  Event camera, a bio-inspired asynchronous triggered camera, offers promising
prospects for fusion with frame-based cameras owing to its low latency and high
dynamic range. However, calibrating stereo vision systems that incorporate both
event and frame-based cameras remains a significant challenge. In this letter,
we present EF-Calib, a spatiotemporal calibration framework for event- and
frame-based cameras using continuous-time trajectories. A novel calibration
pattern applicable to both camera types and the corresponding event recognition
algorithm is proposed. Leveraging the asynchronous nature of events, a
derivable piece-wise B-spline to represent camera pose continuously is
introduced, enabling calibration for intrinsic parameters, extrinsic
parameters, and time offset, with analytical Jacobians provided. Various
experiments are carried out to evaluate the calibration performance of
EF-Calib, including calibration experiments for intrinsic parameters, extrinsic
parameters, and time offset. Experimental results show that EF-Calib achieves
the most accurate intrinsic parameters compared to current SOTA, the close
accuracy of the extrinsic parameters compared to the frame-based results, and
accurate time offset estimation. EF-Calib provides a convenient and accurate
toolbox for calibrating the system that fuses events and frames. The code of
this paper will also be open-sourced at: https://github.com/wsakobe/EF-Calib.
