Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D
  sensors   Consumer-level depth cameras and depth sensors embedded in mobile devices
enable numerous applications, such as AR games and face identification.
However, the quality of the captured depth is sometimes insufficient for 3D
reconstruction, tracking and other computer vision tasks. In this paper, we
propose a self-supervised depth denoising approach to denoise and refine depth
coming from a low quality sensor. We record simultaneous RGB-D sequences with
unzynchronized lower- and higher-quality cameras and solve a challenging
problem of aligning sequences both temporally and spatially. We then learn a
deep neural network to denoise the lower-quality depth using the matched
higher-quality data as a source of supervision signal. We experimentally
validate our method against state-of-the-art filtering-based and deep denoising
techniques and show its application for 3D object reconstruction tasks where
our approach leads to more detailed fused surfaces and better tracking.

10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas