Due to the real-time rendering performance, 3D Gaussian Splatting (3DGS) has emerged as the leading method for radiance field reconstruction. However, its reliance on spherical harmonics for color encoding inherently limits its ability to separate diffuse and specular components, making it challenging to accurately represent complex reflections. To address this, we propose a novel enhanced Gaussian kernel that explicitly models specular effects through view-dependent opacity. Meanwhile, we introduce an error-driven compensation strategy to improve rendering quality in existing 3DGS scenes. Our method begins with 2D Gaussian initialization and then adaptively inserts and optimizes enhanced Gaussian kernels, ultimately producing an augmented radiance field. Experiments demonstrate that our method not only surpasses state-of-the-art NeRF methods in rendering performance but also achieves greater parameter efficiency. Project page at: https://xiaoxinyyx.github.io/augs.

翻译：由于其实时渲染性能，三维高斯泼溅（3DGS）已成为辐射场重建的主流方法。然而，其依赖球谐函数进行颜色编码的方式固有地限制了其分离漫反射与镜面反射分量的能力，使得准确表示复杂反射具有挑战性。为解决此问题，我们提出了一种新颖的增强高斯核，通过视图相关的透明度显式建模镜面反射效应。同时，我们引入了一种误差驱动的补偿策略，以提升现有3DGS场景的渲染质量。我们的方法始于二维高斯初始化，随后自适应地插入并优化增强高斯核，最终生成一个增强辐射场。实验表明，我们的方法不仅在渲染性能上超越了最先进的NeRF方法，还实现了更高的参数效率。项目页面位于：https://xiaoxinyyx.github.io/augs。