Data-driven interior plan generation for residential buildings
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2019/11/08
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Journal
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Indian UGC (journal)
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Refrences43
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Citations82
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Wenming Wu University of Science and Technology of China, China
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Xiao-Ming Fu University of Science and Technology of China, China
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Rui Tang Kujiale, China
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Yuhan Wang Kujiale, China
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Yu-Hao Qi University of Science and Technology of China, China
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Ligang Liu University of Science and Technology of China, China
Abstract
Cite
Wu, Wenming, et al. “Data-Driven Interior Plan Generation for Residential Buildings”. ACM Transactions on Graphics, vol. 38, no. 6, 2019, pp. 1-12, https://doi.org/10.1145/3355089.3356556.
Wu, W., Fu, X.-M., Tang, R., Wang, Y., Qi, Y.-H., & Liu, L. (2019). Data-driven interior plan generation for residential buildings. ACM Transactions on Graphics, 38(6), 1-12. https://doi.org/10.1145/3355089.3356556
Wu, Wenming, Xiao-Ming Fu, Rui Tang, Yuhan Wang, Yu-Hao Qi, and Ligang Liu. “Data-Driven Interior Plan Generation for Residential Buildings”. ACM Transactions on Graphics 38, no. 6 (2019): 1-12. https://doi.org/10.1145/3355089.3356556.
Wu W, Fu XM, Tang R, Wang Y, Qi YH, Liu L. Data-driven interior plan generation for residential buildings. ACM Transactions on Graphics. 2019;38(6):1-12.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Raster-to-Vector | 2017 | |||
| LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator. In International Conference on Learning Representations. | 2019 | |||
| Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs | 2018 | |||
| Architectural layout design optimization. Engineering optimization 34, 5 | 2002 | |||
| 10.1109/CVPR.2017.28 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| The essence of smart home design based on 5G communication | Multimedia Tools and Applications |
| 2024 | |
| FVCap: An Approach to Understand Scanned Floor Plan Images Using Deep Learning and its Applications | SN Computer Science | 2024 | ||
| An edge-weighted graph triumvirate to represent modular building layouts | Automation in Construction |
| 2 | 2024 |
| The impact of architecturally qualified data in deep learning methods for the automatic generation of social housing layouts | Automation in Construction |
| 2024 | |
| Quality assessment of residential layout designs generated by relational Generative Adversarial Networks (GANs) | Automation in Construction |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 32
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing and was published in 2020. The most recent citation comes from a 2024 study titled FVCap: An Approach to Understand Scanned Floor Plan Images Using Deep Learning and its Applications. This article reached its peak citation in 2023, with 34 citations. It has been cited in 41 different journals, 17% of which are open access. Among related journals, the Automation in Construction cited this research the most, with 19 citations. The chart below illustrates the annual citation trends for this article.