Disparity Based Depth Estimation Using Light Field Camera

Light field cameras have a unique feature of capturing the direction of light rays along with its intensity. This additional information is used to estimate the depth of a 3-D scene. Recently a considerable amount of research has been done in depth estimation using light field data. However, these depth estimation methods heavily rely on iterative optimization techniques and do not fully exploit the inherent structured light field data. In this paper, we present a novel three-step disparity based algorithm to estimate accurate depth maps of a light field image. First, an initial depth map of scene points is estimated using principal views by estimating the disparity of segments in the central image. This initial depth helps in resolving ambiguity in depth propagation of refined depth map in Epi-Polar line Images (EPIs). Second, refined depth is estimated at lines using the disparity vector in EPIs. Finally, refined depth at lines in EPIs is propagated to other locations using the initial depth map. We also provided a synthetic data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms.