Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistent Video Depth Estimation   We present an algorithm for reconstructing dense, geometrically consistent
depth for all pixels in a monocular video. We leverage a conventional
structure-from-motion reconstruction to establish geometric constraints on
pixels in the video. Unlike the ad-hoc priors in classical reconstruction, we
use a learning-based prior, i.e., a convolutional neural network trained for
single-image depth estimation. At test time, we fine-tune this network to
satisfy the geometric constraints of a particular input video, while retaining
its ability to synthesize plausible depth details in parts of the video that
are less constrained. We show through quantitative validation that our method
achieves higher accuracy and a higher degree of geometric consistency than
previous monocular reconstruction methods. Visually, our results appear more
stable. Our algorithm is able to handle challenging hand-held captured input
videos with a moderate degree of dynamic motion. The improved quality of the
reconstruction enables several applications, such as scene reconstruction and
advanced video-based visual effects.

14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: eess
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph