  Although the application of Transformers in 3D point cloud processing has
achieved significant progress and success, it is still challenging for existing
3D Transformer methods to efficiently and accurately learn both valuable global
features and valuable local features for improved applications. This paper
presents a novel point cloud representational learning network, called 3D Dual
Self-attention Global Local (GLocal) Transformer Network (3DGTN), for improved
feature learning in both classification and segmentation tasks, with the
following key contributions. First, a GLocal Feature Learning (GFL) block with
the dual self-attention mechanism (i.e., a novel Point-Patch Self-Attention,
called PPSA, and a channel-wise self-attention) is designed to efficiently
learn the GLocal context information. Second, the GFL block is integrated with
a multi-scale Graph Convolution-based Local Feature Aggregation (LFA) block,
leading to a Global-Local (GLocal) information extraction module that can
efficiently capture critical information. Third, a series of GLocal modules are
used to construct a new hierarchical encoder-decoder structure to enable the
learning of "GLocal" information in different scales in a hierarchical manner.
The proposed framework is evaluated on both classification and segmentation
datasets, demonstrating that the proposed method is capable of outperforming
many state-of-the-art methods on both classification and segmentation tasks.
