Depth propagation based on depth consistency

Semi-automatic 2D-to-3D conversion (2D-3D) is preferred due to its advantage of handling the trade-off between human participation and 3D conversion effects. In this paper, a novel depth propagation algorithm based on depth consistency is proposed, which can be widely employed in semi-automatic 2D-3D. The depth consistency refers to the principle that two neighboring pixels should have similar depth values if their color values or intensities are similar. Based on this observation, depth estimation is modeled as a constrained optimization problem. Two contributions of this paper are : first, a convenient tool for image 2D-3D is implemented, which can obtain a good depth map with the help of limited user´s scribbles on the input image; second, a novel depth propagation algorithm is presented for estimating the depth maps of the non-key-frames in video 2D-3D. In this algorithm, the depth values of high-confidence matched pixels in the non-key-frame are first assigned with the values of the correspondences in the key-frame and then, depth values of the remaining pixels are estimated by solving a constrained optimization problem which is constructed by exploiting depth consistency. Experimental results show that, compared with the shifted bilateral filtering (SBF) algorithm, the proposed algorithm not only holds a similar performance, but also increases the processing speed by nearly 5 times on average.