Naturalizing Neuromorphic Vision Event Streams Using GANs

Understand

Dynamic vision sensors are able to operate at high temporal resolutions within resource constrained environments, though at the expense of capturing static content.

• The sparse nature of event streams enables efficient downstream processing tasks as they are suited for power-efficient spiking neural networks.
• One of the challenges associated with neuromorphic vision is the lack of interpretability of event streams.
• While most application use-cases do not intend for the event stream to be visually interpreted by anything other than a classification network, there is a lost opportunity to integrating these sensors in spaces that conventional high-speed CMOS sensors cannot go.