Event cameras are a new type of vision sensor that incorporates asynchronous
and independent pixels, offering advantages over traditional frame-based
cameras such as high dynamic range and minimal motion blur. However, their
output is not easily understandable by humans, making the reconstruction of
intensity images from event streams a fundamental task in event-based vision.
While recent deep learning-based methods have shown promise in video
reconstruction from events, this problem is not completely solved yet. To
facilitate comparison between different approaches, standardized evaluation
protocols and diverse test datasets are essential. This paper proposes a
unified evaluation methodology and introduces an open-source framework called
EVREAL to comprehensively benchmark and analyze various event-based video
reconstruction methods from the literature. Using EVREAL, we give a detailed
analysis of the state-of-the-art methods for event-based video reconstruction,
and provide valuable insights into the performance of these methods under
varying settings, challenging scenarios, and downstream tasks.

本文提出了 EVREAL 框架，并针对当前事件感知视觉领域中的基于深度学习的视频重建方法进行了统一的评价，旨在为不同的方法提供比较基础和统一的评价标准，提供对这些方法在不同场景下，挑战性场景和下游任务下性能的宝贵见解。