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

GenScan: A Generative Method for Populating Parametric 3D Scan Datasets