  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.
