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.

本文提出了一种使用直接映射从相机光线到沿着光线最有可能影响像素最终外观的位置的方法，从而实现比标准方法快一个数量级的体积渲染神经网络模型的渲染、训练和微调，同时避免了在多个点上查询体积网络以呈现图像的过程时间缓慢的问题。与现有的方法不同的是，我们的方法适用于一般的体积，并且可以进行端到端的训练。