## 4 Performance Analysis of the Generated Models in Virtual Environments  
After comparing and evaluating the 3D models generated by the various scanning tools based on different characteristics, we proceeded to measure their performance on Meta's VR headsets: Quest 2, Quest Pro, Quest 3. To this end, we imported the.obj files of the models into a Unity project, where we configured scenes that were subsequently built and deployed on the headset for native execution, aiming to collect performance metrics without interference from the hardware of the connected PC. The chipset, GPU and RAM memory of the Meta Quest devices used are as follows:  
* Meta Quest 2. Chipset: Qualcomm Snapdragon XR2, 8 cores (1 \(\times\) 2.84 GHz, 3 \(\times\) 2.42 GHz, 4 \(\times\) 1.8 GHz), integrated GPU: Qualcomm Adreno 650 @ 587 MHz, 1267 GFLOPS. RAM: 6 GB.
* Meta Quest Pro. Chipset: Qualcomm Snapdragon XR2+, 8 cores (1 \(\times\) 2.84 GHz, 3 \(\times\) 2.42 GHz, 4 \(\times\) 1.8 GHz), integrated GPU: Qualcomm Adreno 650 @ 587 MHz, 1267 GFLOPS. RAM: 12 GB.
* Meta Quest 3. Chipset: Qualcomm Snapdragon XR2 Gen 2, 6 cores (1 \(\times\) 3.19 GHz, 2 \(\times\) 2.8 GHz, 3 \(\times\) 2.0 GHz) GHz, integrated GPU: Qualcomm Adreno 740 @ 492 MHz, 3.1 GFLOPS. RAM: 8 GB.  
While it would have been preferable to include a more comprehensive range of VR headset models in the study, financial constraints within the research project necessitated a selective approach. The three Meta models were selected for their potential to facilitate an examination of the progression among models that share a direct lineage. Furthermore, the Meta Pro model is equipped with internal cameras, which will facilitate the integration of eye tracking in future research. The Meta Quest 3 model supports switching between virtual reality and augmented reality, which is also promising for subsequent investigations. Two further reasons influenced the decision to procure and utilise the Meta models in the study. Firstly, they are the best-selling models currently, as confirmed by data from sources such as Counterpoint ([[https://www.counterpointresearch.com/insights/global-xr-ar-vr-headsets-market-share/](https://www.counterpointresearch.com/insights/global-xr-ar-vr-headsets-market-share/)]([https://www.counterpointresearch.com/insights/global-xr-ar-vr-headsets-market-share/](https://www.counterpointresearch.com/insights/global-xr-ar-vr-headsets-market-share/))), Statista ([[https://www.statista.com/topics/10052/vr-headsets/](https://www.statista.com/topics/10052/vr-headsets/)]([https://www.statista.com/topics/10052/vr-headsets/](https://www.statista.com/topics/10052/vr-headsets/))) and Amazon (accessed on 7 July 2024). Secondly, they exhibit an excellent balance between quality/features (as outlined above) and cost, making them accessible to future VR consumers across various economic backgrounds.  
On the other hand, it is worth noting that the design of the scenes is very minimalist, with a display stand for positioning the products. We chose this design to avoid overloading the hardware of the headsets by rendering objects in the scene that are not part of the study, as this could introduce noise in the measurements that would not faithfully represent the performance offered by the models. An example of a scene can be seen in Figure 8. We are aware of the limitations of not considering dynamic optimisation techniques in the analysis, but our objective is to see the performance provided by the models that small businesses will provide to the platform in the future, without considering future performance improvements that may be included in the development of the application. Moreover, even though we will not analyse the effects of lightning sources or materialsin the rendering process, we have shown in Section 3.3 the features of the textures of the 3D models used in the study. Next, the process of collecting performance data and the decisions made regarding the objectives of this work will be described.