## 6 Conclusions and Future Work  
In this work, we have selected a series of scanning techniques (photogrammetry, LiDAR sensors and NeRF) to analyse, both in terms of visual quality and performance in current VR headsets on the market, the results of scanning a series of products using software applications on mobile devices (Polycam and Luma AI). This analysis was conducted with the goal of investigating and exploring low-cost 3D scanning tools, enabling small business owners to afford to digitise their product catalogues and include them in any VR shopping platform. In this way, we aim for small businesses with limited technological and financial resources to participate in the evolution of e-commerce in the coming years. At the same time, this would simplify the management of VR shopping platforms on the developer's side, as it would be the small businesses themselves who digitise their products and keep their catalogues updated, without having to depend on graphic designers to model the products. For this reason, it is essential that the results offered by the tools recommended to small merchants provide good visual quality, encouraging VR shopping users to explore immersive shopping experiences. In this first part of the analysis, we first measured the inherent error of the models obtained with the selected applications for the study, as we did not have a professional 3D scanner to provide us with a ground truth model. For this, we modelled six pieces of expanded polystyrene in Blender, with their real measurements, and used them as a reference to compare the 3D models obtained after scanning these pieces with the applications. The results showed that the high-poly models introduced the least error. After this, we evaluated the visual quality of five products with different materials and geometries, using metrics that do not require a reference model such as NR-3 DQA [32] or MM-PCQA [34] and metrics that do require one such as CMDM [35]. The results obtained positioned the models from Luma AI (low poly, medium poly, and high poly) as those with the best visual quality, followed by Polycam using photogrammetry and Polycam using LiDAR sensors. Following this first part, we began the second analysis, focused on the performance provided by the models offering the highest graphic quality (see Section 4), as we must ensure that, despite having good visual quality, they must provide a pleasant user experience when executed on VR headsets in standalone mode. Thus, we built scenes in Unity with the least possible amount of assets, logic, and other engine elements in order to isolate the obtained data as much as possible to the rendering of the Luma AI models in the Meta Quest 2, Pro and 3. According to the perceived fluidity, the number of objects of the corresponding type was multiplied to try to reach a theoretical maximum from which the application did not run fluidly. The data showed that the high-poly models from Luma AI should not be used for standalone VR shopping environments, despite being the most detailed and visually quality-rich. Instead, medium-poly models should be used if the shopping environment will host very few objects, or low-poly models with the possibility that the virtual space can accommodate a much larger number of objects (up to 18 times more for acceptable performance). As we saw in the data shown in Section 3.3, the difference in visual quality of the high-poly models compared to the low-poly ones is approximately 5%, so much better performance is provided at the cost of losing very little visual quality. Finally, we explored the use of optimising high-poly models to convert them into ultra low-poly models, with a lower number of polygons than those offered by the low-poly models from Luma AI. The results, despite confirming that in terms of visual quality they