Target disambiguation is a common problem in gaze interfaces, as eye tracking has accuracy and precision limitations. In 3D environments this is compounded by objects overlapping in the field of view, as a result of their positioning at different depth with partial occlusion. We introduce VOR depth estimation, a method based on the Vestibulo-ocular reflex of the eyes in compensation of head movement, and explore its application to resolve target ambiguity. The method estimates gaze depth by comparing the rotations of the eye and the head when the users look at a target and deliberately rotate their head. We show that VOR eye movement presents an alternative to vergence for gaze depth estimation, that is feasible also with monocular tracking. In an evaluation of its use for target disambiguation, our method outperforms vergence for targets presented at greater depth.

Diako Mardanbegi, Tobias Langlotz, and Hans Gellersen. 2019. Resolving Target Ambiguity in 3D Gaze Interaction through VOR Depth Estimation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 612, 1–12.