Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic 3-D measurement based on fringe-to-fringe transformation using
  deep learning   Fringe projection profilometry (FPP) has become increasingly important in
dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of
the measured object before shape profiling. However, traditional phase
retrieval techniques often require a large number of fringes, which may
generate motion-induced error for dynamic objects. In this paper, a novel phase
retrieval technique based on deep learning is proposed, which uses an
end-to-end deep convolution neural network to transform a single or two fringes
into the phase retrieval required fringes. When the object's surface is located
in a restricted depth, the presented network only requires a single fringe as
the input, which otherwise requires two fringes in an unrestricted depth. The
proposed phase retrieval technique is first theoretically analyzed, and then
numerically and experimentally verified on its applicability for dynamic 3-D
measurement.

13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an