Stereo for Image-Based Rendering Using Image Over-Segmentation

In this paper, we propose a stereo method specifically designed for image-based rendering. For effective image-based rendering, the interpolated views need only be visually plausible. The implication is that the extracted depths do not need to correct, as long as the recovered views appear to be correct. Our stereo algorithm relies on over-segmenting the source images. Computing match values over entire segments rather than single pixels provides robustness to noise and intensity bias. Color-based segmentation also helps to more precisely delineate object boundaries, which is important for reducing boundary artifacts in synthesized views. The depths of the segments for each image are computed using loopy belief propagation within a Markov Random Field framework. Neighboring MRFs are used for occlusion reasoning and ensuring that neighboring depth maps are consistent. We tested our stereo algorithm on several stereo pairs from the Middlebury data set, and show rendering results based on two of these data sets. We also show results for video-based rendering.