NLOSdiffuser: Generalized Steady-State Non-Line-of-sight Imaging toward Indoor Scenarios

Xian Gao, Luyang Wang, Jiacheng Ruan, Yuyang Zhang, Zongyun Zhang, Ting Liu, Yuzhuo Fu

Abstract: Non-Line-of-sight (NLOS) imaging broadens the field of view to observe objects beyond direct sightlines, attracting considerable research interest. However, previous studies often overlook real-world environmental impacts, focusing on idealized scenarios, which limits their practical applicability. We introduce a steady-state NLOS imaging method tailored for indoor environments, showcasing robust generalization across varied settings despite training on static scenes. Our method employs a synthetic indoor dataset mimicking real-world conditions for training and diverse scenarios for evaluation. A two-stage network initially conducts pixel-level coarse reconstruction to convert diffuse reflection images into intermediate feature maps, followed by semantic-level reconstruction using a pre-trained diffusion model, yielding highly recognizable images. Comprehensive experiments demonstrate superior reconstruction accuracy, image quality, and adaptability compared to conventional methods. Verified in real-world scenarios, our method effectively images various concealed objects, marking significant progress in NLOS imaging.

External IDs:dblp:conf/icmcs/GaoWRZZLF25