## 4 Discussion  
Virtual view image acquisition is the key technology in the field of 3D display. Fast and accurate access to high-quality virtual view images is conducive to the promotion of 3D display technology in the fields of smart medicine, autonomous driving, and intelligent machine manufacturing. In the existing technologies, the geometric object information among multi-view images can be used for virtual view image acquisition, but this method is usually limited by the acquisition cost of time and economic factors, including the complex hardware structure of the acquisition system, a large amount of acquisition view images, correction of the acquisition camera and accuracy of the acquisition image. When the monocular image is used for virtual view image acquisition, since the monocular image only contains less 3D object information, the acquired base line of virtual view image is small, the edge of object in the synthesized image is very fuzzy and there are some voids. Therefore, to acquire high-quality virtual view images, it is urgent to solve the above problems.  
In the existing methods of virtual view image acquisition through multi-view images, the geometric object information among multi-view images or the dense camera array are usually used for acquisition, but the images need to be corrected before virtual view image estimation, which increases the implementation cost. At the same time, artifacts and voids appear easily. When a CNN is used to acquire virtual view image, depth information is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ballet** & **RMSE** & **SSIM** & **PSNR** \\ \hline
[7] & 1.238 & 0.7958 & 25.98 \\
[15] & 1.165 & 0.8328 & 27.03 \\
[58] & 0.712 & 0.8942 & 30.85 \\ Our & 0.683 & 0.8937 & 31.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Virtual view image performance on ballet dancers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Breakdancers** & **RMSE** & **SSIM** & **PSNR** \\ \hline
[7] & 1.103 & 0.7326 & 23.98 \\
[15] & 1.085 & 0.7596 & 25.69 \\
[58] & 0.801 & 0.8325 & 30.81 \\ Our & 0.754 & 0.8425 & 30.53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Virtual view image performance on breakdowners.  
mostly needed, but the depth information acquired by the CNN is not accurate enough, which affects the quality of virtual view image. The quality is improved when a GAN is used to acquire a virtual view image. However, the use of a full convolutional network decreases the network's efficiency, which is unfavorable to the promotion of 3D display technology. At the same time, limited by the amount of experimental data, most network models have poor generalization ability. Therefore, a GAN was adopted as the basic network framework of virtual view image estimation in this paper.  
To increase the quality of the virtual view image, we propose a virtual view acquisition technique for complex scenes in monocular images based on layered depth image. The multilayer representation of the LDI is used to group the depth discontinuities at the edge of the occluded area reasonably. The edge inpainting network is combined with the inpainting network of color and depth to fill the information of color and depth in the occluded area to generate the virtual viewpoint image. Then, a GAN is used to optimize the color and depth to further improve the quality of the generated virtual viewpoint image. The experimental results show that compared with other advanced virtual view methods, the proposed method has very good expressiveness and generalization ability.