  In this paper we address the problem of view synthesis from large baseline
light fields, by turning a sparse set of input views into a Multi-plane Image
(MPI). Because available datasets are scarce, we propose a lightweight network
that does not require extensive training. Unlike latest approaches, our model
does not learn to estimate RGB layers but only encodes the scene geometry
within MPI alpha layers, which comes down to a segmentation task. A Learned
Gradient Descent (LGD) framework is used to cascade the same convolutional
network in a recurrent fashion in order to refine the volumetric representation
obtained. Thanks to its low number of parameters, our model trains successfully
on a small light field video dataset and provides visually appealing results.
It also exhibits convenient generalization properties regarding both the number
of input views, the number of depth planes in the MPI, and the number of
refinement iterations.
