Evidence map›Paper›PMID 40169547›Full record

ArticleNature communications2025

Accurate eye tracking from dense 3D surface reconstructions using single-shot deflectometry.

Jiazhang Wang, Tianfu Wang, Bingjie Xu, Oliver Cossairt, Florian Willomitzer

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jiazhang WangWyant College of Optical Sciences, University of Arizona, Tuscon, AZ, 85721, USA. jiazhangwang@arizona.edu.ORCID http://orcid.org/0009-0008-5657-5483
Tianfu WangDepartment of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.
Bingjie XuDepartment of Computer Science, Northwestern University, Evanston, IL, 60208, USA.
Oliver CossairtDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA.
Florian WillomitzerWyant College of Optical Sciences, University of Arizona, Tuscon, AZ, 85721, USA. fwillomitzer@arizona.edu.ORCID http://orcid.org/0000-0003-1563-1131

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Eye-tracking plays a crucial role in the development of virtual reality devices, neuroscience research, and psychology. Despite its significance in numerous applications, achieving an accurate, robust, and fast eye-tracking solution remains a considerable challenge for current state-of-the-art methods. While existing reflection-based techniques (e.g., "glint tracking") are considered to be very accurate, their performance is limited by their reliance on sparse 3D surface data acquired solely from the cornea surface. In this paper, we rethink the way how specular reflections can be used for eye tracking: We propose a method for accurate and fast evaluation of the gaze direction that exploits teachings from single-shot phase-measuring-deflectometry. In contrast to state-of-the-art reflection-based methods, our method acquires dense 3D surface information of both cornea and sclera within only one single camera frame (single-shot). For a typical measurement, we acquire  >3000× more surface reflection points ("glints") than conventional methods. We show the feasibility of our approach with experimentally evaluated gaze errors on a realistic model eye below only 0.13°. Moreover, we demonstrate quantitative measurements on real human eyes in vivo, reaching accuracy values between only 0.46° and 0.97°.

Indexed as

Eye MovementsEye-Tracking TechnologyImaging, Three-DimensionalAlgorithmsCorneaFixation, OcularHumansSclera

Identifiers

PMID40169547
PMCPMC11962075

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.