Event cameras or neuromorphic cameras mimic the human perception system as they measure the per-pixel intensity change rather than the actual intensity level. In contrast to traditional cameras, such cameras capture new information about the scene at MHz frequency in the form of sparse events. The high temporal resolution comes at the cost of losing the familiar per-pixel intensity information. In this work we propose a variational model that accurately models the behaviour of event cameras, enabling reconstruction of intensity images with arbitrary frame rate in real-time. Our method is formulated on a per-event-basis, where we explicitly incorporate information about the asynchronous nature of events via an event manifold induced by the relative timestamps of events. In our experiments we verify that solving the variational model on the manifold produces high-quality images without explicitly estimating optical flow.
|Publication status||E-pub ahead of print - 23 Sep 2016|
|Event||2016 British Machine Vision Conference: BMVC 2016 - York, United Kingdom|
Duration: 19 Sep 2016 → 22 Sep 2016
|Conference||2016 British Machine Vision Conference|
|Period||19/09/16 → 22/09/16|