[2605.29097] GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
[Submitted on 27 May 2026]
Title:GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
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Abstract: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, (2) implements a physics-based RF volumetric rendering pipeline, and (3) proposes a novel lensless sampling and lensless alpha blending strategy that makes full-space sampling feasible during training. By learning signed distance functions, reflectiveness, and signal power through MLPs and trainable parameters, GeRaF takes the first step towards reconstructing millimeter-level geometry from RF signals in real-world settings.
Comments: Accepted at NeurIPS 2025 (Spotlight)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.29097 [cs.CV]
(or arXiv:2605.29097v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.29097
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Advances in Neural Information Processing Systems 38 (2026): 94200-94230
Submission history
From: Jiachen Lu [view email] [v1] Wed, 27 May 2026 20:58:36 UTC (19,647 KB)
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