  Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce
indirect reflections from the hidden objects. Active NLOS imaging systems rely
on the capture of the time of flight of light through the scene, and have shown
great promise for the accurate and robust reconstruction of hidden scenes
without the need for specialized scene setups and prior assumptions. Despite
that existing methods can reconstruct 3D geometries of the hidden scene with
excellent depth resolution, accurately recovering object textures and
appearance with high lateral resolution remains an challenging problem. In this
work, we propose a new problem formulation, called NLOS photography, to
specifically address this deficiency. Rather than performing an intermediate
estimate of the 3D scene geometry, our method follows a data-driven approach
and directly reconstructs 2D images of a NLOS scene that closely resemble the
pictures taken with a conventional camera from the location of the relay wall.
This formulation largely simplifies the challenging reconstruction problem by
bypassing the explicit modeling of 3D geometry, and enables the learning of a
deep model with a relatively small training dataset. The results are NLOS
reconstructions of unprecedented lateral resolution and image quality.
