Image-guided depth propagation using superpixel matching and adaptive autoregressive model

Semi-automatic 2D-to-3D conversion is preferred for its advantage of handling the trade-off between human participation and 3D conversion effects. In this paper, we propose a novel depth propagation algorithm for semi-automatic 2D-to-3D conversion based on an adaptive image-guided autoregressive (AR) model joint superpixel matching. Firstly, we perform superpixel matching to estimate motion vectors of superpixels. Then, we conduct depth compensation based on motion vectors to obtain the current depth map from the reference depth map. However, the size of each superpixel is not exactly the same, which causes matching errors in the compensated depth map. Thus, we employ an adaptive image-guided AR model to minimize matching errors, and obtain the final depth map by minimizing AR prediction errors. Experimental results demonstrate that the proposed method successfully performs depth propagation and produce high-quality depth maps for 2D-to-3D conversion.