2D to 3D conversion with motion-type adaptive depth estimation

Cheolkon Jung, Lei Wang, Xiaohua Zhu, Licheng Jiao

Abstract: 2D to 3D conversion is an important task for 3DTV broadcasting services due to the lack of stereoscopic 3D contents. In this paper, we propose 2D to 3D conversion with motion-type adaptive depth estimation. Because the most important depth cue is motion parallax in our method, we first perform motion estimation between sequential video frames. Then, we adopt a motion-type adaptive approach to depth map estimation because videos have different depth structures according to the type of motion. To be specific, depth from motion is exploited to estimate depth maps in the case of global motion while the depth maps are generated based on the depth from template with the local motion-guided refinement in the case of local motion. Finally, we employ depth image-based rendering (DIBR) to generate stereoscopic virtual views from the depth maps. Experimental results demonstrate that the proposed 2D to 3D conversion is very effective in generating accurate depth maps and providing realistic 3D effects.