A continuous scan-to-HBIM workflow based on integrated multisensor acquisition and real-time semantic modelling

The increasing adoption of Building Information Modeling (BIM) in the AECO sector has highlighted persistent limitations in Scan-to-HBIM workflows, particularly related to fragmentation, manual processing, and lack of continuity between data acquisition and modeling. This study proposes and validates a continuous Scan-to-HBIM workflow based on integrated multisensor acquisition and real-time semantic modeling, aiming to reduce these discontinuities and improve data consistency. The method is implemented through an all-in-one platform combining mobile LiDAR, photogrammetry, sensor fusion (IMU–SLAM), machine learning for semantic segmentation, and extended reality (XR) for in-field validation, enabling the direct generation of parametric BIM elements during acquisition. The approach is tested on the ex Mulino Gallisai, a complex and degraded heritage building, using a controlled benchmarking protocol against a traditional pipeline. Results show high metric reliability (MAE = 1.68 cm), semantic recognition accuracy of 88.2%, and a Manual Correction Ratio of 11.8%, indicating reduced human intervention. The integrated workflow also achieves a 29% reduction in total processing time while improving spatial continuity and topological coherence. These findings demonstrate that a continuous, integrated Scan-to-HBIM paradigm is technically feasible and can shift modeling from a post-process reconstruction to a real-time generative process, supporting more efficient and reliable digital representations and contributing to the development of Digital Twin-oriented workflows.