基于特征通道和空间位置注意力的三维点云特征学习网络

点云模型的分类与部件分割是三维点云数据处理的基本任务，其核心在于获取可以有效表示三维模型的点云特征。提出一个引入注意力机制的三维点云特征学习网络。该网络采用多层次点云特征提取方法，首先使用特征通道注意力模块获取各通道间的关联，增强关键通道信息; 接着引入空间位置注意力机制，基于点的空间位置信息获取各点的注意力权重；然后结合以上2种注意力机制获取增强的点云特征;最后基于该特征继续进行多层次特征提取，获得面向下游任务的点云特征。分别在ModelNet40和ShapeNet数据集上进行形状分类与部件分割实验，结果表明，使用所提方法可以实现高精度、具有鲁棒性的三维点云形状分类与分割。

A 3D point cloud feature learning network based onfeature channel and spatial position attentions

The classification and part segmentation of point cloud models are the basic tasks of 3D point cloud data processing, and the core is to obtain point cloud features that can effectively represent 3D models. This paper proposes a 3D point cloud feature learning network that introduces attention mechanisms. The network adopts a hierarchical point cloud feature extraction method. In the process of hierarchical feature extraction, the feature channel attention mechanism is adopted to obtain the correlation among channels, and the key channel information is enhanced. The spatial position attention mechanism is adopted to obtain the attention weight of each point based on the spatial information of the points. The enhanced point cloud feature is obtained by combining two or more attention mechanisms. Based on this feature, multi-level feature extraction is performed to obtain the final point cloud features for downstream tasks. Shape classification and part segmentation experiments are performed on ModelNet40 and ShapeNet datasets, respectively. The experimental results show that the proposed method can achieve high-precision and robust 3D point cloud shape classification and segmentation.