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 in a large area of the relay surface, which severely hinders their availability to variable relay scenarios in practical applications such as robotic vision, autonomous driving, rescue operations and remote sensing. In this work, we propose a Bayesian framework for NLOS imaging without specific requirements on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we design a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm for high-quality reconstructions. Our approach is capable of reconstructing both the albedo and surface normal of the hidden objects with fine details under general relay settings. Moreover, with a regular relay surface, coarse rather than dense measurements are enough for our approach such that the acquisition time can be reduced significantly. As demonstrated in multiple experiments, the proposed framework substantially extends the application range of NLOS imaging.

非视线 (NLOS) 成像旨在重建被直接视线遮挡的目标。现有的非视距成像算法需要在中继面的大面积规则网格点进行密集测量，这严重阻碍了它们在机器人视觉、自动驾驶、救援行动和遥感等实际应用中对可变中继场景的可用性。在这项工作中，我们提出了一个用于 NLOS 成像的贝叶斯框架，对照明和检测点的空间模式没有特定要求。通过引入虚拟共焦信号，我们设计了一种用于高质量重建的共焦互补信号-对象协同正则化 (CC-SOCR) 算法。我们的方法能够在一般中继设置下重建具有精细细节的隐藏物体的反照率和表面法线。此外，对于规则的中继表面，粗略而不是密集的测量对于我们的方法来说就足够了，这样可以显着减少采集时间。正如多项实验所证明的那样，所提出的框架大大扩展了 NLOS 成像的应用范围。