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Recent work on 4D point cloud sequences has attracted a lot of attention.However, obtaining exhaustively labeled 4D datasets is often very expensive andlaborious, so it is especially important to investigate how to utilize rawunlabeled data. However, most existing self-supervised point cloudrepresentation learning methods only consider geometry from a static snapshotomitting the fact that sequential observations of dynamic scenes could revealmore comprehensive geometric details. And the video representation learningframeworks mostly model motion as image space flows, let alone being3D-geometric-aware. To overcome such issues, this paper proposes a new 4Dself-supervised pre-training method called Complete-to-Partial 4D Distillation.Our key idea is to formulate 4D self-supervised representation learning as ateacher-student knowledge distillation framework and let the student learnuseful 4D representations with the guidance of the teacher. Experiments showthat this approach significantly outperforms previous pre-training approacheson a wide range of 4D point cloud sequence understanding tasks including indoorand outdoor scenarios.

Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning