Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stereo Magnification: Learning View Synthesis using Multiplane Images   The view synthesis problem--generating novel views of a scene from known
imagery--has garnered recent attention due in part to compelling applications
in virtual and augmented reality. In this paper, we explore an intriguing
scenario for view synthesis: extrapolating views from imagery captured by
narrow-baseline stereo cameras, including VR cameras and now-widespread
dual-lens camera phones. We call this problem stereo magnification, and propose
a learning framework that leverages a new layered representation that we call
multiplane images (MPIs). Our method also uses a massive new data source for
learning view extrapolation: online videos on YouTube. Using data mined from
such videos, we train a deep network that predicts an MPI from an input stereo
image pair. This inferred MPI can then be used to synthesize a range of novel
views of the scene, including views that extrapolate significantly beyond the
input baseline. We show that our method compares favorably with several recent
view synthesis methods, and demonstrate applications in magnifying
narrow-baseline stereo images.

17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio