Reprojection-Guided Non-Line-of-Sight Imaging Under Irregular Undersampling

摘要

Non-line-of-sight (NLOS) imaging enables the visualization of scenes outside the direct line of sight, with broad potential applications in robotic vision, autonomous navigation, disaster relief, and medical diagnostics. However, most existing NLOS methods depend on densely sampled transients collected from large and continuous relay surfaces, which restricts their applicability in real-world scenarios where the available relay surfaces are typically sparse, irregular, or fragmented. To overcome the challenges posed by irregular undersampling in NLOS imaging, we propose a reprojection-guided framework designed for robust performance in such scenarios. At its core is a transient recovery network that reconstructs denoised, fully-sampled transients from noisy, undersampled inputs, thereby mitigating the inherent ill-posedness of the problem. To further enhance recovery quality and improve generalization, we develop a range-space reprojection (RSRP) module that extracts intrinsic information from the measurements and generates explicit guidance. This guidance is leveraged by the spatio-temporal modulation (STM) blocks, which adaptively control its influence to enhance transient denoising and recovery. Extensive experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches, showing strong generalization across diverse relay surfaces and marking a substantial step toward practical and robust NLOS imaging.