Depth propagation with tensor voting for 2D-to-3D video conversion
Recently, 2D-to-3D video conversion has raised great interest because of the increasing demand of 3D video contents. A critical component in semi-automatic methods is to estimate the depth information of non-key frames from key frames. To overcome the sensitiveness to occlusion in existing schemes, a depth propagation method is proposed in this paper where tensor voting is leveraged to eliminate occlusion points, thereby derive accurate sparse depth. Furthermore, high dimensional tensor is constructed with coordinate, motion and color features to represent points in the sparse depth. A dense depth can be generated via tensor-voting based interpolation. It is robust against object occlusion and inaccurate motion estimation because tensor voting can efficiently make denoising and capture the structures. Experiment results demonstrate that the proposed method outperforms state-of-the-art semi-automatic techniques.