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Automatic Room Detection and Reconstruction in Cluttered Indoor Environments with Complex Room Layouts

Claudio Mura, Oliver Mattausch, Alberto Jaspe Villanueva, Enrico Gobbetti, and Renato Pajarola

November 2014

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.

Reference and download information

Claudio Mura, Oliver Mattausch, Alberto Jaspe Villanueva, Enrico Gobbetti, and Renato Pajarola. Automatic Room Detection and Reconstruction in Cluttered Indoor Environments with Complex Room Layouts. Computers & Graphics, 44: 20-32, November 2014. http://dx.doi.org/10.1016/j.cag.2014.07.005.

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Bibtex citation record

@Article{Mura:2014:ARD,
    author = {Claudio Mura and Oliver Mattausch and Alberto {Jaspe Villanueva} and Enrico Gobbetti and Renato Pajarola},
    title = {Automatic Room Detection and Reconstruction in Cluttered Indoor Environments with Complex Room Layouts},
    journal = {Computers \& Graphics},
    volume = {44},
    pages = {20--32},
    publisher = {Elsevier Science Publishers B. V.},
    address = {Amsterdam, The Netherlands},
    month = {November},
    year = {2014},
    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. },
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Mura:2014:ARD'},
}