The documentation and enhancement of Cultural Heritage require under-standing and conveying the meaning of historical signs in a specific context. Today, this process leverages the most advanced digital representation systems, focusing on computer vision and information and communication technology. These systems open new horizons for the narrative of archaeology (Gabellone, Capitale culturale e capitale umano. L’innovazione al servizio della Cultura, LuBeC, Lucca, 2016).
Point Cloud Data Semantization for Parametric Scan-to-HBIM Modeling Procedures
This paper proposes a structured methodology for converting survey data into semantically enriched HBIM models through Scan-to-HBIM workflows. The research integrates point cloud acquisition, segmentation, and parametric modeling within a hierarchical data framework aimed at generating reusable and information-rich digital representations of historical architecture.
The workflow combines terrestrial laser scanning, mobile SLAM, and photogrammetry, followed by post-processing and semantic segmentation to isolate architectural components. These elements are then classified and linked to parametric BIM families through a taxonomy-based system, enabling the creation of adaptive Historic Building Object Models (HBOM). The approach supports both geometric accuracy and information management, facilitating interpretation, conservation planning, and model reuse. Results highlight the effectiveness of semantic structuring in improving HBIM processes, while confirming the need for case-specific modeling strategies due to the complexity of built heritage.