Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis

• URL: http://arxiv.org/abs/2605.07781v1
• Date: Fri, 08 May 2026 14:19:28 GMT
• Title: Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis
• Abstract summary: We introduce a framework enabling differentiable RF propagation simulation directly within visually reconstructed neural scenes.<n>We provide cross-modal evidence that neural reconstructions can serve as unified spatial representations for both electromagnetic propagation simulation and photorealistic view synthesis.
• Score: 13.057153756597904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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