Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Residual Attention Graph Convolutional Network for Geometric 3D Scene
  Classification   Geometric 3D scene classification is a very challenging task. Current
methodologies extract the geometric information using only a depth channel
provided by an RGB-D sensor. These kinds of methodologies introduce possible
errors due to missing local geometric context in the depth channel. This work
proposes a novel Residual Attention Graph Convolutional Network that exploits
the intrinsic geometric context inside a 3D space without using any kind of
point features, allowing the use of organized or unorganized 3D data.
Experiments are done in NYU Depth v1 and SUN-RGBD datasets to study the
different configurations and to demonstrate the effectiveness of the proposed
method. Experimental results show that the proposed method outperforms current
state-of-the-art in geometric 3D scene classification tasks.

11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an