  The neuromorphic event cameras, which capture the optical changes of a scene,
have drawn increasing attention due to their high speed and low power
consumption. However, the event data are noisy, sparse, and nonuniform in the
spatial-temporal domain with an extremely high temporal resolution, making it
challenging to design backend algorithms for event-based vision. Existing
methods encode events into point-cloud-based or voxel-based representations,
but suffer from noise and/or information loss. Additionally, there is little
research that systematically studies how to handle static and dynamic scenes
with one universal design for event-based vision. This work proposes the
Aligned Event Tensor (AET) as a novel event data representation, and a neat
framework called Event Frame Net (EFN), which enables our model for event-based
vision under static and dynamic scenes. The proposed AET and EFN are evaluated
on various datasets, and proved to surpass existing state-of-the-art methods by
large margins. Our method is also efficient and achieves the fastest inference
speed among others.
