A Stereo-Vision-Assisted model for depth map super-resolution

In this paper, we propose a novel Stereo-Vision-Assisted (SVA) model for depth map super-resolution. Given a low-resolution depth map as input, we investigate to enhance its resolution or quality using the registered and potentially highresolution color stereo image pair. First, based on the mutual benefits between raw depth map and features of highresolution color image, we model the relationship with two constraint terms of local and non-local priors which sufficiently explore their complementary nature. Then by considering reliable disparity pixels calculated from stereo matching algorithm, we formulate a stereo disparity regularization term to further reinforce the preservation of fine depth detail. In addition, we employ an efficient iterative algorithm to optimize the objective function. Experimental results demonstrate that our approach can achieve high-quality depth map in terms of both spatial resolution and depth precision.