Abstract
We introduce a novel approach to jointly estimate consistent depth and normal maps from 4D light fields, with
two main contributions. First, we build a cost volume from
focal stack symmetry. However, in contrast to previous approaches, we introduce partial focal stacks in order to be
able to robustly deal with occlusions. This idea already
yields significanly better disparity maps. Second, even recent sublabel-accurate methods for multi-label optimization
recover only a piecewise flat disparity map from the cost
volume, with normals pointing mostly towards the image
plane. This renders normal maps recovered from these approaches unsuitable for potential subsequent applications.
We therefore propose regularization with a novel prior linking depth to normals, and imposing smoothness of the resulting normal field. We then jointly optimize over depth
and normals to achieve estimates for both which surpass
previous work in accuracy on a recent benchmark