Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning light field synthesis with Multi-Plane Images: scene encoding
  as a recurrent segmentation task   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.

11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg