  Event cameras have gained popularity in computer vision due to their data
sparsity, high dynamic range, and low latency. As a bio-inspired sensor, event
cameras generate sparse and asynchronous data, which is inherently incompatible
with the traditional frame-based method. Alternatively, the point-based method
can avoid additional modality transformation and naturally adapt to the
sparsity of events. Still, it typically cannot reach a comparable accuracy as
the frame-based method. We propose a lightweight and generalized point cloud
network called TTPOINT which achieves competitive results even compared to the
state-of-the-art (SOTA) frame-based method in action recognition tasks while
only using 1.5 % of the computational resources. The model is adept at
abstracting local and global geometry by hierarchy structure. By leveraging
tensor-train compressed feature extractors, TTPOINT can be designed with
minimal parameters and computational complexity. Additionally, we developed a
straightforward downsampling algorithm to maintain the spatio-temporal feature.
In the experiment, TTPOINT emerged as the SOTA method on three datasets while
also attaining SOTA among point cloud methods on all five datasets. Moreover,
by using the tensor-train decomposition method, the accuracy of the proposed
TTPOINT is almost unaffected while compressing the parameter size by 55 % in
all five datasets.
