Robust depth map inpainting using superpixels and non-local Gauss-Markov random field prior

Depth maps contain 3D geometrical information of a scene which can be useful in many applications. In this work we address the task of inpainting a depth map afflicted by regions with missing data. Initially, we inpaint depth values missing at random locations as well as due to overlaid text. Subsequently, we propose an approach for filling large holes in the input depth map wherein superpixel division of the corresponding RGB image is also exploited. We use a non-local extension of the classical Gauss-Markov random field model for the completed depth map, so that missing information in the degraded observation can be estimated depending upon self-similarities between non-local patches inside a superpixel based search window. Several experiments performed with disparity maps and real world depth data exhibit the efficacy of the proposed algorithm.