| id |
caadria2020_203 |
| authors |
Xiao, Yahan, Chen, Sen, Ikeda, Yasushi and Hotta, Kensuke |
| year |
2020 |
| title |
Automatic Recognition and Segmentation of Architectural Elements from 2D Drawings by Convolutional Neural Network |
| doi |
https://doi.org/10.52842/conf.caadria.2020.1.843
|
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 843-852 |
| summary |
The BIM modeling process is the most time-consuming aspect. This paper studies the possibility of applying the recognition and segmentation of architectural components by deep learning to assist automatic BIM modeling. The research has two parts: the first one is dataset preparing, that images with the labeled architectural components from an original CAD drawing are made for the network training, and second is training and testing, that a mature network which has been trained in hundreds of labeled images is used to make predictions. The utilization of the current study results is discussed and the optimization method as well. |
| keywords |
BIM; CAD drawings; Recognition and Segmentation; Convolutional Neural Network; Computer vision |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,963,522 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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