Automatic Scan-to-BIM—The Impact of Semantic Segmentation Accuracy

Abstract

Scan-to-BIM is the process of converting point cloud data into a Building Information Model (BIM) that has proven essential for the AEC industry. Scan-to-BIM consists of two fundamental tasks—semantic segmentation and 3D reconstruction. Deep learning has proven useful for semantic segmentation, and its integration into the Scan-to-BIM workflow can benefit the automation of BIM reconstruction. Given the rapid advancement of deep learning algorithms in recent years, it is crucial to analyze how their accuracy impacts reconstruction quality. In this study, we compare the performance of five deep learning models—PointNeXt, PointMetaBase, PointTransformer V1, PointTransformer V3, and Swin3D—and e..