Inatypical activeconfocal NLOS imaging system, as depicted in Figure 1, alaser source and adetector are both focused on the same point onarelay surface.

Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing.Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a large relay surface, which limits their applicability in realistic scenarios with irregular relay surfaces.In this paper, we propose an unsupervised learning-based framework for NLOS imaging from irregularly undersampled transients~(IUT).Our method learns implicit priors from noisy irregularly undersampled transients without requiring paired data, which is difficult and expensive to acquire and align. To overcome the ambiguity of the measurement consistency constraint in inferring the albedo volume, we design a virtual scanning process that enables the network to learn within both range and null spaces for high-quality reconstruction.We devise a physics-guided SURE-based denoiser to enhance robustness to ubiquitous noise in low-photon imaging conditions.

The detector captures photons bouncing back from the scene toward the relay surface, referred to as transients, from which the hidden scene can be recovered using elaborately designed algorithms.

Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing.Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a large relay surface, which limits their applicability in realistic scenarios with irregular relay surfaces.In this paper, we propose an unsupervised learning-based framework for NLOS imaging from irregularly undersampled transients (IUT).Our method learns implicit priors from noisy irregularly undersampled transients without requiring paired data, which is difficult and expensive to acquire and align. To overcome the ambiguity of the measurement consistency constraint in inferring the albedo volume, we design a virtual scanning process that enables the network to learn within both range and null spaces for high-quality reconstruction.We devise a physics-guided SURE-based denoiser to enhance robustness to ubiquitous noise in low-photon imaging conditions.