  Effective learning of spatial-temporal information within a point cloud
sequence is highly important for many down-stream tasks such as 4D semantic
segmentation and 3D action recognition. In this paper, we propose a novel
framework named Point Spatial-Temporal Transformer (PST2) to learn
spatial-temporal representations from dynamic 3D point cloud sequences. Our
PST2 consists of two major modules: a Spatio-Temporal Self-Attention (STSA)
module and a Resolution Embedding (RE) module. Our STSA module is introduced to
capture the spatial-temporal context information across adjacent frames, while
the RE module is proposed to aggregate features across neighbors to enhance the
resolution of feature maps. We test the effectiveness our PST2 with two
different tasks on point cloud sequences, i.e., 4D semantic segmentation and 3D
action recognition. Extensive experiments on three benchmarks show that our
PST2 outperforms existing methods on all datasets. The effectiveness of our
STSA and RE modules have also been justified with ablation experiments.
