This paper 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 (GI) of a scene into individual objects’ light transportation represented via neural networks, and 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)，这是物体与环境之间动态光传输的一种新颖的神经表示。我们的方法将场景的全局照明 (GI) 分解为通过神经网络表示的单个对象的光传输，然后将它们显式组合。因此，它可以使用动态照明、相机、材质和对象进行灵活渲染。我们的渲染具有各种重要的全局照明效果，例如漫射照明、光泽照明、动态阴影和间接照明，从而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，而是实现了与最先进的渲染框架相当的渲染质量，