GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis
<|reference_start|>GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis: 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.<|reference_end|>
@article{he2024gs-phong:,
    title={GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis},
    author={Yumeng He, Yunbo Wang, Xiaokang Yang},
    journal={arXiv preprint arXiv:2405.20791},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20791},
    primaryClass={cs.CV cs.LG}
}