Inspired by objectness measurement and visual saliency

we propose an object window center-surround depth distribution hypothesis for single-view image 2D-to-3D conversion. Based on this model

a depth estimation method fusing objectness and visual saliency is presented. First

visual saliency detection is performed and it is mapped to the depth. Second

some windows are sampled randomly for objectness measuring. Third

an energy function is used to model relations between depth and objectness

and then it is minimized by an iterative method to improve depth estimation results. Finally

3D video is rendered based on depth. Experimental results show that our method improves depth discontinuity at objects boundary and continuity at other regions greatly.