# A Systematic Review on Virtual Reality Technology for Ancient Ceramic Restoration
## 3 Results
### Paper Selection
#### 3.1.1 The Visualization of Ceramics after Restoration with VR Technology  
As ceramic restoration and preservation is a branch of archaeology, the original and primary goal is to restore ceramics as authentic as possible. The visualization concepts focus on reasonable, rigorous reality reconstruction with VR technology instead of manual working. The virtual restoration technologies differ according to the damage degree of excavated ceramics while following archaeological and cultural restoration principles, including authenticity, minimal intervention, and usage of reversibility. Accurate data on ancient ceramic characteristics should be captured with appropriate scanning equipment, such as computed tomography/\(\mu\) computed tomography, structured light, laser scanners, motion sensors, and photogrammetry/SfM [14]. [PERSON], [PERSON] et al. believed that a low-cost facility could obtain a satisfying experimental result of repairing symmetrical objects. However, accuracy problems in digitization and printing still existed [30]. [PERSON] et al. found that unscanned bases of relics lead to limited scanning results and acquisition process mistakes during the digitalization of archaeological relics. His research aims to integrate the missing surface of an object and retain the existing geometry and refined details [31]. Figure 3: Tendency of the research topics of each category. Figure 2: Division of articles in each category. However, some valuable ceramic relics collected by museums and private collectors are hardly removable due to security problems and cannot be scanned and restored with general methods. [PERSON], [PERSON] and [PERSON] reconstructed a 3D model based on 2D images of rotating ancient ceramics through a new reconstruction algorithm. This new algorithm solved the problems of previous image processing methods, for instance, 2D image distortion, complex image background, and large varieties in image quality [26]. This method could yield a high-accuracy 3D model for 3D printing without a high-cost 3D scanner; however, the objects under reconstruction are restricted to rotating shape ceramic. Reconstruction algorithms on computer graphics are the primary tool for researchers. The data captured from the 3D scanner had to be adjusted with advanced computer science for virtual anastomosis and reconstruction [14]. [PERSON] et al. developed an efficient algorithm for surface reconstruction of archaeological pottery, but not on obtaining prior knowledge [32]. [PERSON] proposed a 3D digital reconstruction method for ceramic paintings by obtaining ceramic relics' complete color point cloud data, which is suitable for VR applications [33]. After accomplishing the digital scanning and 3D reconstruction of 115 Sihwan ceramic relics in the Ceramic Museum, he argued that the ceramic information preserved through +5 exposure blend methods on average in 6 combined modes contributed to VR application. [PERSON] et al. proposed DeepJoin, an automated approach for repairing fractured shapes for asymmetrical and symmetrical objects, which is an automated and predicted approach for generating high-resolution restoration shapes with ground truth fractures and restoration [34]. DeepJoin generates plausible restoration shapes for ancient Greek pottery from the Cultural Heritage dataset and serves for more complex objects. This code is open access on the GitHub website and contributes to further work on automated restoration. Machine learning algorithms, such as the optimized parameters of the backpropagation (BP) neural network model, could improve ceramics' identification and classification accuracy in different dynasties [35]. Piecing together dismembered fragments of archaeological objects is challenging for virtual and manual restoration. [PERSON] et al. proposed a method of global archaeological fragment pairings based on artificial fish school algorithm technology.