  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 rectangular 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 with no 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 albedo and surface normal of the hidden objects with
fine details under the most general relay setting. 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 new framework substantially enhances
the applicability of NLOS imaging.
