Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location. While NeRF-based techniques excel at representing fine geometric structures with smoothly varying view-dependent appearance, they often fail to accurately capture and reproduce the appearance of glossy surfaces. We address this limitation by introducing Ref-NeRF, which replaces NeRF's parameterization of view-dependent outgoing radiance with a representation of reflected radiance and structures this function using a collection of spatially-varying scene properties. We show that together with a regularizer on normal vectors, our model significantly improves the realism and accuracy of specular reflections. Furthermore, we show that our model's internal representation of outgoing radiance is interpretable and useful for scene editing.

Ref-NeRF通过替换NeRF的视角相关的出射辐射参数化为反射辐射参数来提高NeRF方法表示高光表面外观的准确性，并且使用一系列空间变化场景属性来构造该函数结构，同时结合法线向量的正则化，可以实现更加真实和准确的高光反射，此外，我们的模型的内部表示也具有可解释性，用于场景编辑。

Ref-NeRF: 神经辐射场的结构化视角依赖外观