Measurement-Adaptive Light-Cone Transform for Robust Non-Line-of-Sight Imaging

Lianfang Wang; Xueying Liu; Junnan Dong; Qinghua Zhang; Jun Liu; Yong Wang; Yuping Duan

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arXiv · 2610.22847

Measurement-Adaptive Light-Cone Transform for Robust Non-Line-of-Sight Imaging

Lianfang Wang·Xueying Liu·Junnan Dong·Qinghua Zhang·Jun Liu·Yong Wang·Yuping Duan

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Abstract

Practical non-line-of-sight (NLOS) measurements are affected by noise and deviations from idealized light transport, which can degrade reconstruction with conventional light-cone transform (LCT) methods. We propose the Measurement-Adaptive Light-Cone Transform (MALCT), a physics-guided framework that retains the efficient Fourier-domain inversion structure of LCT. {Within a unified variational formulation, MALCT combines the analytical response with a shared learned response through measurement-conditioned weighting and predicts a measurement-conditioned, frequency-dependent regularization map.} An attention-enhanced spatio-temporal feature extraction module captures long-range temporal dependencies and local spatial correlations to support reconstruction under low-signal conditions. Experiments on synthetic data and real measurements from different NLOS systems demonstrate improved reconstruction quality. Controlled evaluations further assess robustness to temporal-response broadening and reduced acquisition time. MALCT also preserves scene structures with spatial sampling grids down to $32\times32$, supporting its potential for NLOS imaging under limited acquisition budgets.

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Lianfang Wang, Xueying Liu, Junnan Dong, Qinghua Zhang, Jun Liu, Yong Wang, Yuping Duan. 2026-10-16. Measurement-Adaptive Light-Cone Transform for Robust Non-Line-of-Sight Imaging. https://arxiv.org/abs/2610.22847

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