Abstract
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.
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Acknowledgements
This study was developed within the scope of the thesis [19] titled "Extending Design Cognition with Computer Vision and Generative Deep Learning," supervised by Prof. Dr. Zeynep Mennan at METU Department of Architecture. Original drawings of the building were provided by the Directorate of Construction and Technical Services at Middle East Technical University (METU). In the experiments, the LiDAR Scanner model was developed by Kemal Gülcen through the Photogrammetry laboratory of the Faculty of Architecture. We would like to thank the deanery of the Faculty of Architecture for allowing the use of data.
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Çakmak, B., Öngün, C. (2023). Representing Design Cognition Through 3-D Deep Generative Models. In: Gero, J.S. (eds) Design Computing and Cognition’22. DCC 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-20418-0_18
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DOI: https://doi.org/10.1007/978-3-031-20418-0_18
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