Different from traditional video cameras, event cameras capture asynchronous
events stream in which each event encodes pixel location, trigger time, and the
polarity of the brightness changes. In this paper, we introduce a novel
graph-based framework for event cameras, namely SlideGCN. Unlike some recent
graph-based methods that use groups of events as input, our approach can
efficiently process data event-by-event, unlock the low latency nature of
events data while still maintaining the graph's structure internally. For fast
graph construction, we develop a radius search algorithm, which better exploits
the partial regular structure of event cloud against k-d tree based generic
methods. Experiments show that our method reduces the computational complexity
up to 100 times with respect to current graph-based methods while keeping
state-of-the-art performance on object recognition. Moreover, we verify the
superiority of event-wise processing with our method. When the state becomes
stable, we can give a prediction with high confidence, thus making an early
recognition. Project page: https://zju3dv.github.io/slide_gcn/.

本研究介绍了一种基于图的事件相机新框架 SlideGCN，通过事件逐个处理并保持图的内部结构，从而高效处理事件数据并保持低延迟特性。在图构建方面，采用半径搜索算法来更好地利用事件云的部分规则结构。实验结果表明，相较于当前基于图的方法，我们的方法在保持最先进的目标识别性能的同时，将计算复杂度降低了 100 倍。此外，通过事件级处理，我们验证了该方法的优越性，当状态稳定时，能够以高置信度进行早期识别。