穿透箱体：从雷达信号进行非视域三维重建
Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals
把可见区域（LoS）的视觉几何先验注入神经场，作为约束来引导射频信号在遮挡区域（NLoS）的传播建模，从而稳定 SDF 优化并消除表面歧义；做遮挡补全、非视域重建或跨模态神经场的人可以借鉴这种「用可见几何约束不可见几何」的思路。
原文依据 By integrating visual LoS priors into the neural field formulation, GeRaF 2.0 achieves stable training and physically consistent reconstruction
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从射频（RF）信号重建物体几何本质上具有挑战性，因为射频感知的无透镜成像特性会导致低空间分辨率和高噪声。与光信号不同，射频信号可以穿透遮挡物，从而捕获隐藏场景的信息。现有的非视域（NLoS）三维神经重建方法能够恢复封闭环境内部的粗糙表面，但常常受困于优化不稳定、表面几何含噪以及表面歧义，无法从符号距离场（SDF）中产生准确的零水平集。这些局限在很大程度上源于忽视了封闭区域之外视域（LoS）几何的作用，而该几何为信号传播建模提供了宝贵的物理约束。本文提出统一的视域与非视域神经几何重建框架 GeRaF 2.0，利用外部视域几何来建模并引导射频信号从视域区域向非视域区域传播。通过将视觉视域先验融入神经场表述，GeRaF 2.0 实现了稳定的训练以及对可见与隐藏几何的物理一致重建，在基于射频的几何重建上取得了新的最优性能。
英文原文摘要 1265 字符
Reconstructing object geometry from radio frequency (RF) signals is fundamentally challenging due to the lensless imaging nature of RF sensing, which leads to low spatial resolution and high noise. Unlike light signals, RF signals can penetrate occlusions and thus capture information about hidden scenes. Existing Non-Line-of-Sight (NLoS) 3D neural reconstruction methods can recover coarse surfaces inside enclosed environments but often suffer from unstable optimization, noisy surface geometry, and surface ambiguity, failing to produce accurate zero-level sets from the signed distance field (SDF). These limitations largely stem from neglecting the role of Line-of-Sight (LoS) geometry outside the enclosed region, which provides valuable physical constraints for modeling signal propagation. In this paper, we introduce a Unified LoS and NLoS neural geometry reconstruction framework GeRaF 2.0 that leverages the outside LoS geometry to model and guide RF propagation from the LoS region into the NLoS region. By integrating visual LoS priors into the neural field formulation, GeRaF 2.0 achieves stable training and physically consistent reconstruction of both visible and hidden geometry, setting a new state-of-the-art in RF-based geometry reconstruction.