Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aerial Height Prediction and Refinement Neural Networks with Semantic
  and Geometric Guidance   Deep learning provides a powerful new approach to many computer vision tasks.
Height prediction from aerial images is one of those tasks that benefited
greatly from the deployment of deep learning which replaced old multi-view
geometry techniques. This letter proposes a two-stage approach, where first a
multi-task neural network is used to predict the height map resulting from a
single RGB aerial input image. We also include a second refinement step, where
a denoising autoencoder is used to produce higher quality height maps.
Experiments on two publicly available datasets show that our method is capable
of producing state-of-the-art results. Code is available at
https://github.com/melhousni/DSMNet.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg