  In an era where personalized technology is increasingly intertwined with
daily life, traditional eye-tracking systems and autofocal glasses face a
significant challenge: the need for frequent, user-specific calibration, which
impedes their practicality. This study introduces a groundbreaking
calibration-free method for estimating focal depth, leveraging machine learning
techniques to analyze eye movement features within short sequences. Our
approach, distinguished by its innovative use of LSTM networks and
domain-specific feature engineering, achieves a mean absolute error (MAE) of
less than 10 cm, setting a new focal depth estimation accuracy standard. This
advancement promises to enhance the usability of autofocal glasses and pave the
way for their seamless integration into extended reality environments, marking
a significant leap forward in personalized visual technology.
