Light field cameras record not only the spatial information of observed
scenes but also the directions of all incoming light rays. The spatial and
angular information implicitly contain geometrical characteristics such as
multi-view or epipolar geometry, which can be exploited to improve the
performance of depth estimation. An Epipolar Plane Image (EPI), the unique 2D
spatial-angular slice of the light field, contains patterns of oriented lines.
The slope of these lines is associated with the disparity. Benefiting from this
property of EPIs, some representative methods estimate depth maps by analyzing
the disparity of each line in EPIs. However, these methods often extract the
optimal slope of the lines from EPIs while ignoring the relationship between
neighboring pixels, which leads to inaccurate depth map predictions. Based on
the observation that an oriented line and its neighboring pixels in an EPI
share a similar linear structure, we propose an end-to-end fully convolutional
network (FCN) to estimate the depth value of the intersection point on the
horizontal and vertical EPIs. Specifically, we present a new feature-extraction
module, called Oriented Relation Module (ORM), that constructs the relationship
between the line orientations. To facilitate training, we also propose a
refocusing-based data augmentation method to obtain different slopes from EPIs
of the same scene point. Extensive experiments verify the efficacy of learning
relations and show that our approach is competitive to other state-of-the-art
methods. The code and the trained models are available at
this https URL

本文提出了一种基于全卷积网络的深度估计方法，用于通过利用光场相机获取的空间和角度信息中的几何特征，估计场景的深度图，并引入了 “定向关系模块” 帮助建立直线方向之间的关系来提高估计的精度。