BIM for existing facilities: feasibility of spectral image integration to 3D point cloud data

Accurate geometrical and spatial information of the built environment can be accurately acquired and the resulting 3D point cloud data is required to be processed to construct the digital model, Building Information Modelling (BIM) for existing facilities. Point cloud by laser scanning over the buildings and facilities has been commonly used, but the data requires external information so that any objects and materials can be correctly identified and classified. A number of advanced data processing methods have been developed, such as the use of colour information to attach semantic information. However, the accuracy of colour information depends largely on the scene environment where the image is acquired. The limited number of spectral channels on conventional RGB camera often fails to extract important information about surface material, despite spectral surface reflectance can represent a signature of the material. Hyperspectral imaging can, instead, provide precise representation of spatial and spectral information. By implementing such information to 3D point cloud, the efficiency of material detection and classification in BIM should be significantly improved. In this work, the feasibility of the image integration and discuss practical difficulties in the development.

[1]  David Connah,et al.  Spectral edge: gradient-preserving spectral mapping for image fusion. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[3]  Joachim M. Buhmann,et al.  Weakly supervised semantic segmentation with a multi-image model , 2011, 2011 International Conference on Computer Vision.

[4]  Burcu Akinci – SENSORS IN CONSTRUCTION AND INFRASTRUCTURE MANAGEMENT , 2008 .

[5]  Maryam Mohammadzadeh Darrodi,et al.  Reference data set for camera spectral sensitivity estimation. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Rob Howard,et al.  Building information modelling - Experts' views on standardisation and industry deployment , 2008, Adv. Eng. Informatics.

[7]  D. Foster,et al.  Frequency of metamerism in natural scenes , 2006 .

[8]  Kenneth T. Sullivan,et al.  How To Measure the Benefits of BIM - A Case Study Approach , 2012 .

[9]  Christopher Gorse,et al.  Refurbishment and Upgrading of Buildings , 2000 .

[10]  L. Plümer,et al.  Detection of early plant stress responses in hyperspectral images , 2014 .

[11]  Andrey Dimitrov,et al.  Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections , 2014, Adv. Eng. Informatics.

[12]  David Bryde,et al.  The project benefits of Building Information Modelling (BIM) , 2013 .

[13]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[14]  Mani Golparvar-Fard,et al.  Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs , 2015 .

[15]  Vahid Nourani,et al.  TOPMODEL CAPABILITY FOR RAINFALL-RUNOFF MODELING OF THE AMMAMEH WATERSHED AT DIFFERENT TIME SCALES USING DIFFERENT TERRAIN ALGORITHMS , 2011 .

[16]  Patricio A. Vela,et al.  Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future , 2015, Adv. Eng. Informatics.

[17]  Jaehoon Jung,et al.  Productive modeling for development of as-built BIM of existing indoor structures , 2014 .

[18]  Mani Golparvar-Fard,et al.  Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques , 2011 .

[19]  Osama Moselhi,et al.  Integrating 3D laser scanning and photogrammetry for progress measurement of construction work , 2008 .

[20]  Yusuf Arayici Building information modelling , 2015 .

[21]  Helmi Zulhaidi Mohd Shafri,et al.  DEVELOPMENT AND UTILIZATION OF URBAN SPECTRAL LIBRARY FOR REMOTE SENSING OF URBAN ENVIRONMENT , 2011 .

[22]  M. Herold,et al.  Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications. , 2005, Applied optics.

[23]  Thomas D. Nielsen,et al.  Hyperspectral imaging: a novel approach for microscopic analysis. , 2001, Cytometry.

[24]  Dima Damen,et al.  Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Yusuf Arayici,et al.  Towards building information modelling for existing structures , 2008 .

[26]  Yu-Lin Xu,et al.  Fraunhofer diffraction of electromagnetic radiation by finite periodic structures with regular or irregular overall shapes. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.