  Point cloud has been widely used in the field of autonomous driving since it
can provide a more comprehensive three-dimensional representation of the
environment than 2D images. Point-wise prediction based on point cloud sequence
(PCS) is an essential part of environment understanding, which can assist in
the decision-making and motion-planning of autonomous vehicles. However, PCS
prediction has not been deeply researched in the literature. This paper
proposes a brief review of the sequential point cloud prediction methods,
focusing on interactive scenarios. Firstly, we define the PCS prediction
problem and introduce commonly-used frameworks. Secondly, by reviewing
non-predictive problems, we analyze and summarize the spatio-temporal feature
extraction methods based on PCS. On this basis, we review two types of PCS
prediction tasks, scene flow estimation (SFE) and point cloud location
prediction (PCLP), highlighting their connections and differences. Finally, we
discuss some opening issues and point out some potential research directions.
