  Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such
as floaters or flawed geometry when rendered outside the camera trajectory.
Existing evaluation protocols often do not capture these effects, since they
usually only assess image quality at every 8th frame of the training capture.
To push forward progress in novel-view synthesis, we propose a new dataset and
evaluation procedure, where two camera trajectories are recorded of the scene:
one used for training, and the other for evaluation. In this more challenging
in-the-wild setting, we find that existing hand-crafted regularizers do not
remove floaters nor improve scene geometry. Thus, we propose a 3D
diffusion-based method that leverages local 3D priors and a novel density-based
score distillation sampling loss to discourage artifacts during NeRF
optimization. We show that this data-driven prior removes floaters and improves
scene geometry for casual captures.
