用神经隐式方法从射频信号重建毫米级3D结构，突破穿墙成像的分辨率瓶颈。
GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
- 通过滤波渲染抑制无关信号，解决射频噪声问题
- 构建物理驱动的射频体渲染流程，实现全空间采样
- 适合做无线感知、智能安防等场景下的高精度环境重建
GeRaF 是首个利用神经隐式学习实现近距离射频（RF）信号三维几何重建的方法。与基于RGB或激光雷达的方法不同，射频传感可穿透遮挡物，但因无透镜成像而面临分辨率低、噪声大等问题。由于射频信号在全空间传播，而非像光学成像那样沿1维射线采样，导致体渲染复杂度呈立方增长。此外，射频信号与表面以镜面反射方式交互，需采用根本不同的建模方式。为应对这些挑战，GeRaF 提出：（1）基于滤波的渲染机制以抑制无关信号；（2）实现物理驱动的射频体渲染管道；（3）设计新型无透镜采样与无透镜透明度融合策略，使训练期间全空间采样成为可能。通过在多层感知机中学习符号距离函数、反射率和信号强度，GeRaF 在真实环境中首次实现了毫米级几何重建的可行性。
原文摘要 · Abstract (English)
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.
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