Radiance fields have emerged as a predominant representation for modeling 3D scene appearance. Neural formulations such as Neural Radiance Fields provide high expressivity but require costly ray marching for rendering, whereas primitive-based methods such as 3D Gaussian Splatting offer real-time efficiency through splatting, yet at the expense of representational power. Inspired by advances in both these directions, we introduce splattable neural primitives, a new volumetric representation that reconciles the expressivity of neural models with the efficiency of primitive-based splatting. Each primitive encodes a bounded neural density field parameterized by a shallow neural network. Our formulation admits an exact analytical solution for line integrals, enabling efficient computation of perspectively accurate splatting kernels. As a result, our representation supports integration along view rays without the need for costly ray marching. The primitives flexibly adapt to scene geometry and, being larger than prior analytic primitives, reduce the number required per scene. On novel-view synthesis benchmarks, our approach matches the quality and speed of 3D Gaussian Splatting while using $10\times$ fewer primitives and $6\times$ fewer parameters. These advantages arise directly from the representation itself, without reliance on complex control or adaptation frameworks. The project page is https://vcai.mpi-inf.mpg.de/projects/SplatNet/.

翻译：辐射场已成为建模三维场景外观的主流表示方法。诸如神经辐射场（NeRF）等神经化方法具有高表达能力，但渲染时需要代价高昂的光线步进；而基于基元的方法（如3D高斯溅射）通过溅射实现了实时效率，但以牺牲表示能力为代价。受这两方面进展的启发，我们引入了可溅射神经基元——一种新的体表示方法，它融合了神经模型的高表达能力与基于基元的溅射效率。每个基元编码一个由浅层神经网络参数化的有界神经密度场。我们的公式为线积分提供了精确的解析解，从而能够高效计算透视精确的溅射核。因此，我们的表示支持沿视线方向进行积分，而无需昂贵的光线步进。这些基元能灵活适应场景几何结构，并且由于比先前的解析基元更大，减少了每个场景所需的基元数量。在新视角合成基准测试中，我们的方法在匹配3D高斯溅射的质量和速度的同时，使用的基元数量减少了10倍，参数数量减少了6倍。这些优势直接源于表示本身，无需依赖复杂的控制或适配框架。项目页面为 https://vcai.mpi-inf.mpg.de/projects/SplatNet/。