Recently, Gaussian splatting has emerged as a robust technique for representing 3D scenes, enabling real-time rasterization and high-fidelity rendering. However, Gaussians' inherent radial symmetry and smoothness constraints limit their ability to represent complex shapes, often requiring thousands of primitives to approximate detailed geometry. We introduce Deformable Radial Kernel (DRK), which extends Gaussian splatting into a more general and flexible framework. Through learnable radial bases with adjustable angles and scales, DRK efficiently models diverse shape primitives while enabling precise control over edge sharpness and boundary curvature. iven DRK's planar nature, we further develop accurate ray-primitive intersection computation for depth sorting and introduce efficient kernel culling strategies for improved rasterization efficiency. Extensive experiments demonstrate that DRK outperforms existing methods in both representation efficiency and rendering quality, achieving state-of-the-art performance while dramatically reducing primitive count.

翻译：近年来，高斯抛雪球法已成为表示三维场景的稳健技术，能够实现实时栅格化和高保真渲染。然而，高斯函数固有的径向对称性和平滑性约束限制了其表示复杂形状的能力，通常需要数千个基元来近似精细几何结构。我们提出了可变形径向核（DRK），它将高斯抛雪球法扩展为一个更通用、更灵活的框架。通过具有可调角度和尺度的可学习径向基，DRK能够高效建模多样化的形状基元，同时实现对边缘锐度和边界曲率的精确控制。鉴于DRK的平面特性，我们进一步开发了用于深度排序的精确光线-基元相交计算，并引入了高效的核剔除策略以提升栅格化效率。大量实验表明，DRK在表示效率和渲染质量方面均优于现有方法，在显著减少基元数量的同时实现了最先进的性能。