  In recent years, there have been significant advancements in applying
attention mechanisms to point cloud analysis. However, attention module
variants featured in various research papers often operate under diverse
settings and tasks, incorporating potential training strategies. This
heterogeneity poses challenges in establishing a fair comparison among these
attention module variants. In this paper, we address this issue by rethinking
and exploring attention module design within a consistent base framework and
settings. Both global-based and local-based attention methods are studied, with
a focus on the selection basis and scales of neighbors for local-based
attention. Different combinations of aggregated local features and computation
methods for attention scores are evaluated, ranging from the initial
addition/concatenation-based approach to the widely adopted dot product-based
method and the recently proposed vector attention technique. Various position
encoding methods are also investigated. Our extensive experimental analysis
reveals that there is no universally optimal design across diverse point cloud
tasks. Instead, drawing from best practices, we propose tailored attention
modules for specific tasks, leading to superior performance on point cloud
classification and segmentation benchmarks.
