FULLY AUTOMATED SCAN-TO-BIM VIA POINT CLOUD INSTANCE SEGMENTATION

Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and analyzing existing buildings. However, the acquired data are noisy and unstructured, and the creation of a semantically meaningful BIM representation requires a huge computational effort, as well as expensive and time-consuming human annotations. In this paper, we propose a fully automated scan-to-BIM pipeline. The approach relies on: (i) our dataset (HePIC), acquired from two large buildings and annotated at a point-wise semantic level based on existent BIM models; (ii) a novel ad hoc deep network (BIM-Net++) for semantic segmentation, whose output is then processed to extract instance information necessary to recreate BIM objects; (iii) novel model pretraining and class re-weighting to eliminate the need for a large amount of labeled data and human intervention.