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Accurate 3D gaze estimation using a simple setup remains a challenging issue for headmounted eye tracking. Current regression-based gaze direction estimation methods implicitly assume that all gaze directions intersect at one point called the eyeball pseudo-center. The effect of this implicit assumption on gaze estimation is unknown. In this paper, we find that this assumption is approximate based on a simulation of all intersections of gaze directions, and it is conditional based on a sensitivity analysis of the assumption in gaze estimation. Hence, we propose a gaze direction estimation method with one mapping surface that satisfies conditions of the assumption by configuring one mapping surface and achieving a highquality calibration of the eyeball pseudo-center. This method only adds two additional calibration points outside the mapping surface. Furthermore, replacing the eyeball pseudo-center with an additional calibrated surface, we propose a gaze direction estimation method with two mapping surfaces that further improves the accuracy of gaze estimation. This method improves accuracy on the state-of-the-art method by 20 percent (from a mean error of 1.84 degrees to 1.48 degrees) on a public dataset with a usage range of 1 meter and by 17 percent (from a mean error of 2.22 degrees to 1.85 degrees) on a public dataset with a usage range of 2 meters.

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This paper presents a gamma self-correction method via chord distribution coding to eliminate the system's gamma effect. For this purpose, the prior gamma-Gaussian characteristic value, which is extracted by chord distribution Gaussian fitting of the preset gamma's wrapped phase, is used to map the projector's gamma. Experimental results demonstrate that the proposed method has competitive accuracy and feasibility for fringe acquisition and gamma calculation. Thus, this method can be used for online gamma self-correction in continuous three-dimensional measurement.

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