Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach
<|reference_start|>Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach: 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.<|reference_end|>
@article{hosp2024focal,
    title={Focal Depth Estimation: A Calibration-Free, Subject- and Daytime
  Invariant Approach},
    author={Benedikt W. Hosp, Bj"orn Severitt, Rajat Agarwala, Evgenia Rusak,
  Yannick Sauer, Siegfried Wahl},
    journal={arXiv preprint arXiv:2408.03591},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03591},
    primaryClass={cs.CV cs.AI cs.HC cs.LG eess.SP}
}