RRM：使用輻射引導材料提取的可重新照明資產

RRM: Relightable assets using Radiance guided Material extraction

July 8, 2024

作者: Diego Gomez, Julien Philip, Adrien Kaiser, Élie Michel

cs.AI

摘要

在過去幾年中，以任意光線合成 NeRFs 已成為一個重要問題。最近的研究致力於通過提取基於物理的參數來解決這個問題，這些參數可以在任意光線下進行渲染，但它們在能處理的場景範圍上受到限制，通常對光澤場景處理不當。我們提出了 RRM，一種方法，即使在存在高度反射物體的情況下，也可以提取場景的材料、幾何和環境光照。我們的方法包括一種具有物理感知的輻射場表示，該表示通知基於物理的參數，以及基於拉普拉斯金字塔的表達性環境光結構。我們展示了我們的貢獻在參數檢索任務上優於最先進技術，從而實現了對表面場景的高保真燈光效果和新視角合成。

English

Synthesizing NeRFs under arbitrary lighting has become a seminal problem in the last few years. Recent efforts tackle the problem via the extraction of physically-based parameters that can then be rendered under arbitrary lighting, but they are limited in the range of scenes they can handle, usually mishandling glossy scenes. We propose RRM, a method that can extract the materials, geometry, and environment lighting of a scene even in the presence of highly reflective objects. Our method consists of a physically-aware radiance field representation that informs physically-based parameters, and an expressive environment light structure based on a Laplacian Pyramid. We demonstrate that our contributions outperform the state-of-the-art on parameter retrieval tasks, leading to high-fidelity relighting and novel view synthesis on surfacic scenes.