A mask R-CNN based approach to automatically construct As-is IFC BIM objects from digital images

H. Q. Ying, S. Lee

Research output: Contribution to conferencePaperpeer-review

16 Scopus citations

Abstract

Various image-based building object recognition approaches have been developed to create as-is Building Information Models (BIMs) of existing buildings. However, existing approaches generally rely on human-designed features to automatically or semi-automatically recognize building objects, which makes them sensitive to input images and difficult to extend to new building objects. Furthermore, when constructing object geometries, most of these approaches are limited to rectangular or pre-defined surface shapes. To address these limitations, this study presents a human-designed features-free, shape constraints-free and fully automatic approach to construct as-is BIM objects from images of a building. This approach adapts Mask R-CNN, a deep convolutional neural network, to automatically recognize and segment building objects with arbitrary shapes (i.e., surface boundary shapes) from images. The segmented objects, characterized by object types and pixel masks, are further geometrically fitted to construct surface geometries. Finally, the constructed building objects are defined in the Industry Foundation Classes (IFC) data format. Three types of building objects (i.e., walls, doors, and lifts) are used in this study. Total 430 images containing these objects collected from the interiors of 4 university buildings are used to train and test the Mask R-CNN model. The test results show that the trained model is accurate and robust to recognize and segment all the three types of building objects. Furthermore, the feasibility of the proposed approach is preliminarily validated by successfully extracting IFC building objects from an image.

Original languageEnglish
Pages764-771
Number of pages8
DOIs
StatePublished - 2019
Event36th International Symposium on Automation and Robotics in Construction, ISARC 2019 - Banff, Canada
Duration: 21 May 201924 May 2019

Conference

Conference36th International Symposium on Automation and Robotics in Construction, ISARC 2019
Country/TerritoryCanada
CityBanff
Period21/05/1924/05/19

Keywords

  • As-is BIM object
  • Deep learning
  • IFC
  • Image-based modeling
  • Mask R-CNN

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