Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and ana- lyzing existing buildings. Its pipeline starts with geometric acquisition. (e.g., via photogrammetry or laser scanning) for accurate point cloud collection. 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 annota- tions. In this paper, we propose a fully automated scan-to-BIM pipeline. The approach relies on: (i) our dataset (HePIC), ac- quired 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 informa- tion necessary to recreate BIM objects; (iii) novel model pre- training and class re-weighting to eliminate the need for a large amount of labeled data and human intervention.
Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation
Borin, P.;
2023-01-01
Abstract
Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and ana- lyzing existing buildings. Its pipeline starts with geometric acquisition. (e.g., via photogrammetry or laser scanning) for accurate point cloud collection. 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 annota- tions. In this paper, we propose a fully automated scan-to-BIM pipeline. The approach relies on: (i) our dataset (HePIC), ac- quired 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 informa- tion necessary to recreate BIM objects; (iii) novel model pre- training and class re-weighting to eliminate the need for a large amount of labeled data and human intervention.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
