## 4 Method
### 3D Survey: Aerial and Terrestrial Photogrammetry of the Church of San Valentino in Siusi, Italy
3D survey aimed to obtain an accurate non-invasive digital data collection, achieving the best point clouds (primary data sources) for each architectural and structural element. A wide variety of types characterises stone and brick vaults, so the importance of constructive techniques has played a key role in the reverse engineering of each architectural component. In addition to laser scanning, the application of advanced photogrammetry techniques also allows the acquisition of the shadow areas of the 3D scans and generates true orthophotos of every single architectural element simultaneously. The building's components were identified directly in the field thanks to historical documents and 2D drawings (secondary data sources), which completed the interpretive analysis of the building, description of pathologies, and techniques and materials used for the construction. In this context, as is known, at the beginning of the transformation process (from scan to photogrammetric model), point cloud processing applications lead to an automatic generation of mesh models. Software as ContextCapture and Agisoft Metahsape enables the automatic creation of mesh from dense point cloud thanks to specific algorithms that recognise the scan's point like the data source to generate the mesh's polygons. Mesh interprets the complexity of the shapes through the union of points through polygons based on different algorithms. This type of model corresponds to a sparse representation of a point cloud in the space. To convert a point cloud in a mesh is similar to creating a significant number of polygons anchoring their vertices to each point of the scan. Figure 6 shows how mesh models of the church try to interpret complex shape using a sparse set of points, converting a point cloud into a significant number of polygons. To perform modelling tests to determine the best scan-to-BIM process found that polygon modelling represents a huge constraint for the model generation and the subsequent management of mesh models in BIM application is not aligned with the software's logic itself. Modelling and import tests between free-form software and BIM application found that the mesh model's physical features represent the main problem for mutual recognition in both applications. In particular, the high number of polygons that try to simulate a continuous curved surface increases the size of the model and obstructs the proper functioning of both software (free-form modelling software and BIM application). For this reason, the studies and tests on generative modelling have considered the mesh as a modelling data source but not a possible output for the implementation of XR projects. In particular, as will be explained in the following paragraphs, the first reason was the impossibility to transform polygonal models in a BIM parametric environment because of their physical features such as size, weight, accuracy, and not smooth shape. In the last years, to overcome the issues related to mesh modelling, the technique applied to reverse engineering was based on the generation of cutting planes, complex curves, and slices to represent the main characteristics of curved shapes detected by laser scanners and digital photogrammetry. This segmentation of the point clouds is called slicing. The point-cloud segmentation technique has spread rapidly to different types of disciplines, reaching high LOD and creating complex HBIM projects. Slicing requires a proper interpretation of the right point cloud portion and a significant number of slices (curves from scans). Most of the modelling software allowed the segmentation of point clouds through new tools able to obtain the needed slices with 2D cross-section planes. Once the required slices are obtained, the user can follow the slice by manually drawing the generative curves. Accordingly, most 3D modelling for reverse engineering projects is based on slicing, which requires geometric primitives such as B-splines (Figure 7).