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

eess.IVOct 16, 2026

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

Authors: Lianfang Wang, Xueying Liu, Junnan Dong, Qinghua Zhang, Jun Liu, Yong Wang, Yuping Duan

Organizations: School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China · School of Physics, Nankai University, Tianjin 300071, China

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.

Figures & tables

Figure 1: Overview of the proposed framework. (a) Confocal NLOS imaging configuration. (b) Conventional LCT reconstruction with a fixed analytical Fourier response $\widetilde{H}$ and scalar regularization. (c) MALCT reconstruction with a measurement-conditioned effective response $\widetilde{H}_{\Theta}(\mathbf{k};\tau)$ and regularization map $\alpha_{\Theta}(\mathbf{k};\tau)$ . The effective response combines the analytical response with a shared learned response through measurement-conditioned weighting.

Figure 2: The architecture of our proposed MALCT framework.

Figure 3: The spatio-temporal feature extraction module consists of a 3D UNet, temporal global feature extraction, spatial local feature extraction and fusion module.

Figure 4: Results for the USTC letter and Stanford Statue scenes, with the Statue acquired over 10 min. (a) Central slices of the jointly normalized inverse kernels learned from the USTC letter scene. (b) $64\times 64$ albedo reconstructions using LCT with $\alpha_{\mathrm{High}}=10$ or $\alpha_{\mathrm{Low}}=0.1$ and MALCT with $\alpha_{\mathrm{Learned}}$ .

Figure 5: Evaluation under prescribed Gaussian IRF broadening. (a)1D inverse kernel profiles ( $\kappa=1$ ) for LCT and MALCT. (b) Intensity reconstructions with PSNR/SSIM values. (c) Resampled STFE transients and corresponding reconstruction-projections. (d) Absolute $t$ - $x$ residuals and relative errors.

Method	SNR = 10 dB				SNR = 8 dB				SNR = 5 dB
	Intensity		Depth		Intensity		Depth		Intensity		Depth
	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	MAD $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	MAD $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	RMSE $\downarrow$	MAD $\downarrow$
LCT	21.672	0.480	0.215	0.168	21.569	0.480	0.215	0.168	21.267	0.471	0.215	0.168
FK	26.452	0.677	0.112	0.059	26.414	0.726	0.112	0.059	26.268	0.617	0.111	0.059
PF	26.765	0.367	0.112	0.027	25.998	0.279	0.112	0.027	25.689	0.341	0.133	0.064
LFE	27.052	0.792	0.066	0.021	26.235	0.787	0.065	0.021	26.176	0.789	0.065	0.021

Table 1: Quantitative comparison under different noise levels. PSNR and SSIM are computed for reconstructed intensity images, while RMSE and MAD are computed for depth maps. The best and second-best results are shown in bold and underline , respectively.

Figure 6: Qualitative comparison of the reconstructed intensity images and the depth error maps, where the depth errors are visualized in the range $[0,0.3]$ , within the ROI on the simulated dataset.

Metric	LCT	FK	PF	LFE	NLOST	LPP	MALCT
Time (s) $\downarrow$	0.05	0.06	0.09	0.04	0.42	0.25	0.12
Memory (GB) $\downarrow$	8.2	9.7	11.6	3.3	21.4	9.1	5.5

Table 2: Computational efficiency comparison in terms of inference time and GPU memory usage. The best and second-best results are shown in bold and underline , respectively.

Figure 7: Reconstructions from the Stanford dataset under 180-minute and 10-minute exposures.

Figure 8: Reconstructed hidden scenes and corresponding 2D albedo images from the USTC and Stanford 10-minute real-world datasets.

Figure 9: Reconstructed 2D albedo images from the Stanford 180-minute and USTC datasets under uniform spatial subsampling. Measurements are resized to $128\times 128$ by nearest-neighbor interpolation and processed by the same model without retraining.

Panel of Figure 10 — Figure 10: Ablation study on Dragon and Bike. (a) Configurations. All variants retain the 3D convolutional backbone; NoAtt removes G-TSA and L-SSA. Variants I-IV use fixed scalar regularization, V a globally learned scalar, and VI a measurement-conditioned frequency map. (b) Reconstructions for Variants I-VI and the model without $\mathcal{L}_{\mathrm{TV}}$ or $\mathcal{L}_{\mathrm{Tik}}$ from $32\times 32$ measurements. PSNR and SSIM are computed against the PF reference obtained from the original $512\times 128\times 128$ , 180-min measurements.

Image Reconstruction

Image reconstruction recovers images, volumes, or scenes from incomplete, noisy, indirect, or compressed measurements using physical models and learned priors.

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Time-of-Flight non-line-of-sight (ToF NLOS) imaging techniques provide state-of-the-art reconstructions of scenes hidden around corners by inverting the optical path of indirect photons scattered by visible surfaces and measured by picosecond resolution sensors. The emergence of a wide range of ToF NLOS imaging methods with heterogeneous formulae and hardware implementations obscures the assessment of both their theoretical and experimental aspects. We present a comprehensive study of a representative set of ToF NLOS imaging methods by discussing their similarities and differences under common formulation and hardware. We first outline the problem statement under a common general forward model for ToF NLOS measurements, and the typical assumptions that yield tractable inverse models. We discuss the relationship of the resulting simplified forward and inverse models to a family of Radon transforms, and how migrating these to the frequency domain relates to recent phasor-based virtual line-of-sight imaging models for NLOS imaging that obey the constraints of conventional lens-based imaging systems. We then evaluate performance of the selected methods on hidden scenes captured under the same hardware setup and similar photon counts. Our experiments show that existing methods share similar limitations on spatial resolution, visibility, and sensitivity to noise when operating under equal hardware constraints, with particular differences that stem from method-specific parameters. We expect our methodology to become a reference in future research on ToF NLOS imaging to obtain objective comparisons of existing and new methods.

Abstract

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