论文标题
多尺度的接收场图云分类的图形注意网络
Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification
论文作者
论文摘要
了解点云的含义仍然具有挑战性，即由于点云的不规则和稀疏结构而实现分类或分割的目标。众所周知，PointNet架构是一项针对点云的开创性工作，可以在无序的3D点云上直接学习有效地塑造功能，并取得了有利的性能。但是，该模型未能考虑点云的局部结构的细粒语义信息。之后，提出了许多有价值的作品，以通过对点云的本地贴片的语义特征来增强PointNet的性能。在本文中，提出了用于点云分类的多尺度接收场图形注意力网络（以MRFGAT命名）。通过专注于点云的局部优质功能并根据通道亲和力应用多个注意模块，我们网络的学习功能映射可以很好地捕获点云的丰富特征信息。提出的MRFGAT体系结构在ModelNet10和ModelNet40数据集上进行了测试，结果表明它在形状分类任务中实现了最新的性能。
Understanding the implication of point cloud is still challenging to achieve the goal of classification or segmentation due to the irregular and sparse structure of point cloud. As we have known, PointNet architecture as a ground-breaking work for point cloud which can learn efficiently shape features directly on unordered 3D point cloud and have achieved favorable performance. However, this model fail to consider the fine-grained semantic information of local structure for point cloud. Afterwards, many valuable works are proposed to enhance the performance of PointNet by means of semantic features of local patch for point cloud. In this paper, a multi-scale receptive fields graph attention network (named after MRFGAT) for point cloud classification is proposed. By focusing on the local fine features of point cloud and applying multi attention modules based on channel affinity, the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40 datasets, and results show it achieves state-of-the-art performance in shape classification tasks.
