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Open Access

PointGAT: Graph attention networks for 3D object detection

School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

† Haoran Zhou and Wei Wang contribute equally to this work.

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Abstract

3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective in dealing with pointclouds, in this work, we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT. Our proposed PointGAT outperforms previous approaches on the KITTI test dataset. Experiments in real campus scenarios also demonstrate the potential of our method for further applications.

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Intelligent and Converged Networks
Pages 204-216

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Cite this article:
Zhou H, Wang W, Liu G, et al. PointGAT: Graph attention networks for 3D object detection. Intelligent and Converged Networks, 2022, 3(2): 204-216. https://doi.org/10.23919/ICN.2022.0014

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Received: 15 June 2022
Revised: 06 July 2022
Accepted: 01 August 2022
Published: 06 September 2022
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/