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Example-Based Video Stereolization With Foreground Segmentation and Depth Propagation
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DOI：10.1109/TMM.2014.2341599.png)
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With advances in 3DTV technology, video stereolization has attracted much attention in recent years. Although video stereolization can enrich stereoscopic 3D contents, it is hard to create good depth maps from monocular 2D videos. In this paper, we propose an automatic example-based video stereolization method with foreground segmentation and depth propagation, called EBVS. To consider both performance and computational complexity, we separately estimate depth maps according to the key and non-key frames. In the key frames, we first estimate an initial depth map based on examples from the RGB-D training data set, then refine it to preserve boundaries of foreground objects. In the non-key frames, we propagate the depth map of the key frame using motion compensation, and generate depth maps. Finally, we employ depth-image-based-rendering (DIBR) to generate stereoscopic views from 2D videos and their depth maps. Extensive experiments verify that the proposed EBVS produces visually pleasing and realistic stereoscopic 3D views from 2D videos.
Keyword:
3DTV
depth generation
depth propagation
depth-image-based-rendering
learning-based
stereoscopic views
video stereolization
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