  A variety of Neural Radiance Fields (NeRF) methods have recently achieved
remarkable success in high render speed. However, current accelerating methods
are specialized and incompatible with various implicit methods, preventing
real-time composition over various types of NeRF works. Because NeRF relies on
sampling along rays, it is possible to provide general guidance for
acceleration. To that end, we propose a general implicit pipeline for composing
NeRF objects quickly. Our method enables the casting of dynamic shadows within
or between objects using analytical light sources while allowing multiple NeRF
objects to be seamlessly placed and rendered together with any arbitrary rigid
transformations. Mainly, our work introduces a new surface representation known
as Neural Depth Fields (NeDF) that quickly determines the spatial relationship
between objects by allowing direct intersection computation between rays and
implicit surfaces. It leverages an intersection neural network to query NeRF
for acceleration instead of depending on an explicit spatial structure.Our
proposed method is the first to enable both the progressive and interactive
composition of NeRF objects. Additionally, it also serves as a previewing
plugin for a range of existing NeRF works.
