This article presents object-oriented neural light transfer (NeLT), a novel neural representation of the dynamic light transportation between an object and the environment. Our method disentangles the global illumination of a scene into individual objects’ light transportation represented via neural networks, then composes them explicitly. It therefore enables flexible rendering with dynamic lighting, cameras, materials, and objects. Our rendering features various important global illumination effects, such as diffuse illumination, glossy illumination, dynamic shadowing, and indirect illumination, which completes the capability of existing neural object representation. Experiments show that NeLT does not require path tracing or shading results as input but achieves rendering quality comparable to state-of-the-art rendering frameworks, including the recent deep learning based denoisers.

本文介绍了面向对象的神经光传输（NeLT），这是一种物体与环境之间动态光传输的新颖神经表示。我们的方法将场景的全局照明分解为通过神经网络表示的各个对象的光传输，然后明确地组合它们。因此，它可以使用动态光照、相机、材质和对象进行灵活的渲染。我们的渲染具有各种重要的全局照明效果，例如漫反射照明、光泽照明、动态阴影和间接照明，完善了现有神经对象表示的能力。实验表明，NeLT 不需要路径跟踪或着色结果作为输入，但可以实现与最先进的渲染框架相当的渲染质量，