  Transformer-based networks have achieved impressive performance in 3D point
cloud understanding. However, most of them concentrate on aggregating local
features, but neglect to directly model global dependencies, which results in a
limited effective receptive field. Besides, how to effectively incorporate
local and global components also remains challenging. To tackle these problems,
we propose Asymmetric Parallel Point Transformer (APPT). Specifically, we
introduce Global Pivot Attention to extract global features and enlarge the
effective receptive field. Moreover, we design the Asymmetric Parallel
structure to effectively integrate local and global information. Combined with
these designs, APPT is able to capture features globally throughout the entire
network while focusing on local-detailed features. Extensive experiments show
that our method outperforms the priors and achieves state-of-the-art on several
benchmarks for 3D point cloud understanding, such as 3D semantic segmentation
on S3DIS, 3D shape classification on ModelNet40, and 3D part segmentation on
ShapeNet.
