## 3 Methodology
### Fundamental: Deep-Learning in regard to 3D Deep Reconstruction and Cloud Computing Infrastructure  
Deep learning focuses on the development of neural networks and has been used in many areas, such as classification [45], segmentation [46], and object detection [47]. The field of transitional learning is growing exponentially, whether it be in transfer learning, using the virtual world to describe the real world [48], model compression [49], or deep generative modeling [50], which is the focus of this work. In general, for image regeneration, a regular generative model, such as a generative adversarial network (GAN) or a variational autoencoder (VAE), is used to capture the underlying distribution of the training data. Once trained, the model can generate new samples that exhibit similar characteristics to the original dataset. One of the methods that we find interesting is the unsigned distance field (UDF). Basically, UDF, which is represented as voxel grids or 3D textures where each voxel or texel stores the distance value, is a representation commonly used in computer graphics and geometric modeling to describe shapes or objects in three-dimensional space to render new objects. It represents surfaces as voxel grids or 3D textures, where each voxel or texel stores the distance value. Thus, the existing generative model should be capable of learning the underlying distribution of UDFs and generating from random input to yield new UDF samples. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Studies**} & \multirow{2}{*}{**2D Captured-to-Point Cloud**} & **Point Cloud-to-3D** & **3D Visualization over** \\  & & **Reconstructed Object** & **Virtual/Actual World** \\ \hline PIFu & ✓ & - & - \\ Anchor-UDF, CAP-UDF & - & ✓ & - \\
3D-VR ([PERSON] and [PERSON], 2020) [43] & - & - & ✓ \\
3D-AR ([PERSON] et al., 2020) [44] & & & \\ Proposed Method & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparative summary of recent studies and our proposed method. In general, three kinds of infrastructure have been presented via software, platform, and infrastructure as a service (SaaS, PaaS, and IaaS, respectively) to access transparency, scalability, intelligent monitoring, and security, which is significant for on-cloud implementation. Thus, cloud computing is used in our framework to enable scalable and on-demand access to computing resources with its demanding computational requirements by the computation of a deep-learning approach, as shown in Figure 1. The basis is a type of calculation, whose framework involves machine-to-machine collaboration, whether in storage or distributed computing, to drive computation from the bottom up based on client data and display visual results to the client from the top down. The cloud computing infrastructure is presented in Figure 1.  
Reconstruction phase: 2D-to-point cloud Pixel-Aligned Implicit Function (PIFu) and point cloud-to-3D Unsigned Distance Function-based Consistency-Aware Progression (CAP-UDF)  
Initially, the two-dimensional data were captured from an external environment and were shaped as a point cloud through the Pixel-Aligned Implicit Function (PIFu) [51], which pins and anchors the point cloud as a three-dimensional point cloud object. Other existing studies rely on majority pinning and anchoring or use field aids. This causes a reduction in training time and wastes training resources. On the other hand, some studies present an approach to extract surfaces directly by using the gradient field directly to improve model learning and object reconstruction, as shown in Figure 2. CAP-UDF [52] is an end-to-end remarkable model in a one-stage batch run for reconstructing 3D objects where there are two main parts of the learning point for cloud surface regeneration, i.e., UDFs-based cloud rearrangement and consistency-aware gradient fields \(\
abla f\).