Data completion in building information management: electrical lines from range scans and photographs

BackgroundThe concept of building information management (BIM) is based on its holistic nature. This idea pays off, if all relevant information is fused into one consistent data set. As a consequence, the completeness of data is vital and the research question on how to complete data automatically remains open.MethodsIn this article we present a data completion technique based on knowledge management. We encode expert and domain knowledge in a generative system that represents norms and standards in a machine-readable manner. The implementation of this approach be used to automatically determine a hypothesis on the location of electrical lines within indoor range scans.ResultsThe generative paradigm can encode domain expert knowledge in a machine-readable way. In this article we demonstrate its usage to represent norms and standards.ConclusionsThe benefit of our method is the further completion of digital building information models – a necessary step to take full advantage of building information modeling.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  Dieter W. Fellner,et al.  Modeling Procedural Knowledge: A Generative Modeler for Cultural Heritage , 2010, EuroMed.

[4]  Hayko Riemenschneider,et al.  Irregular lattices for complex shape grammar facade parsing , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Dieter W. Fellner,et al.  AUTOMATIC TEXTURE AND ORTHOPHOTO GENERATION FROM REGISTERED PANORAMIC VIEWS , 2015 .

[6]  D. West Introduction to Graph Theory , 1995 .

[7]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[8]  Awad S. Hanna,et al.  State of Practice of Building Information Modeling in the Electrical Construction Industry , 2014 .

[9]  Reinhard Klein,et al.  An Automated Approach to the Generation of Structured Building Information Models from Unstructured 3d Point Cloud Scans , 2016 .

[10]  Uwe Rüppel,et al.  A graph-based prediction method for electrical wiring in old residential buildings as a part of BIM for Urban Mining purposes , 2014 .

[11]  Robin J. Wilson Introduction to Graph Theory , 1974 .

[12]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[13]  Mark T. Maybury The Meaning of 3D Shape and Some Techniques to Extract it , 2011 .

[14]  Miroslav Chlebík,et al.  The Steiner tree problem on graphs: Inapproximability results , 2008, Theor. Comput. Sci..

[15]  Sven Havemann,et al.  Shape grammars on convex polyhedra , 2013, Comput. Graph..

[16]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Sven Havemann,et al.  CREATING PROCEDURAL WINDOWBUILDING BLOCKS USING THE GENERATIVE FACT LABELING METHOD , 2013 .

[18]  Ulrich Krispel,et al.  A Survey of Algorithmic Shapes , 2015, Remote. Sens..

[19]  Dana S. Richards,et al.  Steiner tree problems , 1992, Networks.

[20]  Reinhard Klein,et al.  Automatic reconstruction of parametric building models from indoor point clouds , 2016, Comput. Graph..

[21]  Ronald L. Graham,et al.  On the History of the Minimum Spanning Tree Problem , 1985, Annals of the History of Computing.

[22]  George Markowsky,et al.  A fast algorithm for Steiner trees , 1981, Acta Informatica.

[23]  Daniel Huber The ASTM E57 file format for 3D imaging data exchange , 2011, Electronic Imaging.