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.

使用 TTPOINT 轻量且通用的点云网络，在运动识别任务中相比传统基于帧的方法只使用了 1.5% 的计算资源，并通过层次结构抽象本地和全局几何特征。同时，通过使用张量列分解方法，即使在对参数进行 55% 的压缩时，也能保持了 TTPOINT 的高准确性。