## 5 Results
### Placement Accuracy  
To examine placement accuracy, we chose two indicators that are commonly used in comparison tests; both indicators consider RMSE and standard deviation. We consider four successive measurements for each point during the measurement process in order to calculate the standard deviation (stability) and average and compare them with the actual measurement to obtain the deviation indicating accuracy. The results in terms of accuracy of AR placement methods are reported in Table 2 and discussed in the following paragraphs. Figure 15: (**a**) Augmentation of a single model entity (beam). (**b**) Augmentation of the whole building by the marker-based method. * Marker-based methods  
Marker-based placement methods can be used to position small virtual objects, such as a beam, with a negligible placement error [60]. According to Table 2, the ARcore method allows achieving an accuracy of 9 cm indoors and 16 cm outdoors. These values are degraded when switching to Vuforia SDK with an accuracy of 13 cm indoors and 20 cm outdoors. Furthermore, we notice that both methods are affected by the change from the indoor environment to the outdoor; this is mainly caused by the influence of the exterior environment and lighting conditions, which can considerably influence the correct detection of markers (Figure 15). Figure 16 illustrates the results obtained by marker-based methods. It can be concluded that marker-based methods are generally accurate in an indoor environment; however, this accuracy of placement may deteriorate with distance from the marker, particularly in the case of Vuforia, as it has been reported in Figure 12.  
* Marker-less methods  
Marker-less methods are generally insensitive to the environmental conditions, except for methods not based on a 3D model, where there is a significant difference due to the quality of the manual adjustment method of the model after a first placement based on the detection of the planes by the visual SLAM (V-SLAM) method. In an indoor environment, marker-less model-based methods give an offset value of 9 cm between the virtual model and the reality. This value deteriorates considerably up to 19 cm for the method not based on a prior 3D model of the building. The accuracy deteriorates for both methods outdoors, reaching 11 cm for model-based methods and 40 cm for no model-based methods. The \"GAMMA AR\" method offers a simple and intuitive manual adjustment of the model which gives the same degree of accuracy for every AR experience. However, this solution is commercial and remains inaccessible to all users (Figure 17a). For SiteVision (Figure 17b,c), we used two methods of placement, namely manual placement and automatic placement. We tested the manual placement in both indoor and outdoor environments \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{1}{c}{**Placement Method**} & \multicolumn{2}{c}{**RMSE (m)**} \\ \cline{3-4}  & & **Indoor** & **Outdoor** \\ \hline \multirow{2}{*}{**Marker-based methods**} & ARcore & 0.09 & 0.16 \\ \cline{2-4}  & Vuforia & 0.13 & 0.20 \\ \hline \multirow{6}{*}{**Marker-less methods**} & SiteVision (Manuel placement) & 0.07 & 0.07 \\ \cline{2-4}  & GAMMA AR & 0.08 & 0.08 \\ \cline{2-4}  & Marker-less (Model-based) & 0.09 & 0.11 \\ \cline{2-4}  & Marker-less (No model-based) & 0.19 & 0.4 \\ \cline{2-4}  & SiteVision (Automatic placement) & - & 3.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy of placement (RMSE) for the studied methods. Figure 16: Comparison between Vuforia and ARcore. which gave the same results (7 cm); this is justified by the fact that the operator readjusts the virtual model without any prior knowledge of the physical environment. For automatic placement, we initiated this mode only outdoors (since it is GNSS based) and we obtained an accuracy of around 3 m. This is explained by the low coverage of the RTX network at the study site.