CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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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
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
full text file.pdf (1,963,522 bytes)
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