Recently, graph-based and Transformer-based deep learning networks have demonstrated excellent performances on various point cloud tasks. Most of the existing graph methods are based on static graph, which take a fixed input to establish graph relations. Moreover, many graph methods apply maximization and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points have the same influence on the centroid's feature, which ignoring the correlation and difference between points. Most Transformer-based methods extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the problems of these two types of models, we propose a new feature extraction block named Graph Transformer and construct a 3D point point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer and Global Transformer modules. Local Transformer uses a dynamic graph to calculate all neighboring point weights by intra-domain cross-attention with dynamically updated graph relations, so that every neighboring point could affect the features of centroid with different weights; Global Transformer enlarges the receptive field of Local Transformer by a global self-attention. In addition, to avoid the disappearance of the gradient caused by the increasing depth of network, we conduct residual connection for centroid features in GTNet; we also adopt the features of centroid and neighbors to generate the local geometric descriptors in Local Transformer to strengthen the local information learning capability of the model. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper.

翻译：近期，基于图与Transformer的深度学习网络在各类点云任务中展现出卓越性能。现有图方法大多基于静态图，通过固定输入建立图关系。此外，许多图方法采用最大化与平均化操作聚合邻域特征，导致仅单个邻域点影响中心点特征，或不同邻域点对中心点特征产生相同影响，从而忽视了节点间的相关性与差异性。而多数基于Transformer的方法依赖全局注意力提取点云特征，缺乏对局部邻域的特征学习。为解决这两类模型的缺陷，本文提出名为图Transformer的新型特征提取模块，并构建三维点云学习网络GTNet，用于学习点云在局部与全局模式下的特征。图Transformer融合图方法与Transformer方法的优势，由局部Transformer与全局Transformer两个模块构成。其中，局部Transformer通过动态图机制，基于动态更新的图关系采用域内交叉注意力计算所有邻域点权重，使每个邻域点能以差异化权重影响中心点特征；全局Transformer则通过全局自注意力扩大局部Transformer的感受野。此外，为避免网络深度增加导致的梯度消失问题，我们在GTNet中对中心点特征进行残差连接；同时在局部Transformer中采用中心点与邻域点特征生成局部几何描述符，以增强模型的局部信息学习能力。本文最终将GTNet应用于形状分类、部件分割与语义分割任务。