Understanding the implication of point cloud is still challenging in the aim of classification or segmentation for point cloud due to its irregular and sparse structure. As we have known, PointNet architecture as a ground-breaking work for point cloud process can learn shape features directly on unordered 3D point cloud and has achieved favorable performance, such as 86% mean accuracy and 89.2% overall accuracy for classification task, respectively. However, this model fails to consider the fine-grained semantic information of local structure for point cloud. Then, a multiscale receptive fields graph attention network (named after MRFGAT) by means of semantic features of local patch for point cloud is proposed in this paper, and the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet datasets, and results show it achieves state-of-the-art performance in shape classification tasks, such as it outperforms GAPNet (Chen et al.) model by 0.1% in terms of OA and compete with DGCNN (Wang et al.) model in terms of MA.

中文翻译：

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点云分类的多尺度接收域图注意力网络

由于点云的不规则和稀疏结构，因此在分类或分割点云的目的中，了解点云的含义仍然具有挑战性。众所周知，PointNet体系结构作为点云过程的开创性工作，可以直接在无序3D点云上学习形状特征，并获得了良好的性能，例如分类任务的平均精度分别为86％和89.2％。但是，该模型无法考虑点云局部结构的细粒度语义信息。然后，本文提出了一种利用点云局部补丁的语义特征的多尺度感受野图注意力网络（以MRFGAT命名），所学习的网络特征图可以很好地捕获点云的丰富特征信息。