## 2 Related Work  
In the 3D warping process, accurate depth value is important for the quality of the virtual image. Due to the precision limitations of stereo matching algorithm and depth camera, the depth image may be coarse. Therefore, many algorithms preprocess the depth image before DIBR, aiming to smooth the depth value inside the object and reduce the dramatical depth change along the boundary between  
Figure 1: Schematic diagram of 3D warping. foreground and background. [PERSON] et al. [PERSON] et al. (2018) propose a symmetric gaussian filter to smooth the depth image. An asymmetric filter is employed to reduce the holes in Reference [PERSON] et al. (2018). In these methods, the regions that do not cause artifacts are also filtered, resulting in degradation of the virtual image quality and increased computational complexity. [PERSON] et al. [PERSON] et al. (2018) use an edge-dependent gaussian filter to overcome this problem. The geometric distortion of non-hole regions in the virtual view is avoided while horizontal edge is smoothed. In Reference [PERSON] et al. (2018), only the background region in the depth image is smoothed, so the foreground object can be protected. These filters can prevent small holes and cracks, but they are not suitable for large baseline and disocclusions. [PERSON] et al. [PERSON] et al. (2018) use the linear interpolation to modify the depth value of the background pixel, which is adjacent to the foreground object. The decrement of the depth value from the foreground to the background causes the disocclusion to be divided into several small holes, which are easily filled by the image inpainting. This method is suitable for large distance between foreground objects and a simple background. For complex background, the partition of disocclusion may cause distortion, which reduces the quality of virtual view. [PERSON] et al. (2018) propose a structure-aided domain transform smoothing method to process the depth image. This method builds a framework in the transformed domain to reduce computational complexity. However, due to the loss of depth details on the foreground edge, the boundary between the foreground and the background object in the synthesized image may become blurred. Currently, there are two types of methods to solve the large disocclusion problems. One is to use the spatial correlation to fill the disocclusion. [PERSON] et al. [PERSON] et al. (2018) propose a classical exemplar-based inpainting method. The priority of the pixels at the boundary of holes are calculated first, then the best matching patch is searched from the source region and copied to the hole which has greatest priority. For this method, foreground textures can be sampled to fill the disocclusion. This is not appropriate because the disocclusion comes from the background region. In order to solve this problem, some improved inpainting methods based on depth information are proposed. [PERSON] et al. [PERSON] et al. (2018) introduce the depth value into the priority calculation and best matching patch searching. The patch with lower depth variance is given higher priority and filled by the patch in the source region, which has similar depth levels and colors. [PERSON] et al. [PERSON] et al. (2018) replace the data term with the depth term, which prioritizes the greatest depth values. [PERSON] (2018) proposes a depth-based gray-level distance to measure the similarity between the two patches. In addition, [PERSON] et al. [PERSON] et al. (2018) propose a fast Markov random field-based method to formulate the hole filling as energy optimization problems and use loopy belief propagation (LBP) to solve them. The distance transform technique is used to compute the message for LBP so that the computational cost can be reduced. For these methods, if there is an error of the foreground object boundary in depth image, ghost will appear that can affect the computation of priority and cause the spread of wrong texture. A foreground removal approach is proposed in Reference [PERSON] et al. (2018).