AI RESEARCH
Taming Vision Priors for Data Efficient mmWave Channel Modeling
arXiv CS.CV
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ArXi:2603.13383v1 Announce Type: new Accurately modeling millimeter-wave (mmWave) propagation is essential for real-time AR and autonomous systems. Differentiable ray tracing offers a physics-grounded solution but still facing deployment challenges due to its over-reliance on exhaustive channel measurements or brittle, hand-tuned scene models for material properties. We present VisRFTwin, a scalable and data-efficient digital-twin framework that integrates vision-derived material priors with differentiable ray tracing.