Depth Estimation from Single Images Using Modified Stacked Generalization

Despite the rapid growth of 3D displays in the last few years, insufficient supply of 3D contents has led to considerable effort in devising 2D to 3D conversion algorithms. Inferring associated depth from single 2D image is still a controversial issue in these algorithms. In this paper we propose an algorithm, which unlike previous strategies, aggregates both global and local information from a pool of images with known depth maps. Hence, we propose to extract a set of features from the image patches of globally similar images in a large 3D image repository. These features describe powerful monocular depth perception cues. Using these relevant and robust features and using modified stacked generalization learning scheme, our scheme directly extracts an accurate depth map from given images. Experimental results demonstrate that our method has surpassed state-of-the-art algorithms in both quantitative and qualitative analysis.