  Neural Radiance Fields (NeRF) have recently gained a surge of interest within
the computer vision community for its power to synthesize photorealistic novel
views of real-world scenes. One limitation of NeRF, however, is its requirement
of accurate camera poses to learn the scene representations. In this paper, we
propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from
imperfect (or even unknown) camera poses -- the joint problem of learning
neural 3D representations and registering camera frames. We establish a
theoretical connection to classical image alignment and show that
coarse-to-fine registration is also applicable to NeRF. Furthermore, we show
that na\"ively applying positional encoding in NeRF has a negative impact on
registration with a synthesis-based objective. Experiments on synthetic and
real-world data show that BARF can effectively optimize the neural scene
representations and resolve large camera pose misalignment at the same time.
This enables view synthesis and localization of video sequences from unknown
camera poses, opening up new avenues for visual localization systems (e.g.
SLAM) and potential applications for dense 3D mapping and reconstruction.
