  We present ExBluRF, a novel view synthesis method for extreme motion blurred
images based on efficient radiance fields optimization. Our approach consists
of two main components: 6-DOF camera trajectory-based motion blur formulation
and voxel-based radiance fields. From extremely blurred images, we optimize the
sharp radiance fields by jointly estimating the camera trajectories that
generate the blurry images. In training, multiple rays along the camera
trajectory are accumulated to reconstruct single blurry color, which is
equivalent to the physical motion blur operation. We minimize the
photo-consistency loss on blurred image space and obtain the sharp radiance
fields with camera trajectories that explain the blur of all images. The joint
optimization on the blurred image space demands painfully increasing
computation and resources proportional to the blur size. Our method solves this
problem by replacing the MLP-based framework to low-dimensional 6-DOF camera
poses and voxel-based radiance fields. Compared with the existing works, our
approach restores much sharper 3D scenes from challenging motion blurred views
with the order of 10 times less training time and GPU memory consumption.
