  Camera calibration is an important prerequisite towards the solution of 3D
computer vision problems. Traditional methods rely on static images of a
calibration pattern. This raises interesting challenges towards the practical
usage of event cameras, which notably require image change to produce
sufficient measurements. The current standard for event camera calibration
therefore consists of using flashing patterns. They have the advantage of
simultaneously triggering events in all reprojected pattern feature locations,
but it is difficult to construct or use such patterns in the field. We present
the first dynamic event camera calibration algorithm. It calibrates directly
from events captured during relative motion between camera and calibration
pattern. The method is propelled by a novel feature extraction mechanism for
calibration patterns, and leverages existing calibration tools before
optimizing all parameters through a multi-segment continuous-time formulation.
As demonstrated through our results on real data, the obtained calibration
method is highly convenient and reliably calibrates from data sequences
spanning less than 10 seconds.
