Semi-automatic 2D to 3D image conversion using a hybrid Random Walks and graph cuts based approach

In this paper, we present a semi-automated method for converting conventional 2D images to stereoscopic 3D. User-defined strokes that correspond to a rough estimate of the depth values in the scene are defined for the image of interest. With these strokes, our system thus determines what the depth values are for the rest of the image, producing a depth map that is ultimately used to create a stereoscopic image pair. Our work is based on a similar scheme which employs Random Walks. However, the related work is quite complex, with many processing steps required to produce the final stereoscopic im age pair. Combined with the evident shortcomings of the related work, but noting the merits of Random Walks, we propose a system that is a hybrid between Random Walks, and the popular Graph Cuts segmentation paradigm. Both segmentation algorithms are used to generate a final cohesive depth map, thus combining the merits of both frameworks together. The generated results show that we can produce good quality stereoscopic image pairs, while using a much more simplified method in comparison to the related Random Walks scheme.