Structure-aware Priority Belief Propagation for Depth Estimation

2D to 3D video conversion has become a popular manner to produce 3D contents, in which the key task is to estimate the depth for the images. However, the main limitation of conventional motion-based depth propagation methods is that they are susceptible to inaccurate motion estimation and occlusion. To address this problem, we propose a two-stage structure-aware depth propagation method for semi-automatic 2D-to-3D conversion. In the first stage, an initial depth map is generated by shifted bilateral filtering over the temporally consistent region, which is determined by validating the forward and backward optical flow fields. In the second stage, the depth of the temporally inconsistent region will be estimated by solving a multi-label graph inference problem. In particular, an efficient priority belief propagation algorithm is developed, in which the priority of nodes to propagate messages depends on the structure saliency from tensor voting. Experimental results show that the proposed method outperforms existing depth estimation methods in public benchmark.