Light Field Depth Estimation via Epipolar Plane Image Analysis and Locally Linear Embedding

Abstract—In this paper, we propose a novel method for 4D light field depth estimation exploiting the special linear structure of epipolar plane image (EPI) and locally linear embedding (LLE). Without high computational complexity, depth maps are estimated locally by locating the optimal slope of each line segmentation on EPIs, which are projected by corresponding scene points. For each pixel to be processed, we build and then minimize the matching cost that aggregates intensity pixel value, gradient pixel value, spatial consistency as well as reliability measure to select the optimal slope from a predefined set of directions. Next, a sub-angle estimation method is proposed to further refine the obtained optimal slope of each pixel. Furthermore, based on a local reliability measure, all the pixels are classified into reliable and unreliable pixels. For the unreliable pixels, LLE is employed to propagate the missing pixels by the reliable pixels based on the assumption of manifold preserving property maintained by natural images. We demonstrate the effectiveness of our approach on a number of synthetic light field examples and real-world light field datasets, and show that our experimental results can achieve higher performance compared with the typical and recent state-of-the art light field stereo matching methods.