Event cameras are bio-inspired sensors that respond to local changes in light
intensity and feature low latency, high energy efficiency, and high dynamic
range. Meanwhile, Spiking Neural Networks (SNNs) have gained significant
attention due to their remarkable efficiency and fault tolerance. By
synergistically harnessing the energy efficiency inherent in event cameras and
the spike-based processing capabilities of SNNs, their integration could enable
ultra-low-power application scenarios, such as action recognition tasks.
However, existing approaches often entail converting asynchronous events into
conventional frames, leading to additional data mapping efforts and a loss of
sparsity, contradicting the design concept of SNNs and event cameras. To
address this challenge, we propose SpikePoint, a novel end-to-end point-based
SNN architecture. SpikePoint excels at processing sparse event cloud data,
effectively extracting both global and local features through a singular-stage
structure. Leveraging the surrogate training method, SpikePoint achieves high
accuracy with few parameters and maintains low power consumption, specifically
employing the identity mapping feature extractor on diverse datasets.
SpikePoint achieves state-of-the-art (SOTA) performance on four event-based
action recognition datasets using only 16 timesteps, surpassing other SNN
methods. Moreover, it also achieves SOTA performance across all methods on
three datasets, utilizing approximately 0.3\% of the parameters and 0.5\% of
power consumption employed by artificial neural networks (ANNs). These results
emphasize the significance of Point Cloud and pave the way for many
ultra-low-power event-based data processing applications.

使用 SpikePoint 结构，利用事件相机与脉冲神经网络相结合的高效能力，在超低功耗应用场景中实现了优秀的动作识别性能。