This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.
The act of changing the direction of a sheet surface along a non-straight curve is a specific case of curved folding. From an architectural point of view, curved folding is an exciting operation. One or a couple of operation can generate highly complex shell-like spatial enclosure. From a digital design perspective, the implementation of curved folding with the built-in toolsets of available computer-aided design softwares is a challenging problem. The equilibrium state of curved folded geometry is needed to be found with a computational form-finding strategy. To use curved folding as a digital design operation, we introduce a new tool through developing a digital procedure for form-finding. The tool we develop can enable the experimentation with curved folding in the early stage of design process and facilitate the subsequent design development. In this article, we briefly present the literature focusing on curved folding in computational geometry, as well as the scope and description of a subclass of curved folding operation. Then, we introduce a digital tool, CURVED.IT through a design manual for its implementation and an algorithmic framework for its extension. Lastly, we discuss the design examples generated by CURVED.IT, and the potentials of the tool.
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