ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 281-288, 2014
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/281/2014/
doi:10.5194/isprsannals-II-5-281-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
28 May 2014
Towards automatic indoor reconstruction of cluttered building rooms from point clouds
M. Previtali1, L. Barazzetti1, R. Brumana1, and M. Scaioni2 1Politecnico di Milano, Department of Architecture, Built Environment and Construction Engineering Via G. Ponzio 31, Milano, Italy
2Tongji University, College of Surveying and Geo-Informatics, 1239 Siping Road, 200092 Shanghai, P.R. China
Keywords: Indoor Reconstruction, Laser scanning, Point cloud, Segmentation Abstract. Terrestrial laser scanning is increasingly used in architecture and building engineering for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning survey are generally not directly ready for generation of such models. A manual modelling phase has to be undertaken to edit and complete 3D models, which may cover indoor or outdoor environments. This paper presents an automated procedure to turn raw point clouds into semantically-enriched models of building interiors. The developed method mainly copes with a geometric complexity typical of indoor scenes with prevalence of planar surfaces, such as walls, floors and ceilings. A characteristic aspect of indoor modelling is the large amount of clutter and occlusion that may characterize any point clouds. For this reason the developed reconstruction pipeline was designed to recover and complete missing parts in a plausible way. The accuracy of the presented method was evaluated against traditional manual modelling and showed comparable results.
Conference paper (PDF, 1837 KB)


Citation: Previtali, M., Barazzetti, L., Brumana, R., and Scaioni, M.: Towards automatic indoor reconstruction of cluttered building rooms from point clouds, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 281-288, doi:10.5194/isprsannals-II-5-281-2014, 2014.

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