Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Quality Monocular Depth Estimation via Transfer Learning   Accurate depth estimation from images is a fundamental task in many
applications including scene understanding and reconstruction. Existing
solutions for depth estimation often produce blurry approximations of low
resolution. This paper presents a convolutional neural network for computing a
high-resolution depth map given a single RGB image with the help of transfer
learning. Following a standard encoder-decoder architecture, we leverage
features extracted using high performing pre-trained networks when initializing
our encoder along with augmentation and training strategies that lead to more
accurate results. We show how, even for a very simple decoder, our method is
able to achieve detailed high-resolution depth maps. Our network, with fewer
parameters and training iterations, outperforms state-of-the-art on two
datasets and also produces qualitatively better results that capture object
boundaries more faithfully. Code and corresponding pre-trained weights are made
publicly available.

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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph