  This paper proposes a 4D backbone for long-term point cloud video
understanding. A typical way to capture spatial-temporal context is using
4Dconv or transformer without hierarchy. However, those methods are neither
effective nor efficient enough due to camera motion, scene changes, sampling
patterns, and the complexity of 4D data. To address those issues, we leverage
the primitive plane as a mid-level representation to capture the long-term
spatial-temporal context in 4D point cloud videos and propose a novel
hierarchical backbone named Point Primitive Transformer(PPTr), which is mainly
composed of intra-primitive point transformers and primitive transformers.
Extensive experiments show that PPTr outperforms the previous state of the arts
on different tasks.
