  The rendering procedure used by neural radiance fields (NeRF) samples a scene
with a single ray per pixel and may therefore produce renderings that are
excessively blurred or aliased when training or testing images observe scene
content at different resolutions. The straightforward solution of supersampling
by rendering with multiple rays per pixel is impractical for NeRF, because
rendering each ray requires querying a multilayer perceptron hundreds of times.
Our solution, which we call "mip-NeRF" (a la "mipmap"), extends NeRF to
represent the scene at a continuously-valued scale. By efficiently rendering
anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable
aliasing artifacts and significantly improves NeRF's ability to represent fine
details, while also being 7% faster than NeRF and half the size. Compared to
NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with
NeRF and by 60% on a challenging multiscale variant of that dataset that we
present. Mip-NeRF is also able to match the accuracy of a brute-force
supersampled NeRF on our multiscale dataset while being 22x faster.
