## 3 Low-Cost Digitisation for VR E-Commerce
### 3D Model Quality Evaluation
It is crucial to remember that we are focusing on technologies that can be easily used by individuals without advanced technical knowledge, such as small business owners, who may not have access to expensive hardware and software resources. Therefore, it is important to find solutions that offer a good balance between quality and accessibility. Polycam stands out in this regard due to its user-friendly interface and extensive usage in the literature. It supports both photogrammetry and LiDAR scanning technologies, which are crucial for our comparative analysis. Polycam's average NR-3 DQA score for optimised models was 0.8111, indicating good quality while maintaining accessibility for non-experts. Conversely, our results indicate that models obtained using LiDAR sensors are less suitable for 3D object scanning in the context of product digitisation. The lower visual quality and higher error rates observed with LiDAR make it less ideal for creating detailed product models. For example, the CMDM values for LiDAR models reached up to 0.318699, significantly higher than those of photogrammetry models. However, LiDAR can be more \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Object** & **Type** & **Types Compared** & **Scanning Application** & **RMS** & **CMDM** \\ \hline \multirow{2}{*}{**Munich shoes**} & MP & HP, MP & LUMA & 0.00041 & **0.132868** \\  & LP & HP, LP & LUMA & 0.00171 & 0.190245 \\  & OP & Polycam & Polycam & 0.01269 & 0.152718 \\  & OP & HP, PolyXiaomi & Polycam & 0.03012 & 0.223199 \\  & OP & HP, PolyiPad & Polycam & 0.03767 & 0.224947 \\ \hline \multirow{2}{*}{**Octopus teddy**} & MP & HP, MP & LUMA & 0.00023 & **0.151651** \\  & LP & HP, LP & LUMA & 0.00098 & 0.17674 \\  & OP & Polycam & Polycam & 0.03360 & 0.204733 \\  & OP & HP, PolyXiaomi & Polycam & 0.00522 & 0.222374 \\  & OP & HP, PolyiPad & Polycam & 0.05976 & 0.318699 \\ \hline \multirow{2}{*}{**Burner**} & MP & HP, MP & LUMA & 0.00009 & **0.0480725** \\  & LP & HP, LP & LUMA & 0.00004 & 0.0637373 \\  & OP & Polycam & Polycam & 0.01451 & 0.12820 \\  & OP & HP, PolyiXiaomi & Polycam & 0.01568 & 0.121982 \\  & OP & HP, PolyiPad & Polycam & 0.01423 & 0.151532 \\ \hline \multirow{2}{*}{**Ceramic mushroom**} & MP & HP, MP & LUMA & 0.00018 & 0.239902 \\  & LP & HP, LP & LUMA & 0.00072 & 0.290423 \\  & OP & Polycam & Polycam & 0.04873 & **0.181069** \\  & OP & HP, PolyiXiaomi & Polycam & 0.03710 & 0.34902 \\  & OP & HP, PolyiPad & Polycam & 0.02446 & 0.350014 \\ \hline \multirow{2}{*}{**Trophy**} & MP & HP, MP & LUMA & 0.00027 & **0.0902698** \\  & LP & HP, LP & LUMA & 0.00141 & 0.1052886 \\  & OP & Polycam & Polycam & 0.08235 & 0.2399709 \\  & OP & HP, PolyiXiaomi & Polycam & 0.03008 & 0.29231 \\  & OP & HP, PolyiPad & Polycam & 0.23793 & 0.390321 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 3D mesh comparisons performed with CloudCompare and CMDM. Regarding the labelling of types compared: HP, LP, MP and OP stands for the same as the previous table, Poly\(<\)device\(>\) = Polycam using such \(<\)device\(>\), Polycam = both models obtained from both devices are compared. appropriate for scanning larger environments, such as entire stores or shopping spaces, where the focus is on capturing the overall layout rather than fine details. Further limitations of these scanning technologies were detected. Products made with transparent material or specular surfaces require a specific scanning environment where light sources do not produce poor results. Moreover, objects with very small dimensions are quite difficult to scan with the technologies proposed. Instead, it would require a professional camera where the optical zoom allows for quality photos, as well as the use of photogrammetry software to transfer the photos to. In Section 4, we will analyze the performance of these models on various current market VR headsets, including the Meta Quest 2, Quest Pro and Quest 3.