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.