Recently, a variety of Neural radiance fields methods have garnered remarkable success in high render speed. However, current accelerating methods is specialized and not compatible for various implicit method, which prevent a real-time composition over different kinds of NeRF works. Since NeRF relies on sampling along rays, it's possible to provide a guidance generally. We propose a general implicit pipeline to rapidly compose NeRF objects. This new 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.

翻译：近年来，多种神经辐射场方法在高速渲染方面取得了显著成功。然而，当前的加速方法具有专用性，与各类隐式方法不兼容，从而阻碍了不同种类NeRF作品的实时组合。由于NeRF依赖于沿射线采样，因此有可能提供通用指导。我们提出了一种通用隐式流水线，用于快速组合NeRF对象。这一新方法能够通过分析光源在对象内部或对象之间投射动态阴影，同时允许多个NeRF对象以任意刚性变换无缝放置并一起渲染。主要地，我们的工作引入了一种称为神经深度场（NeDF）的新型表面表示，它通过允许射线与隐式表面之间的直接相交计算，快速确定对象之间的空间关系。它利用一个相交神经网络查询NeRF以实现加速，而非依赖于显式空间结构。我们的方法是首个能够同时实现NeRF对象的渐进式和交互式组合的方法。此外，它还可作为现有多种NeRF作品的预览插件。