3D point clouds can offer rich geometric content
of information for the scenes. How to learn geometric features
locally and globally has become a growing concern recently.
This paper proposes a novel approach for Point cloud Analysis
based on Transformer network, called PAT, which utilizes
interactive-attention to capture multi-angle features in irregular
point clouds for classification and segmentation. Specifically, the
double-coordinate encoder is designed as local filter operations
by mining neighborhood information from point coordinates
and direction simultaneously in the bi-coordinate system. The
proposed PAT for point cloud analysis can interactively perform
local and global attention across different feature spaces.
Validation was conducted on two public datasets and results
show that the proposed PAT achieves good performance.