  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.
