  Recently, the advancement of 3D point clouds in deep learning has attracted
intensive research in different application domains such as computer vision and
robotic tasks. However, creating feature representation of robust,
discriminative from unordered and irregular point clouds is challenging. In
this paper, our ultimate goal is to provide a comprehensive overview of the
point clouds feature representation which uses attention models. More than 75+
key contributions in the recent three years are summarized in this survey,
including the 3D objective detection, 3D semantic segmentation, 3D pose
estimation, point clouds completion etc. We provide a detailed characterization
(1) the role of attention mechanisms, (2) the usability of attention models
into different tasks, (3) the development trend of key technology.
