  We study the use of parametric building information modeling (BIM) to
automatically generate training data for artificial neural networks (ANNs) to
recognize building objects in photos. Teaching artificial intelligence (AI)
machines to detect building objects in images is the foundation toward
AI-assisted semantic 3D reconstruction of existing buildings. However, there
exists the challenge of acquiring training data which is typically
human-annotated, that is, unless a computer machine can generate high-quality
data to train itself for a certain task. In that vein, we trained ANNs solely
on realistic computer-generated images of 3D BIM models which were
parametrically and automatically generated using the BIMGenE program. The ANN
training result demonstrated generalizability and good semantic segmentation on
a test case as well as arbitrary photos of buildings that are outside the range
of the training data, which is significant for the future of training AI with
generated data for solving real-world architectural problems.
