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.

翻译：多种神经辐射场（NeRF）方法近期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，阻碍了不同NeRF作品间的实时组合。由于NeRF依赖于沿射线的采样，理论上存在为加速提供通用指导的可能性。为此，我们提出一种用于快速组合NeRF物体的通用隐式管线。该方法支持利用解析光源在物体内部或之间投射动态阴影，同时允许多个NeRF物体在任意刚体变换下无缝放置与协同渲染。核心而言，本工作引入了一种称为神经深度场（NeDF）的新型表面表征，通过允许射线与隐式表面之间直接进行相交计算，快速确定物体间的空间关系。它利用相交神经网络查询NeRF以加速计算，而非依赖显式空间结构。所提方法是首个能够实现NeRF物体渐进式与交互式组合的工作。此外，该方法还可作为现有多种NeRF作品的预览插件。