  The non-line-of-sight imaging technique aims to reconstruct targets from
multiply reflected light. For most existing methods, dense points on the relay
surface are raster scanned to obtain high-quality reconstructions, which
requires a long acquisition time. In this work, we propose a signal-surface
collaborative regularization (SSCR) framework that provides noise-robust
reconstructions with a minimal number of measurements. Using Bayesian
inference, we design joint regularizations of the estimated signal, the 3D
voxel-based representation of the objects, and the 2D surface-based description
of the targets. To our best knowledge, this is the first work that combines
regularizations in mixed dimensions for hidden targets. Experiments on
synthetic and experimental datasets illustrated the efficiency and robustness
of the proposed method under both confocal and non-confocal settings. We report
the reconstruction of the hidden targets with complex geometric structures with
only $5 \times 5$ confocal measurements from public datasets, indicating an
acceleration of the conventional measurement process by a factor of 10000.
Besides, the proposed method enjoys low time and memory complexities with
sparse measurements. Our approach has great potential in real-time
non-line-of-sight imaging applications such as rescue operations and autonomous
driving.
