This paper aims to propose an approach to automated classifying building element instance in BIM using deep learning-based 3D object classification algorithm. Recently, studies related to checking or validating engine of BIM object for ensuring data integrity of BIM instances are getting attention. As a part of this research, this paper train recognition models that are targeted at basic building element and interior element using 3D object recognition technique that uses images of objects as inputs. Object recognition is executed in two stages; (1) class of object (e.g. wall, window, seating furniture, toilet fixture and etc.), (2) sub-type of specific classes (e.g. Toilet or Urinal). Using the trained models, BIM plug-in prototype is developed and the performance of this AI-based approach with test BIM model is checked. We expect this recognition approach to help ensure the integrity of BIM data and contribute to the practical use of BIM.
|Title of host publication||Computer-Aided Architectural Design. “Hello, Culture” - 18th International Conference, CAAD Futures 2019, Selected Papers|
|Number of pages||11|
|Publication status||Published - 2019|
|Event||18th International Conference on Computer-Aided Architectural Design Futures, CAAD Futures 2019 - Daejeon, Korea, Republic of|
Duration: 2019 Jun 26 → 2019 Jun 28
|Name||Communications in Computer and Information Science|
|Conference||18th International Conference on Computer-Aided Architectural Design Futures, CAAD Futures 2019|
|Country/Territory||Korea, Republic of|
|Period||19/6/26 → 19/6/28|
Bibliographical noteFunding Information:
Acknowledgement. This research was supported by a grant (19AUDP-B127891-03) from the Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.
© 2019, Springer Nature Singapore Pte Ltd.
All Science Journal Classification (ASJC) codes
- Computer Science(all)