Representing Design Cognition Through 3-D Deep Generative Models

Cakmak B., ÖNGÜN C.

10th International Conference on Design Computing and Cognition (DCC), Glasgow, England, 01 January 2022, pp.289-304 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-3-031-20418-0_18
  • City: Glasgow
  • Country: England
  • Page Numbers: pp.289-304
  • Middle East Technical University Affiliated: Yes


This paper aims to explore alternative representations of the physical architecture using its real-world sensory data through Artificial Neural Networks (ANNs), which is a simulated form of cognition having the ability to learn. 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.