Digital technology provides methods to record and preserve cultural heritage, support conservation and restoration efforts, and share our collective past with a worldwide audience. Between 2011 and 2017, the 3D Survey Group from Politecnico di Milano operated an annual workshop in the medieval village of Ghesc in which photogrammetry and laser-scanner surveys were carried out. The point cloud data acquired in these activities has become "time slices" documenting different stages of the preservation interventions in Ghesc and the evolution of advanced survey techniques. The main objective of this research is to streamline the workflow of delivering immersive and interactive experiences for complex heritage by directly utilising the 3D survey point cloud data, whether derived from a photogrammetric survey, static laser scanner, or mobile mapping. A point cloud-based multiplatform application is designed and delivered with versatile functions. It runs on PC and VR devices to provide virtual access to the village and narrate its revitalisation story. Additionally, it operates on mobile devices with an AR feature that brings vibrancy to the on-site experience. This application integrates high-fidelity point cloud models, detailed information on vernacular architecture in the Ossola Valley, and information on the preservation project with gamified learning experiences. The unconventional approach of using points as rendering primitives in virtual applications offers a practical solution for visualising complex heritage, enabling an efficient transition from the data collection stage to the data sharing stage without the need for 3D reconstruction and intricate BIM modelling.
CITATION STYLE
Lei, Y., Fiorillo, F., & Fassi, F. (2024). 3d survey point cloud data as direct rendering assets for visualising complex heritage in virtual applications. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 48, pp. 279–286). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-Archives-XLVIII-2-W4-2024-279-2024
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