GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
GeRaF is the first method to use neural implicit learning for near-range 3D geometry reconstruction from radio frequency (RF) signals. Unlike RGB or LiDAR-based methods, RF sensing can see through occlusion but suffers from low resolution and noise due to its lensless imaging nature. While lenses in RGB imaging constrain sampling to 1D rays, RF signals propagate through the entire space, introducing significant noise and leading to cubic complexity in volumetric rendering. Moreover, RF signals interact with surfaces via specular reflections, requiring fundamentally different modeling. To address these challenges, GeRaF (1) introduces filter-based rendering to suppress irrelevant signals, ...
| Authors | Jiachen Lu, Hailan Shanbhag, Haitham Al Hassanieh |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 4 August 2026 |
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