让点云渲染更精准，用可变形的核函数替代传统高斯点。
Deformable Radial Kernel Splatting
- 用可学习角度和尺度的径向基函数，突破高斯点的对称性限制。
- 仅用少量基元就实现高质量渲染，显著降低所需点数。
- 适合需要精细几何建模与实时渲染的3D重建场景。
最近，高斯点积（Gaussian splatting）已成为一种稳健的3D场景表示方法，支持实时光栅化与高保真渲染。然而，高斯点固有的径向对称性和平滑性约束限制了其对复杂形状的表达能力，通常需要数千个基元来近似细节几何。本文提出可变形径向核（Deformable Radial Kernel, DRK），将高斯点积拓展为更通用且灵活的框架。通过可学习的径向基函数，支持可调角度与尺度，高效建模多样化的形状基元，并精确控制边缘锐度与边界曲率。鉴于DRK的平面特性，进一步设计了准确的射线-基元相交计算以实现深度排序，并引入高效的核剔除策略提升光栅化效率。大量实验表明，DRK在表示效率与渲染质量上均优于现有方法，达到当前最优性能，同时大幅减少基元数量。
原文摘要 · Abstract (English)
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.
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