  Event cameras, inspired by biological vision systems, provide a natural and
data efficient representation of visual information. Visual information is
acquired in the form of events that are triggered by local brightness changes.
Each pixel location of the camera's sensor records events asynchronously and
independently with very high temporal resolution. However, because most
brightness changes are triggered by relative motion of the camera and the
scene, the events recorded at a single sensor location seldom correspond to the
same world point. To extract meaningful information from event cameras, it is
helpful to register events that were triggered by the same underlying world
point. In this work we propose a new model of event data that captures its
natural spatio-temporal structure. We start by developing a model for aligned
event data. That is, we develop a model for the data as though it has been
perfectly registered already. In particular, we model the aligned data as a
spatio-temporal Poisson point process. Based on this model, we develop a
maximum likelihood approach to registering events that are not yet aligned.
That is, we find transformations of the observed events that make them as
likely as possible under our model. In particular we extract the camera
rotation that leads to the best event alignment. We show new state of the art
accuracy for rotational velocity estimation on the DAVIS 240C dataset. In
addition, our method is also faster and has lower computational complexity than
several competing methods.
