Consistency Constrained Reconstruction of Depth Maps from Epipolar Plane Images

Abstract

In this paper, we propose a method of reconstructing the depth map of a set of multiview images from the epipolar plane images (EPIs) of multiview Images. Our method involves two steps: finding support points and estimating depth. First, we propose to include a consistency term and a smoothness term in the objective function for edge point detection, where the consistency term is used to identify edge points and the smoothness term is applied to mitigate false edge detection due to light density variations caused by viewpoint changes. Then, based on the detected edge points, a depth map can be estimated by solving a energy minimization problem, in which a line uniformness term and a matching error term are introduced to ensure the line traces estimated from EPIs for depth estimation match the colors of edge points well. The depths of non-edge points are then estimated by introducing an additional prior term. In order to speed up our algorithm, the depth estimation problem is aggregated by a winner-take-all strategy. Experiments show that our method outperforms the state-of-the-art schemes in reconstructing depth map with fine details.