  Neural implicit representation of visual scenes has attracted a lot of
attention in recent research of computer vision and graphics. Most prior
methods focus on how to reconstruct 3D scene representation from a set of
images. In this work, we demonstrate the possibility to recover the neural
radiance fields (NeRF) from a single blurry image and its corresponding event
stream. We model the camera motion with a cubic B-Spline in SE(3) space. Both
the blurry image and the brightness change within a time interval, can then be
synthesized from the 3D scene representation given the 6-DoF poses interpolated
from the cubic B-Spline. Our method can jointly learn both the implicit neural
scene representation and recover the camera motion by minimizing the
differences between the synthesized data and the real measurements without
pre-computed camera poses from COLMAP. We evaluate the proposed method with
both synthetic and real datasets. The experimental results demonstrate that we
are able to render view-consistent latent sharp images from the learned NeRF
and bring a blurry image alive in high quality. Code and data are available at
https://github.com/WU-CVGL/BeNeRF.
