Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Scattering Tomography by Deep Learning with Architecture Tailored to
  Cloud Fields   We present 3DeepCT, a deep neural network for computed tomography, which
performs 3D reconstruction of scattering volumes from multi-view images. Our
architecture is dictated by the stationary nature of atmospheric cloud fields.
The task of volumetric scattering tomography aims at recovering a volume from
its 2D projections. This problem has been studied extensively, leading, to
diverse inverse methods based on signal processing and physics models. However,
such techniques are typically iterative, exhibiting high computational load and
long convergence time. We show that 3DeepCT outperforms physics-based inverse
scattering methods in term of accuracy as well as offering a significant orders
of magnitude improvement in computational time. To further improve the recovery
accuracy, we introduce a hybrid model that combines 3DeepCT and physics-based
method. The resultant hybrid technique enjoys fast inference time and improved
recovery performance.

18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat