  Training a Neural Radiance Field (NeRF) without pre-computed camera poses is
challenging. Recent advances in this direction demonstrate the possibility of
jointly optimising a NeRF and camera poses in forward-facing scenes. However,
these methods still face difficulties during dramatic camera movement. We
tackle this challenging problem by incorporating undistorted monocular depth
priors. These priors are generated by correcting scale and shift parameters
during training, with which we are then able to constrain the relative poses
between consecutive frames. This constraint is achieved using our proposed
novel loss functions. Experiments on real-world indoor and outdoor scenes show
that our method can handle challenging camera trajectories and outperforms
existing methods in terms of novel view rendering quality and pose estimation
accuracy. Our project page is https://nope-nerf.active.vision.
