Nonlocal Random Walks Algorithm for Semi-Automatic 2D-to-3D Image Conversion

Abstract

We propose a nonlocal random walks (NRW) algorithm to generate accurate depth from 2D images based on user interaction. First, a graphical model is proposed where edges are corresponding to links between local and nonlocal neighboring pixels. Local edges are weighted by a pixel dissimilarity measure, and spatial distances are incorporated into calculation of nonlocal weights. Second, user-defined values are mapped to probabilities that marked pixels have the maximum depth value, and the probabilities of unmarked pixels are obtained by NRW algorithm. Finally, the dense depth-map is recovered with the resulting probabilities. Since nonlocal principle is effective in preserving fine structures in images, we can recover sharp depth boundaries. Experiments on three images containing color bleeding areas demonstrate that our method achieves much high-quality results compared with the existing random walks (RW) based methods.