  Recent work on 4D point cloud sequences has attracted a lot of attention.
However, obtaining exhaustively labeled 4D datasets is often very expensive and
laborious, so it is especially important to investigate how to utilize raw
unlabeled data. However, most existing self-supervised point cloud
representation learning methods only consider geometry from a static snapshot
omitting the fact that sequential observations of dynamic scenes could reveal
more comprehensive geometric details. And the video representation learning
frameworks mostly model motion as image space flows, let alone being
3D-geometric-aware. To overcome such issues, this paper proposes a new 4D
self-supervised pre-training method called Complete-to-Partial 4D Distillation.
Our key idea is to formulate 4D self-supervised representation learning as a
teacher-student knowledge distillation framework and let the student learn
useful 4D representations with the guidance of the teacher. Experiments show
that this approach significantly outperforms previous pre-training approaches
on a wide range of 4D point cloud sequence understanding tasks including indoor
and outdoor scenarios.
