Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| author | Vaara, Niklas Huynh, Lam Sangi, Pekka López, Miguel Bordallo Heikkilä, Janne |
| author_facet | Vaara, Niklas Huynh, Lam Sangi, Pekka López, Miguel Bordallo Heikkilä, Janne |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07781 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis Vaara, Niklas Huynh, Lam Sangi, Pekka López, Miguel Bordallo Heikkilä, Janne Computer Vision and Pattern Recognition 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. |
| title | Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.07781 |