This study proposes a novel self-calibration method for eye tracking in a virtual reality (VR) headset. The proposed method is based on the assumptions that the user's viewpoint can freely move and that the points of regard (PoRs) from different viewpoints are distributed within a small area on an object surface during visual fixation. In the method, fixations are first detected from the time-series data of uncalibrated gaze directions using an extension of the I-VDT (velocity and dispersion threshold identification) algorithm to a three-dimensional (3D) scene. Then, the calibration parameters are optimized by minimizing the sum of a dispersion metrics of the PoRs. The proposed method can potentially identify the optimal calibration parameters representing the user-dependent offset from the optical axis to the visual axis without explicit user calibration, image processing, or marker-substitute objects. For the gaze data of 18 participants walking in two VR environments with many occlusions, the proposed method achieved an accuracy of 2.1$^\circ$, which was significantly lower than the average offset. Our method is the first self-calibration method with an average error lower than 3$^\circ$ in 3D environments. Further, the accuracy of the proposed method can be improved by up to 1.2$^\circ$ by refining the fixation detection or optimization algorithm.

翻译：本研究提出了一种针对虚拟现实（VR）头戴设备中眼动追踪的新型自校准方法。该方法基于以下假设：用户视点可自由移动，且视觉注视期间不同视点的注视点（PoRs）分布在物体表面的一小块区域内。在该方法中，首先利用I-VDT（速度与离散度阈值识别）算法在三维场景中的扩展，从未校准注视方向的时间序列数据中检测注视点。然后，通过最小化注视点离散度指标的总和来优化校准参数。该方法无需显式用户校准、图像处理或替代标记物，即可识别代表用户依赖的光轴与视轴偏移的最优校准参数。在18名参与者在两个存在大量遮挡的VR环境中行走的眼动数据上，该方法实现了2.1°的精度，显著低于平均偏移。所提方法是首个在三维环境中平均误差低于3°的自校准方法。此外，通过改进注视检测或优化算法，该方法的精度可提升至1.2°。