Researchers at the University of Arizona’s Wyant College of Optical Sciences have unveiled a groundbreaking 3D imaging technique to transform eye-tracking technology. Eye tracking is vital in virtual and augmented reality headsets and industries like entertainment, medical science, and automotive engineering. Despite its importance, accurately tracking eye movements has long been a challenge.
Traditional methods rely on directional data from a handful of surface points—typically about a dozen. Using a powerful imaging technique called deflectometry, the new approach promises a dramatic improvement by capturing data from over 40,000 surface points, or even millions, in a single camera image.
Deflectometry is a 3D imaging technique that allows for the measurement of reflective surfaces with very high accuracy.
Jiazhang Wang, a postdoctoral researcher in Willomitzer’s lab and the study’s first author, said, “More data points provide more information that can be potentially used to increase the accuracy of the gaze direction estimation significantly. For instance, this is critical to enable next-generation virtual reality applications. We have shown that our method can easily increase the number of acquired data points by more than 3,000, compared to conventional approaches.”
Leveraging the power of deflectometry for applications outside the inspection of industrial surfaces is a major research focus of Willomitzer’s research group in the U of A Computational 3D Imaging and Measurement Lab.
They combine deflectometry with advanced computational techniques from computer vision research to create “computational deflectometry.” This approach is used to analyze artworks, develop 3D imaging methods for measuring skin lesions, and enhance eye-tracking technology.
“The unique combination of precise measurement techniques and advanced computation allows machines to ‘see the unseen,’ giving them ‘superhuman vision’ beyond what humans can perceive.
In this study, researchers tested their eye-tracking method using human participants and a realistic eye model. They achieved impressive accuracy, tracking gaze directions to within 0.46 to 0.97 degrees for humans and just 0.1 degrees for the artificial eye model.
Instead of relying on a few infrared light points, the new technique uses a screen showing structured light patterns. Each screen’s over 1 million pixels acts as an individual light source. By analyzing how these patterns reflect off the eye’s cornea and sclera, the researchers created detailed 3D surface data for precise gaze tracking.
The computational reconstruction accurately predicts gaze direction by combining 3D surface data with geometrical constraints about the eye’s optical axis. Previous research showed this technology could seamlessly integrate into virtual and augmented reality systems.
Patterns embedded in headset frames or reflected from visual content—such as images or video—can simplify the system design. Future iterations of the technology might use infrared light instead of visible patterns, allowing it to function without distracting users.
“To obtain as much direction information as possible from the eye’s cornea and sclera without any ambiguities, we use stereo-deflectometry paired with novel surface optimization algorithms,” Wang said. “The technique determines the gaze without making strong assumptions about the shape or surface of the eye, as some other methods do because these parameters can vary from user to user.”
Eye-tracking System uses Ordinary Cell Phone Camera
As a beneficial “side effect,” this innovative technology creates highly detailed surface maps of the eye, which could enable real-time diagnosis and treatment of eye disorders in the future.
According to the researchers, this is the first use of deflectometry for eye tracking. Jiazhang Wang noted that their early implementation already matches or surpasses the accuracy of commercial eye-tracking systems tested on real human eyes.
With a pending patent and plans for commercialization through Tech Launch Arizona, this breakthrough sets the stage for a new era of precise and reliable eye-tracking technology. The team aims to refine the system further with advanced 3D reconstruction methods and artificial intelligence to enhance its capabilities.
Journal Reference
- Wang, J., Wang, T., Xu, B., Cossairt, O., & Willomitzer, F. (2025). Accurate eye tracking from dense 3D surface reconstructions using single-shot deflectometry. Nature Communications, 16(1), 1-12. DOI: 10.1038/s41467-025-56801-1
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