Cumincad : CUMINCAD Papers : Paper cdrf2022_371:Collapsing Complexities: Encoding Multidimensional Architecture Models into Images

id	cdrf2022_371
authors	Viktória Sándor, Mathias Bank, Kristina Schinegger, and Stefan Rutzinger
year	2022
title	Collapsing Complexities: Encoding Multidimensional Architecture Models into Images
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_32
summary	The paper details a 3D to 2D encoding method, which can store complex digital 3D models of architecture within a single image. The proposed encoding works in combination with a point cloud notation and a sequential slicing operation where each slice of points is stored as a single row of pixels in the UV space of a 1024?×?1024 image. The performance of the notation system is compared between a StyleGan2 and existing image editing methods and evaluated through the production of new 3D models of houses with material attributes. The uncovered findings maintain the relatively high level of detail stored through the encoding while allowing for innovative ways of form-finding—producing new and unseen 3d models of architectural houses.
series	cdrf
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last changed	2024/05/29 14:03