Reflective Gaussian Splatting

Abstract

Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we introduce a Reflective Gaussian splatting (Ref-Gaussian) framework characterized with two components: (I) Physically based deferred rendering that empowers the rendering equation with pixel-level material properties via formulating split-sum approximation; (II) Gaussian-grounded inter-reflection that realizes the desired inter-reflection function within a Gaussian splatting paradigm for the first time. To enhance geometry modeling, we further introduce material-aware normal propagation and an initial per-Gaussian shading stage, along with 2D Gaussian primitives. Extensive experiments on standard datasets demonstrate that Ref-Gaussian surpasses existing approaches in terms of quantitative metrics, visual quality, and compute efficiency. Further, we show that our method serves as a unified solution for both reflective and non-reflective scenes, going beyond the previous alternatives focusing on only reflective scenes. Also, we illustrate that Ref-Gaussian supports more applications such as relighting and editing.

Cite

Text

Yao et al. "Reflective Gaussian Splatting." International Conference on Learning Representations, 2025.

Markdown

[Yao et al. "Reflective Gaussian Splatting." International Conference on Learning Representations, 2025.](https://mlanthology.org/iclr/2025/yao2025iclr-reflective/)

BibTeX

@inproceedings{yao2025iclr-reflective,
  title     = {{Reflective Gaussian Splatting}},
  author    = {Yao, Yuxuan and Zeng, Zixuan and Gu, Chun and Zhu, Xiatian and Zhang, Li},
  booktitle = {International Conference on Learning Representations},
  year      = {2025},
  url       = {https://mlanthology.org/iclr/2025/yao2025iclr-reflective/}
}