@inproceedings{43e60f2a579d422bbaa9e4547be22473,
title = "Reducing the Sim-to-Real Gap for Event Cameras",
abstract = "Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called {\textquoteleft}events{\textquoteright} 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 20–40\% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with our method, and up to 15\% for optic flow networks. A challenge in evaluating event based video reconstruction is lack of quality ground truth images in existing datasets. To address this, we present a new High Quality Frames (HQF) dataset, containing events and ground truth frames from a DAVIS240C that are well-exposed and minimally motion-blurred. We evaluate our method on HQF + several existing major event camera datasets.",
author = "Timo Stoffregen and Cedric Scheerlinck and Davide Scaramuzza and Tom Drummond and Nick Barnes and Lindsay Kleeman and Robert Mahony",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-58583-9\_32",
language = "English",
isbn = "9783030585822",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science+Business Media B.V.",
pages = "534--549",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings",
address = "Netherlands",
}