  We propose a generic event camera calibration framework using image
reconstruction. Instead of relying on blinking LED patterns or external
screens, we show that neural-network-based image reconstruction is well suited
for the task of intrinsic and extrinsic calibration of event cameras. The
advantage of our proposed approach is that we can use standard calibration
patterns that do not rely on active illumination. Furthermore, our approach
enables the possibility to perform extrinsic calibration between frame-based
and event-based sensors without additional complexity. Both simulation and
real-world experiments indicate that calibration through image reconstruction
is accurate under common distortion models and a wide variety of distortion
parameters
