  Neural radiance fields (NeRF) excel at synthesizing new views given
multi-view, calibrated images of a static scene. When scenes include
distractors, which are not persistent during image capture (moving objects,
lighting variations, shadows), artifacts appear as view-dependent effects or
'floaters'. To cope with distractors, we advocate a form of robust estimation
for NeRF training, modeling distractors in training data as outliers of an
optimization problem. Our method successfully removes outliers from a scene and
improves upon our baselines, on synthetic and real-world scenes. Our technique
is simple to incorporate in modern NeRF frameworks, with few hyper-parameters.
It does not assume a priori knowledge of the types of distractors, and is
instead focused on the optimization problem rather than pre-processing or
modeling transient objects. More results on our page
https://robustnerf.github.io.
