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.

该研究提出一种基于变分模型、考虑异步事件的事件相机重构图像的方法，可以以任意帧率实时重构图像，无需显式估计光流。