This paper presents a novel neural implicit radiance representation for free
viewpoint relighting from a small set of unstructured photographs of an object
lit by a moving point light source different from the view position. We express
the shape as a signed distance function modeled by a multi layer perceptron. In
contrast to prior relightable implicit neural representations, we do not
disentangle the different reflectance components, but model both the local and
global reflectance at each point by a second multi layer perceptron that, in
addition, to density features, the current position, the normal (from the
signed distace function), view direction, and light position, also takes shadow
and highlight hints to aid the network in modeling the corresponding high
frequency light transport effects. These hints are provided as a suggestion,
and we leave it up to the network to decide how to incorporate these in the
final relit result. We demonstrate and validate our neural implicit
representation on synthetic and real scenes exhibiting a wide variety of
shapes, material properties, and global illumination light transport.

本研究提出了一种新颖的神经隐式亮度表示方法，用于从一小组不规则的物体照片中进行自由视角重照。我们通过多层感知器将形状表示为有符号距离函数。与先前的隐式神经重照方法不同，我们没有分离不同的反射分量，而是通过第二个多层感知器在每个点上模拟局部和全局反射，并融合阴影和高光提示来辅助网络对应的高频光传输效果建模。在合成和真实场景中展示和验证了我们的神经隐式表示方法，这些场景具有各种形状、材料属性和全局光照传输。

使用阴影和高光提示重新照明神经辐射场

Relighting Neural Radiance Fields with Shadow and Highlight Hints

We present a method for composing photorealistic scenes from captured images
of objects. Our work builds upon neural radiance fields (NeRFs), which
implicitly model the volumetric density and directionally-emitted radiance of a
scene. While NeRFs synthesize realistic pictures, they only model static scenes
and are closely tied to specific imaging conditions. This property makes NeRFs
hard to generalize to new scenarios, including new lighting or new arrangements
of objects. Instead of learning a scene radiance field as a NeRF does, we
propose to learn object-centric neural scattering functions (OSFs), a
representation that models per-object light transport implicitly using a
lighting- and view-dependent neural network. This enables rendering scenes even
when objects or lights move, without retraining. Combined with a volumetric
path tracing procedure, our framework is capable of rendering both intra- and
inter-object light transport effects including occlusions, specularities,
shadows, and indirect illumination. We evaluate our approach on scene
composition and show that it generalizes to novel illumination conditions,
producing photorealistic, physically accurate renderings of multi-object
scenes.

提出一种基于神经辐射场和基于对象的神经散射函数的方法，能够在不重新训练的情况下，对静态及动态场景进行光线追踪模拟，拥有在新光照和新物品排列情况下泛化能力的物理准确的多物体场景组成渲染技术。