Semantic segmentation of images of building facilities

Thomas Czerniawski, Fernanda Leite

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Scan-to-BIM is the process of converting a 3D reconstruction into a building information model (BIM). The process has two parts: (1) sorting subsets of the reconstruction into classes (semantic segmentation) defined by a BIM taxonomy and (2) identifying geometric parameters describing each class instance. Here we demonstrate the ability of deep learning artificial neural networks to semantically segment images of building facilities. We found this deep learning approach capable of simultaneously recognizing: ceiling, wall, plumbing, duct, door, floor, and stairs classes. This semantic scope surpasses state-of-the-art building system recognition methods and represents progress towards comprehensive BIM creation.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
StatePublished - 2019
Externally publishedYes
Event26th International Workshop on Intelligent Computing in Engineering, EG-ICE 2019 - Leuven, Belgium
Duration: Jun 30 2019Jul 3 2019

ASJC Scopus subject areas

  • General Computer Science


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