Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification

Albert Mosella-Montoro, Javier Ruiz-Hidalgo; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


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.

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[bibtex]
@InProceedings{Mosella-Montoro_2019_ICCV,
author = {Mosella-Montoro, Albert and Ruiz-Hidalgo, Javier},
title = {Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}