Semantic segmentation of images of building facilities

Scan-to-BIM is the process of converting a 3D reconstruction into a building information model (BIM). The process has two parts: (1) sorting subsets of the reconstruction into classes (semantic segmentation) defined by a BIM taxonomy and (2) identifying geometric parameters describing each class instance. Here we demonstrate the ability of deep learning artificial neural networks to semantically segment images of building facilities. We found this deep learning approach capable of simultaneously recognizing: ceiling, wall, plumbing, duct, door, floor, and stairs classes. This semantic scope surpasses state-of-the-art building system recognition methods and represents progress towards comprehensive BIM creation.