  Neural Radiance Fields (NeRF) have received considerable attention recently,
due to its impressive capability in photo-realistic 3D reconstruction and novel
view synthesis, given a set of posed camera images. Earlier work usually
assumes the input images are of good quality. However, image degradation (e.g.
image motion blur in low-light conditions) can easily happen in real-world
scenarios, which would further affect the rendering quality of NeRF. In this
paper, we present a novel bundle adjusted deblur Neural Radiance Fields
(BAD-NeRF), which can be robust to severe motion blurred images and inaccurate
camera poses. Our approach models the physical image formation process of a
motion blurred image, and jointly learns the parameters of NeRF and recovers
the camera motion trajectories during exposure time. In experiments, we show
that by directly modeling the real physical image formation process, BAD-NeRF
achieves superior performance over prior works on both synthetic and real
datasets. Code and data are available at https://github.com/WU-CVGL/BAD-NeRF.
