Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional cameras would fail. Recent work has demonstrated impressive results using Convolutional Neural Networks (CNNs) for video reconstruction and optic flow with events. We present strategies for improving training data for event based CNNs that result in 25-40% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with our method, and up to 80% for optic flow networks. A challenge in evaluating event based video reconstruction is lack of quality groundtruth images in existing datasets. To address this, we present a new High Quality Frames (HQF) dataset, containing events and groundtruth frames from a DAVIS240C that are well-exposed and minimally motion-blurred. We evaluate our method on HQF + several existing major event camera datasets.

本文介绍了一种用于提高事件基于CNNs的训练数据的策略，可为视频重建网络和光流网络带来20-40％的性能提升，并提出了一种新的高质量数据集HQF以解决事件基于视频重建缺乏质量地真实图像的问题。

降低事件相机的模拟到现实差距