  Though neural radiance fields (NeRF) have demonstrated impressive view
synthesis results on objects and small bounded regions of space, they struggle
on "unbounded" scenes, where the camera may point in any direction and content
may exist at any distance. In this setting, existing NeRF-like models often
produce blurry or low-resolution renderings (due to the unbalanced detail and
scale of nearby and distant objects), are slow to train, and may exhibit
artifacts due to the inherent ambiguity of the task of reconstructing a large
scene from a small set of images. We present an extension of mip-NeRF (a NeRF
variant that addresses sampling and aliasing) that uses a non-linear scene
parameterization, online distillation, and a novel distortion-based regularizer
to overcome the challenges presented by unbounded scenes. Our model, which we
dub "mip-NeRF 360" as we target scenes in which the camera rotates 360 degrees
around a point, reduces mean-squared error by 57% compared to mip-NeRF, and is
able to produce realistic synthesized views and detailed depth maps for highly
intricate, unbounded real-world scenes.
