A Physical Measurement Benchmark for Passive Non-Line-of-Sight Imaging under Indoor Illumination and Environmental Reflections

Recovering hidden images from indirect light is a challenging problem in physical imaging. In illuminated indoor environments, target-dependent signals are mixed with ambient illumination and reflections from walls, furniture, and other surfaces, making it difficult to determine whether generative models preserve target-specific information or merely produce visually plausible outputs. We introduce a physical benchmark for passive non-line-of-sight image reconstruction under indoor illumination and environmental reflections. The benchmark contains 13,000 physically acquired measurement–target pairs spanning ten STL-10 categories, together with background references, fixed split manifests, and provenance information. The target content consists of 2D STL-10 images projected onto a hidden screen, while the physical contribution lies in the paired measurement acquisition and its evaluation protocol. We support deterministic measurement-to-image reconstruction and coarse-conditioned latent-diffusion refinement, and separately evaluate measurement-only inputs and additional class-conditioned settings. The benchmark provides unified pixel, structural, perceptual, and semantic metrics, together with matched evaluations of deterministic reconstruction, a pretrained latent diffusion model, and a full-UNet fine-tuned variant. Across the matched diagnostic comparisons, domain fine-tuning produces a pronounced perceptual shift at high denoising strength, substantially reducing LPIPS without improving pixel or structural metrics. These results reveal a consistent gap between perceptual plausibility and target-specific reconstruction fidelity rather than a uniform improvement across metrics.