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.
近年来，高斯点云技术（Gaussian Splatting）作为一种鲁棒的三维场景表示方法迅速发展，实现了实时光栅化和高保真渲染。然而，高斯本身的径向对称性和光滑性限制了其对复杂形状的表示能力，通常需要成千上万个基元才能逼近细节几何。 为解决这一问题，我们提出了 可变形径向核（Deformable Radial Kernel, DRK），将高斯点云扩展为更通用且灵活的框架。通过可学习的径向基函数，DRK 支持角度和尺度的可调节性，能够高效地建模多样的形状基元，同时实现对边缘锐度和边界曲率的精确控制。针对 DRK 的平面特性，我们进一步开发了准确的光线与基元相交计算方法，用于深度排序，并引入了高效的核剔除策略以提高光栅化效率。 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，不仅达到了当前最先进的性能，还显著减少了基元数量，展现了卓越的表示能力和实用价值。