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This paper introduces the Measurement-Adaptive Light-Cone Transform (MALCT), a physics-guided framework for robust non-line-of-sight (NLOS) imaging that addresses noise and idealized light transport deviations. MALCT integrates an analytical response with a learned response via measurement-conditioned weighting and predicts a frequency-dependent regularization map within a variational formulation. Experimental results show improved reconstruction quality and robustness to temporal response broadening and reduced acquisition time, even with low spatial sampling.
A learned, measurement-conditioned regularization map unlocks robust NLOS reconstruction from noisy, non-ideal measurements, outperforming standard LCT.
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.