  Deep learning is expected to offer new opportunities and a new paradigm for
the field of architecture. One such opportunity is teaching neural networks to
visually understand architectural elements from the built environment. However,
the availability of large training datasets is one of the biggest limitations
of neural networks. Also, the vast majority of training data for visual
recognition tasks is annotated by humans. In order to resolve this bottleneck,
we present a concept of a hybrid system using both building information
modeling (BIM) and hyperrealistic (photorealistic) rendering to synthesize
datasets for training a neural network for building object recognition in
photos. For generating our training dataset BIMrAI, we used an existing BIM
model and a corresponding photo-realistically rendered model of the same
building. We created methods for using renderings to train a deep learning
model, trained a generative adversarial network (GAN) model using these
methods, and tested the output model on real-world photos. For the specific
case study presented in this paper, our results show that a neural network
trained with synthetic data; i.e., photorealistic renderings and BIM-based
semantic labels, can be used to identify building objects from photos without
using photos in the training data. Future work can enhance the presented
methods using available BIM models and renderings for more generalized mapping
and description of photographed built environments.
