Mini Review
Cost-Effective as-Built BIM Modelling Using 3D Point- Clouds and Photogrammetry
Stephen Oliver1, Saleh Seyedzadeh2, Farzad Pour Rahimian1*, Nashwan Dawood1 and Sergio Rodriguez1
1School of Computing, Engineering and Digital Technologies, Teesside University, Tees Valley, Middlesbrough, UK
2Department of Engineering, University of Strathclyde, Glasgow, UK
Farzad Pour Rahimian, School of Computing, Engineering and Digital Technologies, Teesside University, Tees Valley, Middlesbrough, UK.
Received Date: December 20, 2019; Published Date: January 09, 2020
Abstract
The classification and translation of 3D point-clouds into virtual environments is a time consuming and often tedious process. While there are large crowdsourcing projects for segmenting general environments, there are no such projects with a focus on AEC industries. The nature of these industries makes industry-specific projects too esoteric for conventional approaches to data collation. However, in contrast to other projects, the built environment has a rich data set of BIM and other 3D models with mutable properties and implicitly embedded relationships which are ripe for exploitation. In this article, the readers will find discussion on contemporary applications of image processing, photogrammetry, BIM and artificial intelligence. They will also find discussions on the applications of artificial intelligence, photogrammetry in BIM and image processing that will likely be at the forefront of related research in the coming years.
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Stephen Oliver, Saleh Seyedzadeh, Farzad Pour Rahimian, Nashwan Dawood, Sergio Rodriguez. Cost-Effective as-Built BIM Modelling Using 3D Point- Clouds and Photogrammetry. Cur Trends Civil & Struct Eng. 4(5): 2020. CTCSE.MS.ID.000599.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.