occlusion-aware process to extract planar patches as candidate walls, separating them from clutter and coping with missing data, and automatically extracts the individual rooms that compose the environment by applying a diffusion process on the space partitioning induced by the candidate walls. This diffusion process, which has a natural interpretation in terms of heat propagation, makes our method robust to artifacts and other imperfections that occur in typical scanned data of interiors. For each room, our algorithm reconstructs an accurate polyhedral model by applying methods from robust statistics. We demonstrate the validity of our approach by evaluating it on both synthetic models and real-world 3D scans of indoor environments.

We present a robust approach for reconstructing the main architectural structure of complex indoor environments given a set of cluttered 3D input range scans. Our method uses an efficient occlusion-aware process to extract planar patches as candidate walls, separating them from clutter and coping with missing data, and automatically extracts the individual rooms that compose the environment by applying a diffusion process on the space partitioning induced by the candidate walls. This diffusion process, which has a natural interpretation in terms of heat propagation, makes our method robust to artifacts and other imperfections that occur in typical scanned data of interiors. For each room, our algorithm reconstructs an accurate polyhedral model by applying methods from robust statistics. We demonstrate the validity of our approach by evaluating it on both synthetic models and real-world 3D scans of indoor environments.

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7 citations in Web of Science®
5 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:graphics, architecture, floor plans, 3D reconstruction, point cloud, scanning, normal estimation
Language:English
Date:November 2014
Deposited On:16 Jan 2015 13:28
Last Modified:05 Apr 2016 18:46
Publisher:Elsevier
ISSN:0097-8493
Publisher DOI:https://doi.org/10.1016/j.cag.2014.07.005
Official URL:http://www.sciencedirect.com/science/article/pii/S0097849314000661
Other Identification Number:merlin-id:10250
Permanent URL: https://doi.org/10.5167/uzh-104333

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