利用阴影和高光提示对神经辐射场进行重新照明
Relighting Neural Radiance Fields with Shadow and Highlight Hints
August 25, 2023
作者: Chong Zeng, Guojun Chen, Yue Dong, Pieter Peers, Hongzhi Wu, Xin Tong
cs.AI
摘要
本文提出了一种新颖的神经隐式辐射表示方法,用于从一小组非结构化照片中的物体进行自由视点照明。这些照片中的物体由一个移动的点光源照亮,该光源与视角位置不同。我们将形状表示为由多层感知器建模的有符号距离函数。与先前的可照明隐式神经表示不同,我们不对不同的反射分量进行分离,而是通过第二个多层感知器在每个点上同时建模局部和全局反射。除了密度特征、当前位置、法线(来自有符号距离函数)、视角方向和光源位置外,该多层感知器还考虑阴影和高光提示,以帮助网络建模相应的高频光传输效应。这些提示只是作为建议提供,我们让网络决定如何将其纳入最终的照明结果中。我们在展示和验证我们的神经隐式表示时使用了合成和真实场景,这些场景展示了各种形状、材质属性和全局照明光传输。
English
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.