View synthesis based on Conditional Random Fields and graph cuts

TLDR: A novel method to synthesize intermediate views from two stereo images and disparity maps that is robust to errors in disparity maps and provides an explicit probabilistic model to select the best candidate for each disoccluded pixel efficiently with Conditional Random Fields and graph-cuts is proposed.

Abstract: We propose a novel method to synthesize intermediate views from two stereo images and disparity maps that is robust to errors in disparity maps. The proposed method computes a placement matrix from each disparity map that can be used to correct errors when warping pixels from reference view to virtual view. The second contribution is a new hole filling method that uses depth, edge, and segmentation information to aid the process of filling disoccluded pixels. The proposed method selects pixels from segmented regions that are connected to the disoccluded region as candidates to fill the disoccluded pixels. We also provide an explicit probabilistic model to select the best candidate for each disoccluded pixel efficiently with Conditional Random Fields (CRFs) and graph-cuts.