Decoupling the illumination in 3D scenes is crucial for novel view synthesis and relighting. In this paper, we propose a novel method for representing a scene illuminated by a point light using a set of relightable 3D Gaussian points. Inspired by the Blinn-Phong model, our approach decomposes the scene into ambient, diffuse, and specular components, enabling the synthesis of realistic lighting effects. To facilitate the decomposition of geometric information independent of lighting conditions, we introduce a novel bilevel optimization-based meta-learning framework. The fundamental idea is to view the rendering tasks under various lighting positions as a multi-task learning problem, which our meta-learning approach effectively addresses by generalizing the learned Gaussian geometries not only across different viewpoints but also across diverse light positions. Experimental results demonstrate the effectiveness of our approach in terms of training efficiency and rendering quality compared to existing methods for free-viewpoint relighting.

翻译：在三维场景中解耦光照对于新视角合成与重光照至关重要。本文提出了一种新颖的方法，使用一组可重光照的3D高斯点来表示由点光源照亮的场景。受Blinn-Phong模型的启发，我们的方法将场景分解为环境光、漫反射和镜面反射分量，从而能够合成逼真的光照效果。为了促进独立于光照条件的几何信息分解，我们引入了一种新颖的基于双层优化的元学习框架。其核心思想是将不同光照位置下的渲染任务视为一个多任务学习问题，我们的元学习方法通过将学习到的高斯几何不仅推广到不同视角，还推广到不同的光照位置，从而有效地解决了这一问题。实验结果表明，与现有的自由视点重光照方法相比，我们的方法在训练效率和渲染质量方面均表现出色。