Neural Networks Meet Light Transport Physics for Passive Non-Line-of-Sight Imaging Enhancement

Passive non-line-of-sight (NLOS) imaging has garnered significant interest as a promising technique for cost-effective “corner-turning sensing”. Existing methods, however, face fundamental limitations: data-driven models generally suffer from limited generalization and interpretability, while physics-based approaches typically produce low-fidelity reconstructions. To address these challenges, this paper proposes a hybrid physics-data-driven imaging (HPDI) framework. HPDI employs a dual-path architecture that integrates a physics-informed coarse-to-fine pathway (CTFP) with a data-driven implicit reconstruction pathway (IRP). The former executes a staged progression from physics-informed coarse reconstruction to final refinement, while the latter encodes scene-specific statistical priors in an end-to-end manner. An adaptive fusion network synergistically integrates the physical insights of CTFP and the statistical abstractions of IRP, yielding enhanced reconstruction performance. To enable comprehensive evaluation, we construct multiple datasets covering diverse acquisition conditions (with/without occluder) and representative scenarios (sparse/complex). Experimental results demonstrate that HPDI consistently outperforms state-of-the-art methods in reconstruction fidelity, while exhibiting enhanced generalization under distribution shifts and higher data efficiency. This work represents a significant step toward harnessing the complementary strengths of physics-informed modeling and data-driven learning, thereby advancing the development of high-performance NLOS imaging. The code and dataset are publicly available athttps://github.com/RL-NLOS/HPDI_for_Passive_NLOS.