  The availability of rich 3D datasets corresponding to the geometrical
complexity of the built environments is considered an ongoing challenge for 3D
deep learning methodologies. To address this challenge, we introduce GenScan, a
generative system that populates synthetic 3D scan datasets in a parametric
fashion. The system takes an existing captured 3D scan as an input and outputs
alternative variations of the building layout including walls, doors, and
furniture with corresponding textures. GenScan is a fully automated system that
can also be manually controlled by a user through an assigned user interface.
Our proposed system utilizes a combination of a hybrid deep neural network and
a parametrizer module to extract and transform elements of a given 3D scan.
GenScan takes advantage of style transfer techniques to generate new textures
for the generated scenes. We believe our system would facilitate data
augmentation to expand the currently limited 3D geometry datasets commonly used
in 3D computer vision, generative design, and general 3D deep learning tasks.
