Decomposing a scene into its shape, reflectance, and illumination is a challenging but essential problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination. Though recent work has shown that implicit representations can be used to model the radiance field of an object, these techniques only enable view synthesis and not relighting. Additionally, evaluating these radiance fields is resource and time-intensive. By decomposing a scene into explicit representations, any rendering framework can be leveraged to generate novel views under any illumination in real-time. NeRD is a method that achieves this decomposition by introducing physically-based rendering to neural radiance fields. Even challenging non-Lambertian reflectances, complex geometry, and unknown illumination can be decomposed into high-quality models. The datasets and code is available on the project page: https://markboss.me/publication/2021-nerd/
翻译:将场景分解成其形状、反射和光化是计算机视觉和图形中一个具有挑战性但至关重要的问题。当光化不是实验室条件下的单一光源,而是无限制的环境光化时,这一问题就具有内在的更大挑战性。虽然最近的工作表明,可以使用隐含的表象来模拟物体的光亮场,但这些技术只能进行视觉合成而不是点亮。此外,评价这些光亮场是资源和时间密集型的。通过将场景分解成清晰的表象,任何显示框架都可以在实时任何光化下产生新的观点。NERD是一种方法,通过向神经光亮场引入物理的图象来实现这种分解。即使是挑战非光化的反射场、复杂的几何形状和未知的光化也可以分解成高质量的模型。数据集和代码可以在项目网页上查阅: https://marcos.me/publication/2021-nerd/ 上查阅。