Non-line-of-sight imaging with arbitrary illumination and detection pattern

Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at regular grid points over a large relay surface, limiting applicability in practical scenarios (robotic vision, autonomous driving, rescue, remote sensing). This work proposes a Bayesian framework for NLOS imaging without constraints on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we develop a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm enabling high-quality reconstructions. The approach reconstructs both albedo and surface normals with fine details under general relay settings. With a regular relay surface, coarse rather than dense measurements suffice, significantly reducing acquisition time. Experiments demonstrate that this framework substantially extends the application range of NLOS imaging.

Xintong Liu, Jianyu Wang, Leping Xiao, Zuoqiang Shi, Xing Fu, Lingyun Qiu