PointGAT:用于三维物体检测的图形注意网络

Haoran Zhou;Wei Wang;Gang Liu;Qingguo Zhou
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引用次数: 1

摘要

三维目标检测是许多应用中的关键技术,在各种检测方法中,基于点云的方法是近年来最热门的研究课题。鉴于图神经网络(Graph Neural Network, GNN)被认为是处理点云的有效方法,本文将其与注意力机制相结合,提出了一种三维目标检测方法PointGAT。我们提出的PointGAT在KITTI测试数据集上优于以前的方法。在真实校园场景中的实验也证明了我们的方法具有进一步应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PointGAT: Graph attention networks for 3D object detection
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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