@conference {cRuiz-Hidalgo19,
	title = {Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification},
	booktitle = {IEEE Conference on Computer Vision Workshop (ICCVW)},
	year = {2019},
	month = {11/2019},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Seoul, Korea},
	abstract = {<p>Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGBD 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.\&nbsp;</p>},
	doi = {10.1109/ICCVW.2019.00507},
	url = {https://imatge-upc.github.io/ragc/},
	author = {Mosella-Montoro, Albert and Ruiz-Hidalgo, J.}
}
