3D Gaussian Splatting (3DGS) has advanced radiance field reconstruction by enabling real-time rendering. However, its reliance on Gaussian kernels for geometry and low-order Spherical Harmonics (SH) for color encoding limits its ability to capture complex geometries and diverse colors. We introduce Deformable Beta Splatting (DBS), a deformable and compact approach that enhances both geometry and color representation. DBS replaces Gaussian kernels with deformable Beta Kernels, which offer bounded support and adaptive frequency control to capture fine geometric details with higher fidelity while achieving better memory efficiency. In addition, we extended the Beta Kernel to color encoding, which facilitates improved representation of diffuse and specular components, yielding superior results compared to SH-based methods. Furthermore, Unlike prior densification techniques that depend on Gaussian properties, we mathematically prove that adjusting regularized opacity alone ensures distribution-preserved Markov chain Monte Carlo (MCMC), independent of the splatting kernel type. Experimental results demonstrate that DBS achieves state-of-the-art visual quality while utilizing only 45% of the parameters and rendering 1.5x faster than 3DGS-based methods. Notably, for the first time, splatting-based methods outperform state-of-the-art Neural Radiance Fields, highlighting the superior performance and efficiency of DBS for real-time radiance field rendering.
3D Gaussian Splatting (3DGS) 通过实现实时渲染推进了辐射场重建的研究。然而，它依赖于高斯核来表示几何结构和低阶球面调和函数（SH）进行颜色编码，这限制了其捕捉复杂几何和多样化颜色的能力。为此，我们引入了 Deformable Beta Splatting (DBS)，一种可变形且紧凑的方法，增强了几何和颜色表示。DBS 用可变形 Beta 核替代了高斯核，Beta 核具有有界支持和自适应频率控制，能够以更高的保真度捕捉精细的几何细节，同时实现更好的内存效率。此外，我们将 Beta 核扩展到颜色编码，改善了漫反射和镜面反射成分的表示，与基于 SH 的方法相比，获得了更优的结果。进一步地，与依赖高斯性质的先前密集化技术不同，我们通过数学证明，单独调整正则化的透明度即可确保分布保持的马尔科夫链蒙特卡洛（MCMC），而与 Splatting 核类型无关。实验结果表明，DBS 实现了最先进的视觉质量，同时仅使用 3DGS 方法 45% 的参数，并且渲染速度比 3DGS 快 1.5 倍。值得注意的是，首次出现了 Splatting 方法超越最先进的神经辐射场（NeRF）技术，突显了 DBS 在实时辐射场渲染中的优越性能和效率。