Weicai Long, Yusen Hou, Junning Feng, Houcheng su, Shuo Yang, Donglin Xie, Yanlin Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Non-line-of-sight (NLOS) imaging aims to reconstruct scenes hidden from direct view and has broad applications in robotic vision, rescue operations, autonomous driving, and remote sensing. However, most existing methods rely on densely sampled transients from large, continuous relay surfaces, which limits their practicality in real-world scenarios with aperture constraints. To address this limitation, we propose an unsupervised zero-shot framework tailored for confocal NLOS imaging with aperture-limited relay surfaces. Our method leverages latent diffusion models to recover fully-sampled transients from undersampled versions by enforcing measurement consistency during the sampling process. To further improve recovered transient quality, we introduce a progressive recovery strategy that incrementally recovers missing transient values, effectively mitigating the impact of severe aperture limitations. In addition, to suppress error propagation during recovery, we develop a backpropagation-based error correction reconstruction algorithm that refines intermediate recovered transients by enforcing sparsity regularization in the voxel domain, enabling high-fidelity final reconstructions. Extensive experiments on both simulated and real-world datasets validate the robustness and generalization capability of our method across diverse aperture-limited relay surfaces. Notably, our method follows a zero-shot paradigm, requiring only a single pretraining stage without paired data or pattern-specific retraining, which makes it a more practical and generalizable framework for NLOS imaging.

Depth sensing is essential for intelligent computer vision applications, but it often suffers from low range precision and spatial resolution. To address this problem, we propose a novel framework that combines non-uniform sampling and reconstruction based on graph theory. Our framework consists of two main components: (1) a graph Laplacian induced non-uniform sampling (GLINUS) scheme that samples depth signals more densely around edges and contours than in smooth regions, and (2) an ensemble of priors (EoP) model that reconstructs the high-quality depth map using adaptive dual-tree discrete wavelet packets (ADDWP) transform, graph total variation regularizer, and graph Laplacian regularizer with color guidance. We solve the reconstruction problem using the alternating direction method of multipliers (ADMM). Our experiments demonstrate that our framework can capture fine structures and global information in depth signals and produce superior depth reconstruction results.

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. Extensive experiments on both simulated and real-world data validate the performance and generalization of our method. Compared with the state-of-the-art (SOTA) method, our method achieves higher fidelity, greater robustness, and remarkably faster inference times by orders of magnitude. The code and model are available at https://github.com/XingyuCuii/Virtual-Scanning-NLOS.