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.

翻译：本文提出了一种新颖的神经隐式辐射表示方法，用于从少量无结构照片中实现自由视角重光照，这些照片中的物体由与视角位置不同的移动点光源照明。我们将形状表示为多层感知器建模的有符号距离函数。与先前的可重光照隐式神经表示不同，我们未解耦不同反射分量，而是通过第二个多层感知器对每个点的局部和全局反射进行建模，该感知器除了密度特征、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，还引入了阴影和高光提示，以辅助网络模拟相应的高频光传输效应。这些提示作为建议提供，网络可自主决定如何将其整合到最终的重光照结果中。我们在包含各种形状、材质属性和全局光照传输的合成场景与真实场景上验证并展示了该神经隐式表示的有效性。