## 4 Results

Table 4: Calculation of relative errors in Experiment 1. Figure 4: Measurement of length (**a**) and volume (**b**) in the objects imported into iTwin Capture Desktop software ([[https://www.bentley.com/software/itwin-platform/](https://www.bentley.com/software/itwin-platform/)]([https://www.bentley.com/software/itwin-platform/](https://www.bentley.com/software/itwin-platform/)), accessed on 3 November 2024). ning parameters was identified by analyzing the effects of the different mean standardized values of each parameter level on \(\rm E_{o}\). The ANOVA analysis of the parallelepiped game box is summarized in Table 6. This table includes the number of degrees of freedom, DF, the adjusted sum of squares, Adj SS, the adjusted mean squared, Adj MS, the ratio of the variance between the groups to the variance within the groups, F-value, and the probability of observing the data, or something more extreme, assuming that the null hypothesis (all means are equal) is true in accordance with the \(p\)-value. The analysis of this table shows that in this object the distance to the object, the speed, and the lighting conditions are statistically significant as their \(p\)-values are lower than the reference value of 0.05. On the contrary, both the sensor angle and the number of passes do not have a statistically significant impact on the means of the experiments; in other words, they are not statistically representative. To identify the optimal levels among the three analyzed scanning parameters that are statistically significant, a Taguchi analysis was conducted, focusing on the lowest error for each significant parameter level. For the parallelepiped game box, the distance with the lowest error corresponds to level 1, which is 0.5 m. The sensor speed that yielded the lowest error is level 3, which represents the slow speed. Finally, the optimal lighting condition is \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **DF** & **Adj SS** & **Adj MS** & \(F\)**-Value** & \(p\)**-Value** \\ \hline Distance & 2 & 0.26367 & 0.13183 & 85.22 & 0.000 \\ \hline Speed & 2 & 0.02007 & 0.01003 & 6.49 & 0.025 \\ \hline Sensor angle & 2 & 0.00349 & 0.00174 & 1.13 & 0.376 \\ \hline Lighting & 2 & 0.01704 & 0.00852 & 5.51 & 0.037 \\ \hline Number of passes & 2 & 0.01050 & 0.00525 & 3.39 & 0.093 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the ANOVA for the parallelepiped game box. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\rm E_{o}\) & **Object 1** & **Object 2** & **Object 3** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 5: Obtained Overall Errors, \(\rm E_{o}\). level 1, which is artificial low lighting. The effects of the different levels of the statistically significant scanning parameters in the errors are summarized in Table 7.  
The ANOVA analysis of the Rubik's cube block is summarized in Table 8. This analysis reveals that, unlike the parallelepiped game box, none of the scanning parameters were statistically significant, as all \(p\)-values exceeded 0.05. Table 9 provides a summary of how the various levels of the different scanning parameters impact the errors in the analysis of this object. Finally, the results of the ANOVA analysis for the Buddha figure are summarized in Table 10. This analysis indicates that the only statistically significant scanning parameter for this object was the distance to the object, with a \(p\)-value of 0.043, which is less than 0.05. This parameter is highlighted in bold in the table. This parameter is highlighted in bold in the table. Additionally, Table 11 provides a summary of how the different distance levels affect measurement errors for this object. The analysis shows that, similar to the parallelepiped game box, the optimal distance level is the first one, 0.5 m. \begin{table}
\begin{tabular}{c c c c} \hline
**Level** & **Distance** & **Speed** & **Lighting** \\ \hline
1 & 0.0503 & 0.1860 & 0.1109 \\ \hline
2 & 0.0568 & 0.1205 & 0.1819 \\ \hline
3 & 0.3102 & 0.1109 & 0.1246 \\ \hline \end{tabular}
\end{table}