  Previous studies have demonstrated the effectiveness of point-based neural
models on the point cloud analysis task. However, there remains a crucial issue
on producing the efficient input embedding for raw point coordinates. Moreover,
another issue lies in the limited efficiency of neighboring aggregations, which
is a critical component in the network stem. In this paper, we propose a Global
Attention-guided Dual-domain Feature Learning network (GAD) to address the
above-mentioned issues. We first devise the Contextual Position-enhanced
Transformer (CPT) module, which is armed with an improved global attention
mechanism, to produce a global-aware input embedding that serves as the
guidance to subsequent aggregations. Then, the Dual-domain K-nearest neighbor
Feature Fusion (DKFF) is cascaded to conduct effective feature aggregation
through novel dual-domain feature learning which appreciates both local
geometric relations and long-distance semantic connections. Extensive
experiments on multiple point cloud analysis tasks (e.g., classification, part
segmentation, and scene semantic segmentation) demonstrate the superior
performance of the proposed method and the efficacy of the devised modules.
