{"slug": "geraf-neural-geometry-reconstruction-from-radio-frequency-signals", "title": "GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals", "summary": "Researchers have developed GeRaF, the first neural implicit learning method for reconstructing 3D geometry from radio frequency signals, enabling see-through-occlusion sensing without relying on RGB or LiDAR. The system addresses RF imaging's inherent low resolution and noise by introducing filter-based rendering, a physics-based volumetric pipeline, and lensless sampling strategies. This breakthrough achieves millimeter-level geometry reconstruction in real-world settings, potentially transforming applications in autonomous navigation, security, and industrial inspection where visual sensors fail.", "body_md": "arXiv:2605.29097v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/geraf-neural-geometry-reconstruction-from-radio-frequency-signals", "canonical_source": "https://arxiv.org/abs/2605.29097", "published_at": "2026-05-29 04:00:00+00:00", "updated_at": "2026-05-29 04:16:04.826727+00:00", "lang": "en", "topics": ["neural-networks", "computer-vision", "artificial-intelligence", "machine-learning", "ai-research"], "entities": ["GeRaF"], "alternates": {"html": "https://wpnews.pro/news/geraf-neural-geometry-reconstruction-from-radio-frequency-signals", "markdown": "https://wpnews.pro/news/geraf-neural-geometry-reconstruction-from-radio-frequency-signals.md", "text": "https://wpnews.pro/news/geraf-neural-geometry-reconstruction-from-radio-frequency-signals.txt", "jsonld": "https://wpnews.pro/news/geraf-neural-geometry-reconstruction-from-radio-frequency-signals.jsonld"}}