提出新方法提升眼动追踪精度，适配空间计算需求。
GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing
- 用形状误差正则化优化瞳孔椭圆拟合，提升定位准确率。
- 设计坐标变换算法，在GazeTrack数据集上降低眼动角度误差。
- 模型精度高且计算量小，适合实时空间计算应用。
眼动追踪在虚拟现实与增强现实应用中日益重要，但现有方法的追踪精度尚不足以满足空间计算的需求。我们设计了一套眼动数据采集框架，利用高精度设备构建了首个高精度基准数据集GazeTrack，涵盖不同种族、年龄和视力状况的被试，用于瞳孔定位与眼动追踪研究。提出一种新颖的形状误差正则化方法，约束瞳孔椭圆拟合过程，结合开源数据集训练，提升了语义分割与瞳孔位置预测的准确性。同时，发明了一种类纸张展开的坐标变换方法，可精准预测GazeTrack数据集上的注视向量。最终构建的眼动向量生成模型，在保持较低计算复杂度的同时，显著降低了眼动角度误差。
原文摘要 · Abstract (English)
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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