  In this paper, we discuss an imitation learning based method for reducing the
calibration error for a mixed reality system consisting of a vision sensor and
a projector. Unlike a head mounted display, in this setup, augmented
information is available to a human subject via the projection of a scene into
the real world. Inherently, the camera and projector need to be calibrated as a
stereo setup to project accurate information in 3D space. Previous calibration
processes require multiple recording and parameter tuning steps to achieve the
desired calibration, which is usually time consuming process. In order to avoid
such tedious calibration, we train a CNN model to iteratively correct the
extrinsic offset given a QR code and a projected pattern. We discuss the
overall system setup, data collection for training, and results of the
auto-correction model.
