## 6 Discussion  
### Assessment of AR Placement Methods  
The present evaluation approach aimed to assess the placement performance of a 3D model using several AR methods and solutions. Several approaches have been tested, discussed and assessed based on two criteria: placement accuracy and model stability. Furthermore, several influencing factors have been studied to identify the optimal conditions to be respected on site.  
In case of marker-based methods, the AR scene is influenced by the quality of the markers, the maturity of the AR SDKs and the complexity of the virtual models [60] as well as the distance from the virtual object to the marker, the size and the spatial arrangement of the markers [13]. The results of our tests for the three studied factors (distance to the marker, the SDK and the complexity of the model) have shown that marker-based methods require a preliminary study before deploying the solution, taking into consideration the expected objectives. The accuracy and stability of placement and the time required for development will be the matter of a pre-study based on the requirements. For the case of the marker-based method by Vuforia SDK, this technique may not be usable on site because in order for the model to be augmented and stable, the camera must be close to the markers at all times, which is difficult in the case of a large construction with occlusions.  
Tests show that the stability of marker-based AR solutions is relatively low, which is significantly influenced by the quality of the markers and the maturity of the AR SDKs. Thus, to develop a stable marker-based AR application, we need to select an appropriate AR SDK, create high-quality markers, and even optimize existing feature recognition algorithms as needed; this was confirmed by the study of [62].  
In the case of marker-less methods, the tests demonstrated that there is a clear distinction between the various methods and solutions used. The algorithm developed is the main difference between these approaches. The ARcore SDK case has demonstrated perfect 3D model stability and placement accuracy. This is the case with the Site-Vision and GAMMA AR solution, which has shown good results in terms of model stability and placement accuracy. However, due to the RTX network's poor coverage in the study site, the automatic placement mode generates metric stability and accuracy results. It can be stated that marker-less methods remain an unacceptable choice in the construction and 3D model application. The strength of these methods lies in the development of algorithms without limits of applications suitable for the specificities of the construction. Indeed, these methods are not constrained by the presence of elements exogenous to the construction (markers and the accuracy of its location in the real environment).  
Consistent with previous studies findings, developers of marker-less AR-3D model applications can either use better localization technology and better hardware to improve stability and accuracy (using Trimble SiteVision) or use vision-based methods with powerful algorithms.  
Finally, our research brings an interesting contribution in the context of AR placement methods. It fills in a gap in the literature by proposing an assessment approach for state-art placement methods in AR. Such an evaluation gives a good basis both for the scientific community and the professionals for guiding the choice of the adequate AR placement method for a given context. This last contribution is discussed in the next section.