paper

EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction

arXiv:2305.00434 · doi:10.1109/CVPRW59228.2023.00410

Abstract

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.

arXiv comment19 pages, 9 figures. Has been accepted for publication at the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, 2023. The project page can be found at https://ercanburak.github.io/evreal.html

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