  Neural Radiance Fields (NeRF) is a technique for high quality novel view
synthesis from a collection of posed input images. Like most view synthesis
methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images
have been processed by a lossy camera pipeline that smooths detail, clips
highlights, and distorts the simple noise distribution of raw sensor data. We
modify NeRF to instead train directly on linear raw images, preserving the
scene's full dynamic range. By rendering raw output images from the resulting
NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In
addition to changing the camera viewpoint, we can manipulate focus, exposure,
and tonemapping after the fact. Although a single raw image appears
significantly more noisy than a postprocessed one, we show that NeRF is highly
robust to the zero-mean distribution of raw noise. When optimized over many
noisy raw inputs (25-200), NeRF produces a scene representation so accurate
that its rendered novel views outperform dedicated single and multi-image deep
raw denoisers run on the same wide baseline input images. As a result, our
method, which we call RawNeRF, can reconstruct scenes from extremely noisy
images captured in near-darkness.
