  The recent years have given rise to a large number of techniques for "looking
around corners", i.e., for reconstructing occluded objects from time-resolved
measurements of indirect light reflections off a wall. While the direct view of
cameras is routinely calibrated in computer vision applications, the
calibration of non-line-of-sight setups has so far relied on manual measurement
of the most important dimensions (device positions, wall position and
orientation, etc.). In this paper, we propose a semi-automatic method for
calibrating such systems that relies on mirrors as known targets. A roughly
determined initialization is refined in order to optimize a spatio-temporal
consistency. Our system is general enough to be applicable to a variety of
sensing scenarios ranging from single sources/detectors via scanning
arrangements to large-scale arrays. It is robust towards bad initialization and
the achieved accuracy is proportional to the depth resolution of the camera
system. We demonstrate this capability with a real-world setup and despite a
large number of dead pixels and very low temporal resolution achieve a result
that outperforms a manual calibration.
