Representations in design computing through 3-D deep generative models

Çakmak B., ÖNGÜN C.

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, vol.38, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38
  • Publication Date: 2024
  • Doi Number: 10.1017/s0890060424000106
  • Journal Name: Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: 3-D deep generative models, computational design, deep learning, point cloud
  • Middle East Technical University Affiliated: Yes

Abstract

This paper aims to explore alternative representations of the physical architecture using its real-world sensory data through artificial neural networks (ANNs). In the project developed for this research, a detailed 3-D point cloud model is produced by scanning a physical structure with LiDAR. Then, point cloud data and mesh models are divided into parts according to architectural references and part-whole relationships with various techniques to create datasets. A deep learning model is trained using these datasets, and new 3-D models produced by deep generative models are examined. These new 3-D models, which are embodied in different representations, such as point clouds, mesh models, and bounding boxes, are used as a design vocabulary, and combinatorial formations are generated from them.