Skip to main navigation Skip to search Skip to main content

Pipes of AI - Machine Learning Assisted 3D Modelling Design

  • Chuan Liu
  • , Jiaqi Shen
  • , Yue Ren
  • , Hao Zheng*
  • *Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

28 Downloads (CityUHK Scholars)

Abstract

Style transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.
Original languageEnglish
Title of host publicationProceedings of the 2020 DigitalFUTURES
Subtitle of host publicationThe 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
EditorsPhilip F. Yuan, Jiawei Yao, Chao Yan, Xiang Wang, Neil Leach
PublisherSpringer Singapore
Pages17-26
Number of pages10
Edition1
ISBN (Electronic)978-981-33-4400-6
ISBN (Print)978-981-33-4399-3, 978-981-33-4402-0
DOIs
Publication statusPublished - 26 Jun 2020
Externally publishedYes
EventCDRF 2020: The 2nd International Conference on Computational Design and Robotic Fabrication - College of Architecture and Urban Planning (CAUP), Tongji University, Shanghai, China
Duration: 5 Jul 2020 → 5 Jul 2020

Conference

ConferenceCDRF 2020
PlaceChina
CityShanghai
Period5/07/20 → 5/07/20

Bibliographical note

Publication date information for this publication is provided by the author(s) concerned.

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

Fingerprint

Dive into the research topics of 'Pipes of AI - Machine Learning Assisted 3D Modelling Design'. Together they form a unique fingerprint.

Cite this