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Authors: Stefano Pini 1 ; Guido Borghi 2 and Roberto Vezzani 2 ; 1

Affiliations: 1 DIEF - Dipartimento di Ingegneria “Enzo Ferrari”, University of Modena and Reggio Emilia, Italy ; 2 AIRI - Artificial Intelligence Research and Innovation Center, University of Modena and Reggio Emilia, Italy

Keyword(s): Event Cameras, Event Frames, Simulated Event Frames, Color Frame Synthesis, Automotive.

Abstract: Event cameras are biologically-inspired sensors that gather the temporal evolution of the scene. They capture pixel-wise brightness variations and output a corresponding stream of asynchronous events. Despite having multiple advantages with respect to traditional cameras, their use is partially prevented by the limited applicability of traditional data processing and vision algorithms. To this aim, we present a framework which exploits the output stream of event cameras to synthesize RGB frames, relying on an initial or a periodic set of color key-frames and the sequence of intermediate events. Differently from existing work, we propose a deep learning-based frame synthesis method, consisting of an adversarial architecture combined with a recurrent module. Qualitative results and quantitative per-pixel, perceptual, and semantic evaluation on four public datasets confirm the quality of the synthesized images.

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Paper citation in several formats:
Pini, S.; Borghi, G. and Vezzani, R. (2020). Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 37-47. DOI: 10.5220/0008934700370047

@conference{visapp20,
author={Stefano Pini. and Guido Borghi. and Roberto Vezzani.},
title={Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={37-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008934700370047},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras
SN - 978-989-758-402-2
IS - 2184-4321
AU - Pini, S.
AU - Borghi, G.
AU - Vezzani, R.
PY - 2020
SP - 37
EP - 47
DO - 10.5220/0008934700370047
PB - SciTePress

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