## 3 Results
### Latency Sensibility Analysis
#### 3.2.3 Analysis Mesh-Latency  
The analysis of latency variability in response to variable model size and level of details is a crucial step in understanding the performance of the AR application on the holographic table and its potential impact on user experience and motion sickness symptoms. The four models that were analyzed include the following (Table 1):  
* Two highly detailed surveys of \"Torretta Viscontea\" in Monza, Italy, performed with professional instruments and tools, exported with different levels of detail;
* One low-detailed survey of \"Torretta Viscontea\" using a toolchain workaround for comparison;
* One low-detailed survey of the entire \"Parco di Monza\" with a total surface of 688 km\({}^{2}\) using the same toolchain workaround. The recorded latency and performance data of the application, in conjunction with user responses and potential motion sickness symptoms, provide valuable insights into the relationship between the model size, level of details, application performance, and user comfort. By analyzing the data, patterns and correlations can be identified to understand how the different models impact the application's performance and user experience. The analysis reveals that the highly detailed surveys, due to their larger size and higher level of details, result in excessively increased computational costs, rendering performance, and latency, which potentially critically impact the overall stability and accuracy of the 3D model on the holographic table. This may lead to visual artifacts and motion sickness symptoms in users. On the other hand, the low-detailed surveys, despite their lower level of details, have better performance and lower latency due to their smaller size and reduced computational costs. \begin{table}
\begin{tabular}{l l l l l l} \hline  & **Vertices** & **Faces** & **Size [MB]** & **File ext.** & **Google** \\ \hline High quality & 8.510.529 & 17.000.000 & 1.810 & Obj & No \\ Low quality & 580.488 & 913.723 & 731 & Obj & No \\ “Torretta & 5.215 & 5.025 & 1.42 & Fbx & Yes \\ Nonza Park & 487.780 & 335.890 & 14. & Fbx & Yes \\ \hline \end{tabular}
\end{table} 
Table 1: File size and characteristics of the tested models. The findings of the analysis provide potential strategies for optimizing the performance of the application, such as optimizing rendering processes, reducing computational costs, and mitigating visual artifacts, in order to enhance user comfort and reduce the likelihood of motion sickness. Additionally, the analysis aims at helping to identify the optimal balance between the model size, level of details, and application performance to provide a smooth and enjoyable user experience without compromising the stability and accuracy of the 3D model on a holographic table [28]. The analysis of file and model size differences between high-accuracy and low-accuracy models is essential to understand the applicability and quality of lightweight low-accuracy models in the context of the AR application on the holographic table. To assess the quality of those models, Cyclone 3 DR was used to run a quality analysis by comparing the high-accuracy point clouds of \"Torretta Viscontea\" with their low-accuracy counterparts obtained using the FOSS (Free and Open-Source Software) toolchain [29]. The first comparison performed was based on the covered surface of the models. It was observed that the high-accuracy surveys covered a surface of 1879 square meters, with significantly high accuracy in purely quantitative terms. This indicates that the low-accuracy models can capture a large portion of the surface details of the high-fidelity models, despite their reduced file size and lower level of details. The next comparison was performed in terms of adherence, which measures how accurate the low-accuracy models are compared to the most precise digitized version of the model.