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

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture