## 3 Low-Cost Digitisation for VR E-Commerce
### Features and Quality Metrics Used  
We aimed to retrieve characteristics that could help us identify its quality and detail and gain an insight into the performance of a VR application that uses a considerable number of such models. We aimed to select state-of-the-art 3D graphics quality metrics that worked with 3D model representations, either meshes or point clouds. Hence, to select and use such metrics, we required that (a) the process followed by the metric takes as input a 3D mesh or 3D point cloud, easily obtained from the scanned model and (b) the source code of the metric was public in order to be able to use it with our data, for which papers with code ([[https://paperswithcode.com](https://paperswithcode.com)]([https://paperswithcode.com](https://paperswithcode.com)) accessed on 7 July 2024) web page was useful. The features of the 3D models we collected were:  
1. Size in MB of the.obj file. 2. The number of polygons of the model (polygon count). 3. The texture's size of the model in MB. 4. NR-3 DQA 1.0 ([[https://github.com/zzc-1998/NR-3](https://github.com/zzc-1998/NR-3) DQA]([https://github.com/zzc-1998/NR-3](https://github.com/zzc-1998/NR-3) DQA) accessed on 7 July 2024) [32]. This metric, which does not need a reference model, takes as input a point cloud or 3D mesh, extracting colour and geometric features, such as curvature, sphericity or linearity. After estimating four statistical parameters, a vector of 64 features is constructed and fed to a Support Vector Regression (SVR) neural network to provide a quality score in the form of a Mean Opinion Score (MOS) obtained by regression. For training, the public database Waterloo Point Cloud (WPC) [33] is used, which comprises 760 point clouds including 20 original ones and others distorted through different methods. These clouds are evaluated by obtaining an MOS, which represents the visual quality with which the point cloud is perceived by the human visual system. 5. MM-PCQA 1.0 ([[https://github.com/zzc-1998/MM-PCQA](https://github.com/zzc-1998/MM-PCQA)]([https://github.com/zzc-1998/MM-PCQA](https://github.com/zzc-1998/MM-PCQA)) accessed on 7 July 2024) was proposed by [PERSON] et al. [34]. This metric, which does not need a reference model, takes as input a point cloud in.PLY format. The proposed metric works by first splitting the point cloud into sub-models to capture local geometric distortions such as point offsets and down-sampling. It then converts the point clouds into 2D image projections to extract texture features. Both the sub-models and the projected images are encoded using point-based neural networks, such as PointNet++ and DGCNN (Dynamic Graph CNN), and image-based neural networks. The main innovation of this metric is the symmetric cross-modal attention mechanism, which fuses quality-aware multi-modal information to predict the quality score. The fused features are then used to predict the quality score of the point cloud. The loss function used to train the model includes components to minimise the difference between the predicted and actual quality scores and to maintain the quality ranking between the different point clouds. In this case, the WPC database was also used to train the neural networks. 6. CMDM (Color Mesh Distortion Measure) 0.15.1 ([[https://github.com/MEPP-team/MEPP2](https://github.com/MEPP-team/MEPP2)]([https://github.com/MEPP-team/MEPP2](https://github.com/MEPP-team/MEPP2)) accessed on 7 July 2024) [35]. This metric, which uses a reference mesh, evaluates the quality of a 3D mesh with colour, combining geometric and colour features to determine the perception of distortion. It is based on the extraction of features at a local level and their subsequent analysis at a global multi-scale scale. Geometric features include mean curvature and curvature structure comparison, while colour features are extracted by transforming the colours of vertices to the LAB200 HL colour space, considering luminance, chroma and hue.