Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep
  Convolutional Neural Networks   As 3D movie viewing becomes mainstream and Virtual Reality (VR) market
emerges, the demand for 3D contents is growing rapidly. Producing 3D videos,
however, remains challenging. In this paper we propose to use deep neural
networks for automatically converting 2D videos and images to stereoscopic 3D
format. In contrast to previous automatic 2D-to-3D conversion algorithms, which
have separate stages and need ground truth depth map as supervision, our
approach is trained end-to-end directly on stereo pairs extracted from 3D
movies. This novel training scheme makes it possible to exploit orders of
magnitude more data and significantly increases performance. Indeed, Deep3D
outperforms baselines in both quantitative and human subject evaluations.

21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th