# Situated AR Simulations of a Lantern Festival Using a Smartphone and LiDAR-Based 3D Models
## 5 Implementation of the 3D Scan Data  
The 3D data were implemented for cultural preservation, context extension, and the spatial structure analysis of a sustainable festival. The preservation was conducted with more of a direct connection to our daily life, in which the memory should be reserved and recalled. The presentation of structure was exemplified by the definition of spatial frames and elements, such as the canopy and ground level, and infills, including installations and crowds. The structure led to an appropriate representational hierarchy of the scenes to be displayed. Indeed, any part of the framework or lantern installation was feasible for a homogeneous and heterogeneous combination of contexts. The mesh model of multiple objects or the point cloud of the entire site practically correlate the data in different hierarchies from field work and laboratory experiments to different sites. Both data types were able to be interacted with. The app application and development followed the layout of objects and the spatial structure of an event. Since the off-site instancing experience is to be extended to other architectural spaces or urban fabrics, explorations were made of different development approaches, data retrieval, format conversions, app platforms, and instancing scenarios. 3D scan data were utilized to define festival entities and their correlations, both visually and physically. Faro Focus 3D(r) (FARO Technologies Inc., Stuttgart, Germany) was used to scan the 2016 Taipei Lantern Festival. Field work included 37 scan locations, in which more than 19 GB of data or 543 million points were retrieved in a region approximately 300 m wide and 250 m long. The subsequent data manipulation effort constituted more than 10 times that of the field effort. A detailed description and figures of the methods applied can be seen in Figure 2, which included registration inspection, filtering and decimation, scene segmentation, texture adjustment, and format conversion. It is worth noting that holes were purposely not filled to prevent misleading outcomes of the configuration in over-crowded scans. Two approaches were utilized. The first one used a public platform to engage the point cloud as the type of data to interactively manipulate in VR. The second was an app developed specifically for the mesh model, converted from the point cloud in AR. Both types of interactions have to be made on a smartphone platform for the different scenarios of site contexts. The point cloud model was also able to create a mesh model for AR user interaction (Figure 3). Although photogrammetry can create a large area of terrain model using images taken by an Unmanned Aerial Vehicle (UAV) or Unmanned Aircraft Systems  
Figure 2: The data flowchart. (UAS), the mid-range 3D scanner captures small objects, such as power wires, stage frames, lamp poles, tree branches (Figure 4), and installations on building facades with sufficient detail to facilitate inspection. In addition, crowds were also used as a specific element for the reference of scale (Figure 5), and the real depth was usually applied by Depth API (ARCore 1.18, Google Inc., Mountain View, CA, USA) [61]. The model worked with software, including CloudCompare(r) (v2.6.1, EDF R&D, Paris, French) [62], Geomagic Studio(r) (v2014.1.0, former Raindrop Geomagic Inc., Morrisville, NC, USA, now 3D Systems Company, Rock Hill, SC, USA) [63], Meshlab(r) (v1.3.3, Visual Computing Lab-ISTI-CNR, Pisa, Italy) [64], and Autodesk Revit(r) (2020, Autodesk Inc., San Rafael, CA, USA) [65], to review the model, convert formats, and retrieve sections, skylines, ground plans, and images. In addition to the color illustrated on 3D point clouds, an Eye Dome Lighting (EDL) rendering process [66] was applied to differentiate physical elements, particularly in a colorless mode for a better display of edges, depths, or silhouettes (Figure 5b) during the study process.