SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing

Yubo LIU; Yangting LAI; Jianyong CHEN; Lingyu LIANG; Qiaoming DENG

doi:10.1587/transinf.2020EDL8076

SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing

Yubo LIU, Yangting LAI, Jianyong CHEN, Lingyu LIANG, Qiaoming DENG

Full Text Views

0

Cite this

Summary :

Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.12 pp.2725-2729

Publication Date: 2020/12/01

Publicized: 2020/09/03

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2020EDL8076

Type of Manuscript: LETTER

Category: Computer Graphics

Authors

Yubo LIU
  South China University of Technology
Yangting LAI
  South China University of Technology
Jianyong CHEN
  South China University of Technology
Lingyu LIANG
  South China University of Technology
Qiaoming DENG
  South China University of Technology

Keyword

floor plan, urban plan, image parsing, GAN

Cite this

Copy

Yubo LIU, Yangting LAI, Jianyong CHEN, Lingyu LIANG, Qiaoming DENG, "SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 12, pp. 2725-2729, December 2020, doi: 10.1587/transinf.2020EDL8076.
Abstract: Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8076/_p

Copy

@ARTICLE{e103-d_12_2725,
author={Yubo LIU, Yangting LAI, Jianyong CHEN, Lingyu LIANG, Qiaoming DENG, },
journal={IEICE TRANSACTIONS on Information},
title={SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing},
year={2020},
volume={E103-D},
number={12},
pages={2725-2729},
abstract={Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.},
keywords={},
doi={10.1587/transinf.2020EDL8076},
ISSN={1745-1361},
month={December},}

Copy

TY - JOUR
TI - SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing
T2 - IEICE TRANSACTIONS on Information
SP - 2725
EP - 2729
AU - Yubo LIU
AU - Yangting LAI
AU - Jianyong CHEN
AU - Lingyu LIANG
AU - Qiaoming DENG
PY - 2020
DO - 10.1587/transinf.2020EDL8076
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2020
AB - Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.
ER -