  Object detection in three-dimensional (3D) space attracts much interest from
academia and industry since it is an essential task in AI-driven applications
such as robotics, autonomous driving, and augmented reality. As the basic
format of 3D data, the point cloud can provide detailed geometric information
about the objects in the original 3D space. However, due to 3D data's sparsity
and unorderedness, specially designed networks and modules are needed to
process this type of data. Attention mechanism has achieved impressive
performance in diverse computer vision tasks; however, it is unclear how
attention modules would affect the performance of 3D point cloud object
detection and what sort of attention modules could fit with the inherent
properties of 3D data. This work investigates the role of the attention
mechanism in 3D point cloud object detection and provides insights into the
potential of different attention modules. To achieve that, we comprehensively
investigate classical 2D attentions, novel 3D attentions, including the latest
point cloud transformers on SUN RGB-D and ScanNetV2 datasets. Based on the
detailed experiments and analysis, we conclude the effects of different
attention modules. This paper is expected to serve as a reference source for
benefiting attention-embedded 3D point cloud object detection. The code and
trained models are available at:
https://github.com/ShiQiu0419/attentions_in_3D_detection.
