## 3 Low-Cost Digitisation for VR E-Commerce
### 3D Model Quality Evaluation
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Object**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Scanning**} & \multirow{2}{*}{**Device Used**} & \multirow{2}{*}{**Size (MB)**} & \multicolumn{1}{c}{**Polygon**} & \multicolumn{1}{c}{**Texture’s**} & \multicolumn{1}{c}{**NR-**} & \multicolumn{1}{c}{**MM-**} \\  & & **Application** & & & **Count** & **Size (MB)** & **3 DQA** & **PCQA** \\ \hline
**Munich shoes** & HP & LUMA AI & iPad Pro & 98.94 & 999,846 & 18.50 & 0.7272 & **86.06** \\  & MP & LUMA AI & iPad Pro & 13.67 & 149,915 & 13.10 & 0.8101 & 83.52 \\  & LP & LUMA AI & iPad Pro & 2.16 & 24,893 & 10.20 & 0.7617 & 84.85 \\  & OP & Polycam & iPad Pro & 4.04 & 39,627 & 0.42 & 0.7363 & 80.67 \\  & OP & Polycam & Xiaomi Note 8 & 1.94 & 19,424 & 2.30 & **0.8111** & 82.18 \\ \hline
**Octopus teddy** & HP & LUMA AI & iPad Pro & 99.47 & 999,869 & 8.60 & 0.6619 & **78.51** \\  & MP & LUMA AI & iPad Pro & 13.50 & 149,916 & 5.19 & **0.6914** & 70.92 \\  & LP & LUMA AI & iPad Pro & 2.10 & 24,920 & 3.67 & 0.6537 & 66.25 \\  & OP & Polycam & iPad Pro & 0.25 & 2689 & 0.45 & 0.4288 & 20.51 \\  & OP & Polycam & Xiaomi Note 8 & 1.84 & 18,623 & 1.99 & 0.5539 & 65.98 \\ \hline
**Burner** & HP & LUMA AI & iPad Pro & 53.54 & 528,442 & 4.64 & 0.6190 & 70.43 \\  & MP & LUMA AI & iPad Pro & 14.30 & 149,972 & 3.71 & **0.6622** & 69.73 \\  & LP & LUMA AI & iPad Pro & 2.30 & 24,981 & 2.77 & 0.6487 & **70.63** \\  & OP & Polycam & iPad Pro & 0.64 & 6693 & 0.29 & 0.6381 & 67.50 \\  & OP & Polycam & Xiaomi Note 8 & 1.93 & 19,678 & 1.54 & 0.6238 & 69.97 \\ \hline
**Ceramic mushroom** & HP & LUMA AI & iPad Pro & 98.94 & 999,731 & 2.97 & 0.7065 & **78.59** \\  & MP & LUMA AI & iPad Pro & 13.67 & 149,875 & 1.16 & **0.7948** & 61.10 \\  & LP & LUMA AI & iPad Pro & 2.16 & 24,814 & 0.87 & 0.7226 & 72.60 \\  & OP & Polycam & iPad Pro & 0.96 & 10,161 & 0.30 & 0.6748 & 42.66 \\  & OP & Polycam & Xiaomi Note 8 & 1.05 & 10,917 & 2.26 & 0.6542 & 63.071 \\ \hline
**Trophy** & HP & LUMA AI & iPad Pro & 39.2 & 329,208 & 6.43 & 0.7606 & **76.17** \\  & MP & LUMA AI & iPad Pro & 13.9 & 149,923 & 4.93 & **0.8001** & 73.03 \\  & LP & LUMA AI & iPad Pro & 2.2 & 24,964 & 3.97 & 0.7974 & 69.61 \\  & OP & Polycam & iPad Pro & 1.49 & 13,416 & 1.83 & 0.4474 & 69.85 \\  & OP & Polycam & Xiaomi Note 8 & 3.39 & 36,105 & 12.2 & 0.4460 & 72.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data extracted of defined features from each 3D model. HP = High Poly, MP = Medium Poly, LP = Low Poly, OP = Optimised. the need to apply more than one material to the model when rendered by a graphics engine, which may worsen performance. Regarding the quality metrics NR-3 DQA and MM-PCQA, a low result does not necessarily indicate low visual quality. Because the neural networks are regression-based, they are trained with the features extracted from the corresponding models and the MOS obtained from the volunteer study present in the WPC database [33]. Since not only original models are used, but also distortions based on them, it is also necessary to take into account the results obtained considering the group of models of the same object. Analysing the results, we observed discrepancies between the metrics, due to the characteristics of the models used in their algorithms. In the case of NR-3 DQA, we noted that the models from Luma performing best were the medium-poly ones, except for the sport shoes, for which by a few decimals it was the Polycam model in its Android version. It can also be seen that, apart from the sport shoes, Luma's models outperform Polycam's in both versions. When we calculate the percentage difference between the top-performing models and those with inferior results, we find the following. The greatest difference is seen in the trophy, where Polycam's models are outperformed by 45% compared to Luma's medium-poly model, followed by the octopus (39% on the iOS version and 20% on the Android version) and the ceramic mushroom (Polycam's Android model) with a 17% difference.