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.

翻译：事件相机是一种新型视觉传感器，其采用异步且独立的像素单元，与传统基于帧的相机相比具有高动态范围和最小运动模糊等优势。然而，其输出结果难以被人类直接理解，因此从事件流重建强度图像成为事件视觉领域的基础任务。尽管近期基于深度学习的视频重建方法已展现出潜力，但该问题尚未完全解决。为了促进不同方法间的比较，标准化评估协议和多样化测试数据集至关重要。本文提出了一种统一的评估方法论，并引入名为EVREAL的开源框架，用于对文献中多种事件驱动视频重建方法进行综合基准测试与分析。通过EVREAL，我们详细分析了当前先进的事件驱动视频重建方法，揭示了不同设定、挑战性场景及下游任务下这些方法的性能表现，并提供了有价值的见解。