利用陰影和高光提示重新照亮神經輻射場

摘要

本文提出了一種新穎的神經隱式輻射表示法，用於從一小組非結構化的物體照片中進行自由視角燈光重塑，這些照片是由一個移動的點光源照亮，視角位置與之不同。我們將形狀表示為由多層感知器建模的符號距離函數。與先前可重塑的隱式神經表示法不同，我們沒有將不同的反射成分分離，而是通過第二個多層感知器在每個點上模擬局部和全局反射，除了密度特徵、當前位置、法線（從符號距離函數獲得）、視角方向和光源位置外，還考慮陰影和高光提示，以幫助網絡模擬相應的高頻光傳輸效應。這些提示僅作為建議提供，我們讓網絡決定如何將其納入最終的燈光重塑結果。我們在展示具有各種形狀、材料特性和全局照明光傳輸的合成和真實場景上展示並驗證了我們的神經隱式表示法。

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.

利用陰影和高光提示重新照亮神經輻射場

Relighting Neural Radiance Fields with Shadow and Highlight Hints

摘要

Support