Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated
  Disparity Map for Stereo Matching   Disparity prediction from stereo images is essential to computer vision
applications including autonomous driving, 3D model reconstruction, and object
detection. To predict accurate disparity map, we propose a novel deep learning
architecture for detectingthe disparity map from a rectified pair of stereo
images, called MSDC-Net. Our MSDC-Net contains two modules: multi-scale fusion
2D convolution and multi-scale residual 3D convolution modules. The multi-scale
fusion 2D convolution module exploits the potential multi-scale features, which
extracts and fuses the different scale features by Dense-Net. The multi-scale
residual 3D convolution module learns the different scale geometry context from
the cost volume which aggregated by the multi-scale fusion 2D convolution
module. Experimental results on Scene Flow and KITTI datasets demonstrate that
our MSDC-Net significantly outperforms other approaches in the non-occluded
region.

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The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: eess
6
The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ