  Recognizing human actions from point cloud videos has attracted tremendous
attention from both academia and industry due to its wide applications like
automatic driving, robotics, and so on. However, current methods for point
cloud action recognition usually require a huge amount of data with manual
annotations and a complex backbone network with high computation costs, which
makes it impractical for real-world applications. Therefore, this paper
considers the task of semi-supervised point cloud action recognition. We
propose a Masked Pseudo-Labeling autoEncoder (\textbf{MAPLE}) framework to
learn effective representations with much fewer annotations for point cloud
action recognition. In particular, we design a novel and efficient
\textbf{De}coupled \textbf{s}patial-\textbf{t}emporal Trans\textbf{Former}
(\textbf{DestFormer}) as the backbone of MAPLE. In DestFormer, the spatial and
temporal dimensions of the 4D point cloud videos are decoupled to achieve
efficient self-attention for learning both long-term and short-term features.
Moreover, to learn discriminative features from fewer annotations, we design a
masked pseudo-labeling autoencoder structure to guide the DestFormer to
reconstruct features of masked frames from the available frames. More
importantly, for unlabeled data, we exploit the pseudo-labels from the
classification head as the supervision signal for the reconstruction of
features from the masked frames. Finally, comprehensive experiments demonstrate
that MAPLE achieves superior results on three public benchmarks and outperforms
the state-of-the-art method by 8.08\% accuracy on the MSR-Action3D dataset.
