Representations in design computing through 3-D deep generative models

Date

2024-12-10

Author

Çakmak, Başak
Öngün, Cihan

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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.

Subject Keywords

3-D deep generative models, computational design, deep learning, point cloud

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85212036673&origin=inward
https://hdl.handle.net/11511/112890

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Graduate School of Informatics, Article