NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data
Matteo Caligiuri Adriano Simonetto Gianluca Agresti Pietro Zanuttigh
The acquisition of objects outside the Line-of-Sight of cameras is a very
intriguing but also extremely challenging research topic. Recent works showed
the feasibility of this idea exploiting transient imaging data produced by
custom direct Time of Flight sensors. In this paper, for the first time, we
tackle this problem using only data from an off-the-shelf indirect Time of
Flight sensor without any further hardware requirement. We introduced a Deep
Learning model able to re-frame the surfaces where light bounces happen as a
virtual mirror. This modeling makes the task easier to handle and also
facilitates the construction of annotated training data. From the obtained data
it is possible to retrieve the depth information of the hidden scene. We also
provide a first-in-its-kind synthetic dataset for the task and demonstrate the
feasibility of the proposed idea over it.