# Generation of Stereo Images Based on a View Synthesis Network
## 2 Related Works  
[PERSON] et al. [8] treated the problem as a multiclass classification, where the classes are all possible disparities. They proposed a new matching network for stereo matching, and it produced accurate results in less than 1 s of GPU (graphics processing unit) computation. [PERSON] et al. [7] proposed a novel deep learning architecture for regressing disparity using a pair of stereo images. They used three-dimensional convolutions with disparity cost volume, representing geometric features, to predict a disparity map. [PERSON] et al. [10] proposed a multiscale deep network to solve the problem, and it consists of two parts. One makes a coarse global prediction, and the other refines this prediction locally. [PERSON] et al. [13] proposed two CNNs that can predict a depth map and camera pose, including position and direction information, from an image. During training, they used a video sequence as an input and the network selected one frame as the target image. [PERSON] et al. [11] used the depth predicted from the left-eye view to compute a disparity map. They warped the right-eye view as a new left-eye view, and the error calculated from the two left-eye views was used to train the network. [PERSON] et al. [12] furthered this method to propose a more careful approach. Instead of only warping the left-eye view as a new right-eye view or vice versa, they did both simultaneously. They first computed the predicted disparity from the left-eye view, and then warped the left- and right-eye views to obtain new right- and left-eye views, respectively. [PERSON] et al. [16] proposed a network that can receive geometric parameters, such as poses, lights and shapes. They used a view of an object as an input and then added geometric parameters, such as pose and light, during the encoding process. After decoding was conducted, a new view with information about these parameters could be produced. [PERSON] et al. [18] added rotation information during the encoding process, and their network could directly generate pixels of the new rotated view. By contrast, [PERSON] et al. [20] proposed an appearance flow network (AFN), that does not generate pixels directly, but produces appearance flow instead. They sampled the original view with the flow to synthesize a new high-quality view. However, the shortcoming of this method is also obvious. Because the result is sampled from the original view, it is influenced by the correlation between these two views. When the new view and the original view share only a small number of pixels (i.e., the rotation angle is too large to make the new view completely different from the original view), the quality of the new view will be low. After GANs [6] were proposed, some studies attempted to use them to solve the aforementioned problem. [PERSON] et al. [17] proposed a transformation-grounded view synthesis network based on AFNs [20] and GANs. They multiplied the result of the AFN with a visibility map, and converted the problem from that of a new view synthesis to that of image completion. To a certain extent, this facilitated the overcoming of the shortcoming of AFNs. [PERSON] et al. [19] used variational inference to generate global appearance details, such as shape and color, of the new view, and then used GANs to complete the details. However, these methods are designed only for view synthesis of objects, and produce low-quality results when the entire scene is the input. The networks are also limited, in that the trained model is applicable to only one type of object. [PERSON] et al. [14] proposed a method with concepts similar to complementing frames. They used a sequence of multiple and consecutive--but not sufficient--photos of different views as inputs, and then used the network to predict new views that do not appear in the sequence; that is, they cascaded the inputs and results into a relatively smooth video. [PERSON] et al. [15] reconstructed a complete three-dimensional scene, containing color, depth, and normal information, from photo sequences.