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arXiv · 2511.22607

GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing

Xiaoyin Yang
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Abstract

Eye tracking has become increasingly important in virtual and augmented reality applications; however, the current gaze accuracy falls short of meeting the requirements for spatial computing. We designed a gaze collection framework and utilized high-precision equipment to gather the first precise benchmark dataset, GazeTrack, encompassing diverse ethnicities, ages, and visual acuity conditions for pupil localization and gaze tracking. We propose a novel shape error regularization method to constrain pupil ellipse fitting and train on open-source datasets, enhancing semantic segmentation and pupil position prediction accuracy. Additionally, we invent a novel coordinate transformation method similar to paper unfolding to accurately predict gaze vectors on the GazeTrack dataset. Finally, we built a gaze vector generation model that achieves reduced gaze angle error with lower computational complexity compared to other methods.

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Xiaoyin Yang. 2025-11-27. GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing. https://arxiv.org/abs/2511.22607

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