面向无线信道预测的几何代数引导 3DGS 框架
A Geometric Algebra-Informed 3DGS Framework for Wireless Channel Prediction
把 3DGS 从纯渲染表示扩展为可承载物理场（电磁）的显式场景表示，并用几何代数注意力建模射线与物体的交互；对视觉研究者而言，这是一条把可微高斯表示用于非渲染物理量预测（如信号强度场、遮挡衰减）的可复用思路。
原文依据 couples 3D Gaussian splatting with a geometric algebra-based attention mechanism to explicitly model ray-object interactions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出几何代数引导的三维高斯泼溅（GAI-GS）框架，用于无线建模，将三维高斯泼溅与基于几何代数的注意力机制相耦合，以显式建模复杂传播环境中的射线—物体交互。GAI-GS 将联合的空间—电磁（EM）关系编码为词元表示，从而能够在统一的端到端神经网络架构内实现场景级聚合。该设计将无线射线传播建立在电磁学原理之上，使词元间的交互能够建模多径、衰减以及反射/衍射等关键效应。在多个真实室内数据集上的大量评估表明，GAI-GS 在多种无线任务上均持续优于现有基线。
英文原文摘要 792 字符
In this paper, we introduce Geometric Algebra-Informed 3D Gaussian Splatting (GAI-GS), a framework for wireless modeling that couples 3D Gaussian splatting with a geometric algebra-based attention mechanism to explicitly model ray-object interactions in complex propagation environments. GAI-GS encodes joint spatial-electromagnetic (EM) relations into token representations, enabling scene-level aggregation within a unified, end-to-end neural architecture. This design grounds wireless ray propagation in electromagnetic principles, allowing token interactions to model key effects such as multipath, attenuation, and reflection/diffraction. Through extensive evaluations on multiple real-world indoor datasets, GAI-GS consistently surpasses current baselines across various wireless tasks.