  Conventional intensity cameras recover objects in the direct line-of-sight of
the camera, whereas occluded scene parts are considered lost in this process.
Non-line-of-sight imaging (NLOS) aims at recovering these occluded objects by
analyzing their indirect reflections on visible scene surfaces. Existing NLOS
methods temporally probe the indirect light transport to unmix light paths
based on their travel time, which mandates specialized instrumentation that
suffers from low photon efficiency, high cost, and mechanical scanning. We
depart from temporal probing and demonstrate steady-state NLOS imaging using
conventional intensity sensors and continuous illumination. Instead of assuming
perfectly isotropic scattering, the proposed method exploits directionality in
the hidden surface reflectance, resulting in (small) spatial variation of their
indirect reflections for varying illumination. To tackle the shape-dependence
of these variations, we propose a trainable architecture which learns to map
diffuse indirect reflections to scene reflectance using only synthetic training
data. Relying on consumer color image sensors, with high fill factor, high
quantum efficiency and low read-out noise, we demonstrate high-fidelity color
NLOS imaging for scene configurations tackled before with picosecond time
resolution.
