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The neuromorphic event cameras, which capture the optical changes of a scene,have drawn increasing attention due to their high speed and low powerconsumption. However, the event data are noisy, sparse, and nonuniform in thespatial-temporal domain with an extremely high temporal resolution, making itchallenging to design backend algorithms for event-based vision. Existingmethods encode events into point-cloud-based or voxel-based representations,but suffer from noise and/or information loss. Additionally, there is littleresearch that systematically studies how to handle static and dynamic sceneswith one universal design for event-based vision. This work proposes theAligned Event Tensor (AET) as a novel event data representation, and a neatframework called Event Frame Net (EFN), which enables our model for event-basedvision under static and dynamic scenes. The proposed AET and EFN are evaluatedon various datasets, and proved to surpass existing state-of-the-art methods bylarge margins. Our method is also efficient and achieves the fastest inferencespeed among others.

AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision