  How to learn long-range dependencies from 3D point clouds is a challenging
problem in 3D point cloud analysis. Addressing this problem, we propose a
global attention network for point cloud semantic segmentation, named as
GA-Net, consisting of a point-independent global attention module and a
point-dependent global attention module for obtaining contextual information of
3D point clouds in this paper. The point-independent global attention module
simply shares a global attention map for all 3D points. In the point-dependent
global attention module, for each point, a novel random cross attention block
using only two randomly sampled subsets is exploited to learn the contextual
information of all the points. Additionally, we design a novel point-adaptive
aggregation block to replace linear skip connection for aggregating more
discriminate features. Extensive experimental results on three 3D public
datasets demonstrate that our method outperforms state-of-the-art methods in
most cases.
