REVIEW OF THE "AS-BUILT BIM" APPROACHES

Today, we need 3D models of heritage buildings in order to handle more efficiently projects of restoration, documentation and maintenance. In this context, developing a performing approach, based on a first phase of building survey, is a necessary step in order to build a semantically enriched digital model. For this purpose, the Building Information Modeling is an efficient tool for storing and exchanging knowledge about buildings. In order to create such a model, there are three fundamental steps: acquisition, segmentation and modeling. For these reasons, it is essential to understand and analyze this entire chain that leads to a well- structured and enriched 3D digital model. This paper proposes a survey and an analysis of the existing approaches on these topics and tries to define a new approach of semantic structuring taking into account the complexity of this chain.

[1]  Amr A. Oloufa,et al.  Algorithms for automated deduction of topological information , 2005 .

[2]  Andrew W. Fitzgibbon,et al.  High-level model acquisition from range images , 1997, Comput. Aided Des..

[3]  Marc Jaeger,et al.  Segmentation of architecture shape information from 3D point cloud , 2009, VRCAI '09.

[4]  F. Rottensteiner SEMI-AUTOMATIC BUILDING RECONSTRUCTION INTEGRATED IN STRICT BUNDLE BLOCK ADJUSTMENT , 2000 .

[5]  Johannes Wallner,et al.  Approximation algorithms for developable surfaces , 1999, Comput. Aided Geom. Des..

[6]  Burcu Akinci,et al.  The ASDMCon Project: The Challenge of Detecting Defects on Construction Sites , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[7]  Wei-Chung Lin,et al.  CSG-based object recognition using range images , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[8]  George Vosselman,et al.  EXTRACTING WINDOWS FROM TERRESTRIAL LASER SCANNING , 2007 .

[9]  George Vosselman,et al.  Automatic extraction of building features from terrestrial laser scanning , 2006 .

[10]  Thomas A. Funkhouser,et al.  Min-cut based segmentation of point clouds , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[11]  Alfons Kemper,et al.  An analysis of geometric modeling in database systems , 1987, CSUR.

[12]  Matthieu Deveau Utilisation conjointe de données image et laser pour la segmentation et la modélisation 3D , 2006 .

[13]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[14]  Nico Blodow,et al.  Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  L. D. Luca,et al.  Relevé et multi-représentations du patrimoine architectural Définition d'une approche hybride pour la reconstruction 3D d'édifices , 2006 .

[16]  Emmanuel Alby,et al.  Confrontation du relevé laser 3D aux techniques de relevé conventionnelles et développement d'outils numériques pour la restitution architecturale , 2004 .

[17]  Daniel Huber,et al.  Using Context to Create Semantic 3D Models of Indoor Environments , 2010, BMVC.

[18]  Bruce G. Baumgart Winged edge polyhedron representation. , 1972 .

[19]  Christophe Cruz,et al.  From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach , 2012 .

[20]  Helmut Cantzler,et al.  Improving architectural 3D reconstruction by constrained modelling , 2003 .