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Event cameras are biologically-inspired sensors that gather the temporalevolution of the scene. They capture pixel-wise brightness variations andoutput a corresponding stream of asynchronous events. Despite having multipleadvantages with respect to traditional cameras, their use is partiallyprevented by the limited applicability of traditional data processing andvision algorithms. To this aim, we present a framework which exploits theoutput stream of event cameras to synthesize RGB frames, relying on an initialor a periodic set of color key-frames and the sequence of intermediate events.Differently from existing work, we propose a deep learning-based framesynthesis method, consisting of an adversarial architecture combined with arecurrent module. Qualitative results and quantitative per-pixel, perceptual,and semantic evaluation on four public datasets confirm the quality of thesynthesized images.

Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras