Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Leveraging Deep Learning and Path Planning Techniques for 3D Modeling From House Floor Plans

딥러닝과 경로계획 기반의 주택 평면도 3D 모델링 방법

  • Miao, Xu (School of Architecture, Kyungil University) ;
  • Eom, Shin-Jo (School of Architecture, Kyungil University)
  • Received : 2023.11.05
  • Accepted : 2023.12.05
  • Published : 2024.01.30
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Abstract

The surge in BIM technology's popularity has heightened the demand for transforming traditional architectural floor plans into BIM-oriented 3D models. To address this, scholars have introduced diverse methods for this conversion, with those leveraging deep learning technology garnering significant attention. Deep learning-based approaches typically encompass information segmentation and data vectorization. However, the current research landscape lacks exploration into optimizing deep learning technology for high generalization efficiency and promptly vectorizing wall lines. This study enhances the generalization capability of information segmentation in deep learning by incorporating Instance Normalization (IN) and Instance Whitening (IW). Additionally, it introduces a method for swiftly generating wall lines through a proposed vector generation algorithm rooted in path planning. This enhancement facilitates the creation of a BIM-centric automation process, streamlining the conversion of accumulated 2D architectural drawings into efficient 3D models.

Keywords

Acknowledgement

이 연구는 2020년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호:2020R1I1A3073663

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