Data and Parameterization Requirements for 3D Generative Deep Learning Models

Abstract

It is now within reach to use generative artificial intelligence (AI) to autonomously generate full building geometries. However, existing literature utilizing 3D data has focused to a limited degree on architecture and engineering disciplines. A critical first step to expanding the use of generative deep learning models in generative design research is making training data available. This study investigates 3D building model data characteristics that make it suitable for generative AI applications. Key data set attributes are identified through a systematic review of the object-containing datasets currently used to train state-of-the-art 3D GANs. These requirements are then compared to attributes of existing available building datasets. This comparison shows that publicly available data sets of 3D building models lack essential characteristics for generative deep learning. Features that make these building models inadequate for the task include but are not limited to, their mesh formats, low resolution and levels of detail, and inclusion of irrelevant geometry. To achieve the desired properties in this work, necessary transformations of the data are incorporated into a tailored preprocessing pipeline. The pipeline is applied to an existing dataset that contains 3D models of single-family homes. The transformed dataset is tested within state-of-the-art GAN models to assess training performance and document future data requirements for applying deep generative design to buildings. Our experiments show promise for the impact that architectural datasets can make on deep learning applications within the discipline. It also highlights the need for additional 3D building model data to increase the diversity and robustness of new designs.