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Updated: Feb 10, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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3DeepVOG: An Open-Source Framework for Real-Time, Accurate 3D Gaze Tracking with Deep Learning.

Jingkang Zhao1,2, Seyed-Ahmad Ahmadi3, Julian Decker1,4

  • 1German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, Ludwig Maximilian University Munich, Munich, Germany.

Digital Biomarkers
|February 9, 2026
PubMed
Summary
This summary is machine-generated.

3DeepVOG, a novel deep learning framework, enables accurate 3D eye movement tracking for neurological disorders. This open-source tool overcomes limitations of traditional systems, offering a scalable solution for research and clinical use.

Keywords:
Deep learningGaze trackingOpen-source softwareTorsionVideo-oculography

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Area of Science:

  • Neuroscience
  • Ophthalmology
  • Medical Technology

Background:

  • Eye movements are crucial biomarkers for neurological disorders.
  • Traditional video-oculography (VOG) systems have limitations in accuracy, torsional movement tracking, and cost.
  • Existing VOG systems require high-quality imaging and struggle in challenging conditions.

Purpose of the Study:

  • To develop a deep learning framework for accurate 3D eye movement tracking.
  • To overcome the limitations of current VOG systems, including robustness in varied imaging conditions and cost-effectiveness.
  • To enable real-time tracking of horizontal, vertical, and torsional eye movements.

Main Methods:

  • Developed 3DeepVOG, a deep learning framework for 3D monocular gaze tracking.
  • Integrated automated pupil/iris segmentation with a two-sphere eyeball model and corneal refraction correction.
  • Employed a novel mini-patch template matching for real-time torsional tracking.

Main Results:

  • 3DeepVOG achieves real-time performance (>300 fps) with high accuracy (∼0.1° gaze error) in all three dimensions.
  • Oculomotor measures demonstrated good-to-excellent agreement with a gold-standard VOG system.
  • Successfully captured 3D nystagmus in a case of acute unilateral vestibular failure.

Conclusions:

  • 3DeepVOG provides accurate, quantitative 3D eye movement tracking across diverse conditions.
  • The open-source framework offers an accessible and scalable tool for neurological oculomotor disorder research and clinical assessment.