Latest revision as of 17:31, 21 January 2021

Abstract

90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-theart software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75 % recall and 62 % precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67 % of pipe modelling time is saved by EdgeWise. This paper is the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving i

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1007/978-3-319-91635-4_6

• https://www.repository.cam.ac.uk/bitstream/1810/279173/1/EG-ICE2018_012_final_v4.pdf

• https://www.repository.cam.ac.uk/handle/1810/279173

• http://link.springer.com/content/pdf/10.1007/978-3-319-91635-4_6,

http://dx.doi.org/10.1007/978-3-319-91635-4_6 under the license http://www.springer.com/tdm

• https://link.springer.com/chapter/10.1007/978-3-319-91635-4_6,

https://www.repository.cam.ac.uk/handle/1810/279173,

https://doi.org/10.1007/978-3-319-91635-4_6,

https://dblp.uni-trier.de/db/conf/egice/egice2018-1.html#AgapakiB18,

http://publications.eng.cam.ac.uk/979696,

https://rd.springer.com/chapter/10.1007/978-3-319-91635-4_6,

https://academic.microsoft.com/#/detail/2803271030

DOIS: 10.17863/cam.26553 10.1007/978-3-319-91635-4_6