Automatic Depth Prediction from 2D Videos Based on Non-parametric Learning and Bi-directional Depth Propagation

With progress in 3D video technology, 2D to 3D conversion has drawn great attention in recent years. Predicting perceptually reasonable depth information from traditional monocular videos is a challenging task in 2D to 3D conversion. For the purpose of generating convincing depth maps, an efficient depth estimation algorithm utilizing non-parametric learning and bi-directional depth propagation is proposed. First, global depth maps on key frames are generated on the basis of gradient samples and the gradient reconstruction method. Then, foreground objects are extracted and employed to refine global depth maps for producing more local depth details. Next, the depth information of key frames is propagated utilizing bi-directional motion compensation to recover the forward and backward depth information of non-key frames. In the end, a weighting fusion strategy is designed to integrate forward and backward depths for predicting the depth information of each non-key frame. The quality of estimated depth maps is assessed by leveraging objective and subjective quality evaluation criteria. The experimental results verify that the proposed depth prediction framework outperforms some depth estimation methods and is efficient at producing 3D views.