Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution   Limited by the cost and technology, the resolution of depth map collected by
depth camera is often lower than that of its associated RGB camera. Although
there have been many researches on RGB image super-resolution (SR), a major
problem with depth map super-resolution is that there will be obvious jagged
edges and excessive loss of details. To tackle these difficulties, in this
work, we propose a multi-scale progressive fusion network for depth map SR,
which possess an asymptotic structure to integrate hierarchical features in
different domains. Given a low-resolution (LR) depth map and its associated
high-resolution (HR) color image, We utilize two different branches to achieve
multi-scale feature learning. Next, we propose a step-wise fusion strategy to
restore the HR depth map. Finally, a multi-dimensional loss is introduced to
constrain clear boundaries and details. Extensive experiments show that our
proposed method produces improved results against state-of-the-art methods both
qualitatively and quantitatively.

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The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
3
The categories of this paper are: physics