Optimal depth recovery using image guided TGV with depth confidence for high-quality view synthesis

•A confidence-based depth recovery and high quality 3D view synthesis are proposed.•The depth recovery relies on image edges and high depth confidence pixels.•Texture directions of background are effective in hole filling for view synthesis.

This paper presents a new depth image recovery method for RGB-D sensors giving a complete, sharp, and accurate object shape from a noisy boundary depth map. The proposed method uses the image guided Total Generalized Variation (TGV) with the depth confidence. A new directional hole filling method of view synthesis is also investigated to produce natural texture in hole regions whereas reducing blurring effect and preventing distortion. Thus, a high-quality image view can be achieved. Experimental results show that the proposed method yields higher quality recovered depth maps and synthesized image views than other previous methods.