  We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an
HDR radiance field from a set of low dynamic range (LDR) views with different
exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and
novel LDR views under different exposures. The key to our method is to model
the physical imaging process, which dictates that the radiance of a scene point
transforms to a pixel value in the LDR image with two implicit functions: a
radiance field and a tone mapper. The radiance field encodes the scene radiance
(values vary from 0 to +infty), which outputs the density and radiance of a ray
by giving corresponding ray origin and ray direction. The tone mapper models
the mapping process that a ray hitting on the camera sensor becomes a pixel
value. The color of the ray is predicted by feeding the radiance and the
corresponding exposure time into the tone mapper. We use the classic volume
rendering technique to project the output radiance, colors, and densities into
HDR and LDR images, while only the input LDR images are used as the
supervision. We collect a new forward-facing HDR dataset to evaluate the
proposed method. Experimental results on synthetic and real-world scenes
validate that our method can not only accurately control the exposures of
synthesized views but also render views with a high dynamic range.
