Extending design cognition with computer vision and generative deep learning

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2022-2

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Çakmak, Başak

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This thesis proposes to scrutinize the project of extending cognition in the field of design by providing the exploration of alternative procedures of formalization. It aims at investigating the possible extensions of design cognition with a theoretical framework based on the concept of Extended Cognition, traced within two different contexts: One is the phenomenological context, which aims at a sensual and cognitive extension in the design studies of the 20th century searching for a counter-cultural expression; the other is the naturalized context of Extended Cognition, now reconceptualized within the field and paradigm of the cognitive and computational sciences, away from its phenomenological antecedents. The thesis aims at bringing current cognitive and computer science advancements into the design theory by reconsidering the notion of Extended Cognition within a fully naturalized context, and to this aim, explores a methodology to consider and apply the underlying principles of Computer Vision and Generative Deep Learning methods. An interdisciplinary project is developed for testing the approach discussed in the thesis, in which a 3-D point cloud model of a physical structure is produced by LiDAR scanner. Various experiments are carried out with this data, where the datasets created are processed by a Deep Generative Model to produce new 3-D models that are embodied in alternative formal representations such as point clouds, mesh models, and bounding boxes. The generated models are then used as a design vocabulary to produce alternative combinatorial formations that reflect the extensions of design cognition.

Subject Keywords

Design cognition, Point-cloud, Computer vision, Deep generative models

URI

https://hdl.handle.net/11511/96232

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

B. Çakmak, “Extending design cognition with computer vision and generative deep learning,” M.Arch. - Master of Architecture, Middle East Technical University, 2022.