  The inaccessibility of user-perceived reality remains an open issue in
pursuing the accurate calibration of optical see-through (OST) head-mounted
displays (HMDs). Manual user alignment is usually required to collect a set of
virtual-to-real correspondences, so that a default or an offline display
calibration can be updated to account for the user's eye position(s). Current
alignment-based calibration procedures usually require point-wise alignments
between rendered image point(s) and associated physical landmark(s) of a target
calibration tool. As each alignment can only provide one or a few
correspondences, repeated alignments are required to ensure calibration
quality.
  This work presents an accurate and tool-less online OST calibration method to
update an offline-calibrated eye-display model. The user's bare hand is
markerlessly tracked by a commercial RGBD camera anchored to the OST headset to
generate a user-specific cursor for correspondence collection. The required
alignment is object-wise, and can provide thousands of unordered corresponding
points in tracked space. The collected correspondences are registered by a
proposed rotation-constrained iterative closest point (rcICP) method to
optimise the viewpoint-related calibration parameters. We implemented such a
method for the Microsoft HoloLens 1. The resiliency of the proposed procedure
to noisy data was evaluated through simulated tests and real experiments
performed with an eye-replacement camera. According to the simulation test, the
rcICP registration is robust against possible user-induced rotational
misalignment. With a single alignment, our method achieves 8.81 arcmin (1.37
mm) positional error and 1.76 degree rotational error by camera-based tests in
the arm-reach distance, and 10.79 arcmin (7.71 pixels) reprojection error by
user tests.
