  Vehicles, search and rescue personnel, and endoscopes use flash lights to
locate, identify, and view objects in their surroundings. Here we show the
first steps of how all these tasks can be done around corners with consumer
cameras. Recent techniques for NLOS imaging using consumer cameras have not
been able to both localize and identify the hidden object. We introduce a
method that couples traditional geometric understanding and data-driven
techniques. To avoid the limitation of large dataset gathering, we train the
data-driven models on rendered samples to computationally recover the hidden
scene on real data. The method has three independent operating modes: 1) a
regression output to localize a hidden object in 2D, 2) an identification
output to identify the object type or pose, and 3) a generative network to
reconstruct the hidden scene from a new viewpoint. The method is able to
localize 12cm wide hidden objects in 2D with 1.7cm accuracy. The method also
identifies the hidden object class with 87.7% accuracy (compared to 33.3%
random accuracy). This paper also provides an analysis on the distribution of
information that encodes the occluded object in the accessible scene. We show
that, unlike previously thought, the area that extends beyond the corner is
essential for accurate object localization and identification.
