Depth Estimation From a Light Field Image Pair With a Generative Model

IEEE Access

Rynson W. H. Lau¹  Xiaohua Qian²  Yiming Mao³  Hongbin Yu³  Tao Yan³  Fan Zhang³ 

[1] Department of Computer Science, City University of Hong Kong, Hong Kong;Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;Jiangsu Key Laboratory of Media Design and Software Technology, School of Digital Media, Jiangnan University, Jiangsu, China;

关键词: Light field;    depth estimation;    disparity map;    epipolar plane image;    stereo matching;    generative model;   

DOI  :  10.1109/ACCESS.2019.2893354

来源: DOAJ

【 摘 要 】

In this paper, we propose a novel method to estimate the disparity maps from a light field image pair captured by a pair of light field cameras. Our method integrates two types of critical depth cues, which are separately inferred from the epipolar plane images and binocular stereo vision into a global solution. At the same time, in order to produce highly accurate disparity maps, we adopt a generative model, which can estimate a light field image only with the central subaperture view and corresponding hypothesized disparity map. The objective function of our method is formulated to minimize two energy terms/differences. One is the difference between the two types of previously extracted disparity maps and the target disparity maps, directly optimized in the gray-scale disparity space. The other indicates the difference between the estimated light field images and the input light field images, optimized in the RGB color space. Comprehensive experiments conducted on real and virtual scene light field image pairs demonstrate the effectiveness of our method.

【 授权许可】

Unknown