  Volume rendering using neural fields has shown great promise in capturing and
synthesizing novel views of 3D scenes. However, this type of approach requires
querying the volume network at multiple points along each viewing ray in order
to render an image, resulting in very slow rendering times. In this paper, we
present a method that overcomes this limitation by learning a direct mapping
from camera rays to locations along the ray that are most likely to influence
the pixel's final appearance. Using this approach we are able to render, train
and fine-tune a volumetrically-rendered neural field model an order of
magnitude faster than standard approaches. Unlike existing methods, our
approach works with general volumes and can be trained end-to-end.
