用雷达信号穿透障碍物，重建隐藏物体的3D形状。
Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals
- 结合可见与不可见区域的几何先验，统一建模信号传播路径。
- 实现稳定训练，恢复出清晰且物理一致的隐藏表面结构。
- 适合做雷达感知、智能机器人环境重建的研究者参考。
从射频（RF）信号重构物体几何结构极具挑战性，因RF感知无透镜成像特性导致空间分辨率低、噪声大。与光信号不同，RF信号可穿透遮挡，从而获取隐藏场景信息。现有非视域（NLoS）3D神经重建方法虽能恢复封闭环境内的粗略表面，但常面临优化不稳定、表面噪声大、表面模糊等问题，难以从有符号距离场（SDF）中准确提取零值等高线。这些局限主要源于忽视了可视域（LoS）外部几何在建模信号传播中的作用。本文提出统一的LoS与NLoS神经几何重建框架GeRaF 2.0，利用外部可见域几何引导信号从可见区传入不可见区。通过将视觉LoS先验融入神经场建模，GeRaF 2.0实现了稳定训练和可见/隐藏几何的物理一致性重建，在基于雷达的几何重建任务上达到新最优性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。