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：本研究提出了一种面向虚拟现实（VR）头显眼动追踪的新型自标定方法。该方法基于以下假设：用户视点可自由移动，且在视觉注视过程中，不同视点下的注视点（PoRs）分布在物体表面的小范围内。方法首先利用I-VDT（速度与离散度阈值识别）算法向三维场景的扩展，从未标定注视方向的时间序列数据中检测注视点。随后通过最小化注视点离散度度量之和，优化标定参数。该方法无需显式用户标定、图像处理或替代标记物体，即可识别代表用户依赖的光轴到视轴偏移量的最优标定参数。针对18名参与者在两个存在大量遮挡的VR环境中行走时的眼动数据，本方法实现了2.1°的精度，显著低于平均偏移量。本方法是首个在三维环境中平均误差低于3°的自标定方法。此外，通过改进注视检测或优化算法，本方法的精度可提升至1.2°。