  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: \url{https://zju3dv.github.io/slide_gcn/}.
