  Neural Radiance Fields (NeRF) achieves impressive 3D representation learning
and novel view synthesis results with high-quality multi-view images as input.
However, motion blur in images often occurs in low-light and high-speed motion
scenes, which significantly degrades the reconstruction quality of NeRF.
Previous deblurring NeRF methods struggle to estimate pose and lighting changes
during the exposure time, making them unable to accurately model the motion
blur. The bio-inspired event camera measuring intensity changes with high
temporal resolution makes up this information deficiency. In this paper, we
propose Event-driven Bundle Adjustment for Deblurring Neural Radiance Fields
(EBAD-NeRF) to jointly optimize the learnable poses and NeRF parameters by
leveraging the hybrid event-RGB data. An intensity-change-metric event loss and
a photo-metric blur loss are introduced to strengthen the explicit modeling of
camera motion blur. Experiments on both synthetic and real-captured data
demonstrate that EBAD-NeRF can obtain accurate camera trajectory during the
exposure time and learn a sharper 3D representations compared to prior works.
