Inference of relevant BIM objects using CNN for visual-input based auto-modeling

J. Kim, J. Song, J. Lee

Research output: Contribution to conferencePaper

Abstract

This paper aims to propose an approach to inferring relevant BIM objects using techniques for recognition of design attributes and calculation of visual similarity using a deep convolutional neural network. Main objective is a visual-input based modelling approach to the automated building and interior design, and this paper represents a preliminary yet critical part of the process. While designing a building, it is important to consider requirements such strong constraints (e.g. regulations, a request for proposal) as well as relatively weak and qualitative constraints such as preference style of clients or users, design trend and etc. Building Information Modeling (BIM) enables to execute to check and review a building design according to the constraints. Until now there is no research that has focused on relatively qualitative and “soft” constraints such as preference or design trend. As a part of research on design supporting system for such “soft” constraints, this paper focuses on training deep learning models that recognize design attributes of BIM object, calculate visual similarity with other objects, and for visual-input based auto-modeling on BIM using the models. A deep convolutional neural network is utilized to extract a visual feature of the 3D object. The input data type for extracting feature data is a 2D rendering image of an object with a specific view and option. The target object is a chair. The feature data is used as input to training models inferring design attributes such as design style, seating capacity and sub-type of a chair and also calculating the visual similarity between objects. This models plays an important role of visual-input based automated modelling system.

Original languageEnglish
Pages393-398
Number of pages6
Publication statusPublished - 2019 Jan 1
Event36th International Symposium on Automation and Robotics in Construction, ISARC 2019 - Banff, Canada
Duration: 2019 May 212019 May 24

Conference

Conference36th International Symposium on Automation and Robotics in Construction, ISARC 2019
CountryCanada
CityBanff
Period19/5/2119/5/24

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Building and Construction
  • Human-Computer Interaction

Cite this

Kim, J., Song, J., & Lee, J. (2019). Inference of relevant BIM objects using CNN for visual-input based auto-modeling. 393-398. Paper presented at 36th International Symposium on Automation and Robotics in Construction, ISARC 2019, Banff, Canada.