Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals
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 outsi...
| Authors | Jiachen Lu, Hailan Shanbhag, Haitham Al Hassanieh |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 4 August 2026 |
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