用神经高斯表示统一建模无线传播与视觉渲染，实现可微射线追踪。
Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis
- 将高斯原语嵌入可微射线追踪结构，实现视觉重建场景中的无线信号传播模拟。
- 无需手动建模网格，直接从视觉重建中提取物理意义明确的信道冲激响应。
- 适合需要跨模态空间建模的无线数字孪生、元宇宙等应用研究者。
显式神经表示如3D高斯溅射（3DGS）可实现高保真、实时的新视角合成，但优化目标为α混合的光学外观而非可射线相交的几何结构。相比之下，射频（RF）数字孪生需确定性多路径传播，几何决定轨迹及其衰减和时延。本文提出框架，在视觉重建的神经场景中直接实现可微射频传播模拟，支持任意三维位置间的点对点路径计算，同时保持高质量视觉渲染。不同于依赖人工构建网格的传统射频模拟流程，我们将在硬件加速的射线追踪结构中嵌入高斯原语作为基础空间表示。通过从仅视觉重建中提取具有物理意义的信道冲激响应，提供跨模态证据：神经重建可作为电磁传播模拟与照片级视图合成的统一空间表征。
原文摘要 · Abstract (English)
Explicit neural representations such as 3D Gaussian Splatting (3DGS) enable high-fidelity and real-time novel view synthesis, yet optimize for alpha-composited optical appearance rather than ray-intersectable geometry. In contrast, radio-frequency (RF) digital twins require deterministic multi-bounce paths, where the geometry dictates trajectories and their associated attenuation and delay. We introduce a framework enabling differentiable RF propagation simulation directly within visually reconstructed neural scenes, allowing point-to-point path computation between arbitrary 3D locations while preserving high-quality visual rendering. Unlike conventional RF simulation pipelines that rely on manually constructed meshes, we embed Gaussian primitives into a hardware-accelerated ray tracing structure as the underlying spatial representation. By extracting physically meaningful channel impulse responses from visual-only reconstructions, we provide cross-modal evidence that neural reconstructions can serve as unified spatial representations for both electromagnetic propagation simulation and photorealistic view synthesis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。