Adaptive 2D to 3D image conversion using a hybrid Graph Cuts and Random Walks approach

In this paper, we propose an adaptive method for 2D to 3D conversion of images using a user-aided process based on Graph Cuts and Random Walks. Given user-defined labeling that correspond to a rough estimate of depth, the system produces a depth map which, combined with a 2D image can be used to synthesize a stereoscopic image pair. The work presented here is an extension of work done previously combining the popular Graph Cuts and Random Walks image segmentation algorithms. Specifically, we have made the previous approach adaptive, as well as improved the quality of the results. This is achieved by feeding information from the Graph Cuts result into the Random Walks process at two different stages, and using edge and spatial information to adapt various weights. The results show that we can produce good quality stereoscopic 3D image pairs using a simple yet adaptive approach.