  In addition to the high cost and complex setup, the main reason for the
limitation of the three-dimensional (3D) display is the problem of accurately
estimating the user's current point-of-gaze (PoG) in a 3D space. In this paper,
we present a novel noncontact technique for the PoG estimation in a
stereoscopic environment, which integrates a 3D stereoscopic display system and
an eye-tracking system. The 3D stereoscopic display system can provide users
with a friendly and immersive high-definition viewing experience without
wearing any equipment. To accurately locate the user's 3D PoG in the field of
view, we build a regression-based 3D eye-tracking model with the eye movement
data and stereo stimulus videos as input. Besides, to train an optimal
regression model, we also design and annotate a dataset that contains 30 users'
eye-tracking data corresponding to two designed stereo test scenes.
Innovatively, this dataset introduces feature vectors between eye region
landmarks for the gaze vector estimation and a combined feature set for the
gaze depth estimation. Moreover, five traditional regression models are trained
and evaluated based on this dataset. Experimental results show that the average
errors of the 3D PoG are about 0.90~cm on the X-axis, 0.83~cm on the Y-axis,
and 1.48~cm$/$0.12~m along the Z-axis with the scene-depth range in
75~cm$/$8~m, respectively.
