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Event cameras or neuromorphic cameras mimic the human perception system asthey measure the per-pixel intensity change rather than the actual intensitylevel. In contrast to traditional cameras, such cameras capture new informationabout the scene at MHz frequency in the form of sparse events. The hightemporal resolution comes at the cost of losing the familiar per-pixelintensity information. In this work we propose a variational model thataccurately models the behaviour of event cameras, enabling reconstruction ofintensity images with arbitrary frame rate in real-time. Our method isformulated on a per-event-basis, where we explicitly incorporate informationabout the asynchronous nature of events via an event manifold induced by therelative timestamps of events. In our experiments we verify that solving thevariational model on the manifold produces high-quality images withoutexplicitly estimating optical flow.

Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation