  Event cameras or dynamic vision sensors (DVS) record asynchronous response to
brightness changes instead of conventional intensity frames, and feature
ultra-high sensitivity at low bandwidth. The new mechanism demonstrates great
advantages in challenging scenarios with fast motion and large dynamic range.
However, the recorded events might be highly sparse due to either limited
hardware bandwidth or extreme photon starvation in harsh environments. To
unlock the full potential of event cameras, we propose an inventive event
sequence completion approach conforming to the unique characteristics of event
data in both the processing stage and the output form. Specifically, we treat
event streams as 3D event clouds in the spatiotemporal domain, develop a
diffusion-based generative model to generate dense clouds in a coarse-to-fine
manner, and recover exact timestamps to maintain the temporal resolution of raw
data successfully. To validate the effectiveness of our method comprehensively,
we perform extensive experiments on three widely used public datasets with
different spatial resolutions, and additionally collect a novel event dataset
covering diverse scenarios with highly dynamic motions and under harsh
illumination. Besides generating high-quality dense events, our method can
benefit downstream applications such as object classification and intensity
frame reconstruction.
