  With the growing interest in deep learning algorithms and computational
design in the architectural field, the need for large, accessible and diverse
architectural datasets increases. We decided to tackle this problem by
constructing a field-specific synthetic data generation pipeline that generates
an arbitrary amount of 3D data along with the associated 2D and 3D annotations.
The variety of annotations, the flexibility to customize the generated building
and dataset parameters make this framework suitable for multiple deep learning
tasks, including geometric deep learning that requires direct 3D supervision.
Creating our building data generation pipeline we leveraged architectural
knowledge from experts in order to construct a framework that would be modular,
extendable and would provide a sufficient amount of class-balanced data
samples. Moreover, we purposefully involve the researcher in the dataset
customization allowing the introduction of additional building components,
material textures, building classes, number and type of annotations as well as
the number of views per 3D model sample. In this way, the framework would
satisfy different research requirements and would be adaptable to a large
variety of tasks. All code and data are made publicly available.
