## 1 Introduction  
Digital twins have undergone rapid development. The concept originated in 2003 from the work of Professor [PERSON] for use in a variety of fields [1], such as smart cities, smart homes, smart farms, hospitals, aerospace, electric power, intelligent construction, and manufacturing [2,3]. Some of the ideas used in the field of digital twins come from the industrial Internet of Things (IoT), which is the discipline of devising highly capable simulation models, particularly ones that consume data from streams for enhancing performance [4]. In the past few decades, the 3D modeling of real-world objects has been a difficult task, with several applications in computer graphics, gaming, virtual reality (VR), augmented reality (AR), and e-commerce [5,6]. Traditionally, 3D models were made manually using time-consuming and costly procedures, such as 3D scanning or CAD modeling [7]. In general, point cloud data are used to generate the CAD models. Modeling software is used to create a CAD model of the apparel structure from the point cloud [8]; online public repositories are also used to obtain models of real-world apparel items. It should be emphasized that the automatic transformation of 3D scans into CAD is still a work in progress [9]. The surfaces of 3D point clouds, 3D vision, robotics, and graphics all rely on 3D modeling. It bridges the gap between raw point clouds acquired by 3D sensors and editable surfaces for a variety of downstream applications [10]. As a workaround, state-of-the-arttechniques use Uns (UDFs) [11; 12; 13] as a more general representation to reconstruct surfaces from point clouds. However, due to the non-continuous nature of point clouds, these approaches cannot develop UDFs with smooth distance fields near surfaces, even when employing ground truth distance values or large-scale meshes during training. Most Unsigned Distance Functions (UDFs) techniques fail to extract surfaces directly from unsigned distance variables. Deep-learning technology has been a very useful method for solving problems, and deep-learning approaches have shown considerable success in producing accurate 3D models from images. These technologies have the potential to alter the way 3D models are generated and used since they can automatically infer 3D structures from 2D photos. Convolutional neural networks (CNNs) [14] have been used successfully for image identification, object recognition, audio recognition, and classification challenges [15]. CNNs are usually made up of three layers: convolution, pooling, and fully connected layers. CNNs used in conjunction with multi-view stereo (MVS) techniques [16] represent one of the most promising approaches for producing 3D models from images. CNNs can learn to predict depth maps from a single, whereas MVS algorithms can refine depth estimations and construct a complete 3D model from numerous photos. This method produced cutting-edge outcomes in terms of the precision and completeness of the generated 3D models [17]. Even though various studies in 3D object reconstruction have been proposed, especially in the garment industry for virtual trying on, existing studies still lack the feasibility of instant end-to-end transference of 2D image capturing to 3D object reconstructed visualization in the virtual world. To facilitate this, the aim of this study is to accelerate the adaptability of multiple perspectives to the use of mobile devices, for the above-mentioned garment task in particular, which is more accessibly achieved via apparel photographs that use deep-learning techniques to assist in providing details for people's decision making in terms of making purchases through virtual reality glasses using digital twins technology. Hence, the explicit aim of our study is to establish a framework that can receive real-world 2D image data and process this input into 3D objects rendered in the virtual world seamlessly and perfectly.