  Time of flight based Non-line-of-sight (NLOS) imaging approaches require
precise calibration of illumination and detector positions on the visible scene
to produce reasonable results. If this calibration error is sufficiently high,
reconstruction can fail entirely without any indication to the user. In this
work, we highlight the necessity of building autocalibration into NLOS
reconstruction in order to handle mis-calibration. We propose a forward model
of NLOS measurements that is differentiable with respect to both, the hidden
scene albedo, and virtual illumination and detector positions. With only a mean
squared error loss and no regularization, our model enables joint
reconstruction and recovery of calibration parameters by minimizing the
measurement residual using gradient descent. We demonstrate our method is able
to produce robust reconstructions using simulated and real data where the
calibration error applied causes other state of the art algorithms to fail.
