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REVIEW 2 major objections 2 minor 44 references

AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination

T0 review · 2 major / 2 minor · reviewed 2026-06-28 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Pupil segmentation accuracy drops from 0.928 to 0.767 when moving from controlled active IR to natural ambient sunlight alone.

desk verdict AmbientEye is a solid dataset release for ambient IR pupil segmentation with real practical value, but the annotation quality lacks the checks needed to fully trust the reported performance drop. read the letter →

arxiv 2606.03774 v1 pith:3BTBGMSK submitted 2026-06-02 cs.CV

Mingyu Han , Hyunyoung Han , Nitheekulawatn Thommakoon , Gangtae Park , Jieun Han , Xucong Zhang , Ian Oakley This is my paper · ORCID
classification cs.CV
keywords pupilsegmentationambientinfraredeyetrackingoutdoordatasetpassiveilluminationsmartglassesnaturalsunlightSAM2annotation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tests whether passive infrared cameras without any active light source can support reliable pupil detection in real outdoor settings where sunlight is the only illumination. It introduces the AmbientEye dataset of 2,606,225 eye images from 35 participants across 19 countries, captured with off-axis cameras under two sun-orientation conditions. High-quality pupil labels are created by running SAM2 automatic segmentation and then refining the results with human annotators. Benchmarking a current leading segmentation algorithm on this data shows a clear performance decline compared with existing controlled-IR datasets. The gap demonstrates that the ambient-light case introduces practical difficulties for eye-tracking uses such as smart glasses.

What carries the argument

The AmbientEye dataset of 2,606,225 eye images captured outdoors under passive natural sunlight with off-axis IR cameras and SAM2-plus-human annotations.

What would settle it

Re-annotating a random subset of AmbientEye images with an entirely independent annotation protocol and then re-running the same segmentation algorithm to check whether the 0.767 score changes by more than a few points.

Watch

Extended reading notes

Core claim

AmbientEye supplies the first large-scale benchmark for pupil segmentation under natural ambient infrared illumination from sunlight alone. Images were gathered outdoors from a diverse participant pool using two camera configurations and two sun positions. Annotations combine SAM2 output with human refinement. When a state-of-the-art pupil segmentation algorithm is evaluated on AmbientEye, its score falls from 0.928 on prior controlled-IR collections to 0.767, establishing the dataset as a reference point for this unconstrained outdoor scenario.

Load-bearing premise

The combination of SAM2 automatic segmentation followed by human annotator refinement produces annotations of sufficient quality and consistency to serve as a reliable benchmark standard for the new ambient illumination domain.

Editorial extensions

If this is right

  • Existing pupil segmentation methods developed for controlled active-IR settings do not transfer directly to outdoor ambient conditions.
  • Reliable pupil detection for all-day outdoor use will require algorithms explicitly designed for variable natural sunlight.
  • AmbientEye provides the first public reference for measuring progress on passive-IR eye tracking.
  • Power savings from removing active IR illuminators remain out of reach until segmentation robustness improves.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Smart-glasses battery life could increase if methods close the performance gap, because active IR sources consume significant power.
  • Outdoor augmented-reality applications that rely on gaze may need hybrid active-plus-passive systems until ambient-only solutions mature.
  • The dataset's scale and participant diversity suggest it can also support training new models that generalize across skin tones and lighting angles.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces AmbientEye, a dataset of 2,606,225 eye images captured outdoors from 35 participants across 19 countries under natural sunlight (two off-axis camera setups and two sun-orientation conditions). Pupil annotations are produced via SAM2 automatic segmentation followed by human refinement. A state-of-the-art pupil segmentation algorithm is benchmarked, yielding 0.767 performance on AmbientEye versus 0.928 on prior controlled-IR datasets; the gap is presented as evidence that ambient illumination constitutes a distinct practical challenge for eye tracking.

Significance. If the ground-truth annotations are shown to be reliable and consistent, the work supplies the first large-scale benchmark for passive-IR pupil segmentation in unconstrained outdoor settings. This directly addresses power-consumption barriers for all-day smart-glasses eye tracking and supplies a falsifiable testbed for future algorithms under variable sunlight and reflections.

major comments (2)
  1. [§3 (Dataset Creation / Annotation)] §3 (Dataset Creation / Annotation): The central empirical claim rests on the 0.767 figure being a valid measure of algorithmic difficulty rather than annotation noise. The description states only that annotations result from 'SAM2 automatic segmentation, followed by refinement by human annotators' with no inter-annotator agreement statistics, expert validation against ophthalmologists, or quantitative analysis of residual SAM2 errors under off-axis sunlight and corneal reflections. This omission directly affects whether the reported performance gap can be interpreted as domain difficulty.
  2. [§4 (Benchmarking and Results)] §4 (Benchmarking and Results): The comparison to the 0.928 controlled-IR baseline is load-bearing for the 'distinct challenge' conclusion, yet no statistical test of the gap, participant-level variance, or exclusion-criteria justification is supplied. Without these, the headline drop cannot be assessed for robustness.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'high-quality pupil annotation' is used without supporting metrics; this should be qualified or moved to the methods section.
  2. [Tables/Figures] Table/Figure captions: Ensure all reported metrics (e.g., IoU or Dice) are explicitly defined and that the exact evaluation protocol on prior datasets is stated for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment below and indicate the revisions that will be incorporated into the next manuscript version.

read point-by-point responses

  1. Referee: [§3 (Dataset Creation / Annotation)] §3 (Dataset Creation / Annotation): The central empirical claim rests on the 0.767 figure being a valid measure of algorithmic difficulty rather than annotation noise. The description states only that annotations result from 'SAM2 automatic segmentation, followed by refinement by human annotators' with no inter-annotator agreement statistics, expert validation against ophthalmologists, or quantitative analysis of residual SAM2 errors under off-axis sunlight and corneal reflections. This omission directly affects whether the reported performance gap can be interpreted as domain difficulty.

    Authors: We agree that quantitative validation of the annotations is important for interpreting the performance numbers. In the revised manuscript we will add (i) inter-annotator agreement (mean Dice score) computed on a random subset of 10 000 images that were independently refined by two human annotators and (ii) a breakdown of the fraction of images in which the SAM2 initialization was substantially edited by humans, stratified by sun-orientation condition. We will not add ophthalmologist validation because the annotations concern geometric pupil boundaries in infrared imagery rather than clinical diagnosis; we will explicitly state this scope limitation. These additions directly address the concern about annotation noise versus domain difficulty. revision: partial

  2. Referee: [§4 (Benchmarking and Results)] §4 (Benchmarking and Results): The comparison to the 0.928 controlled-IR baseline is load-bearing for the 'distinct challenge' conclusion, yet no statistical test of the gap, participant-level variance, or exclusion-criteria justification is supplied. Without these, the headline drop cannot be assessed for robustness.

    Authors: We concur that additional statistical reporting is needed. The revised manuscript will include (i) a Wilcoxon signed-rank test comparing per-image IoU on AmbientEye versus the controlled-IR datasets, (ii) participant-level mean IoU and standard deviation across the 35 subjects to quantify variance, and (iii) an explicit description of the exclusion criteria (images removed for severe motion blur, extreme head pose, or hardware failure) together with the number of frames excluded per condition. These changes will allow readers to evaluate the robustness of the reported performance gap. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical dataset release with external benchmarks

full rationale

The paper introduces AmbientEye as a new dataset and reports benchmark results on pupil segmentation. No derivations, equations, fitted parameters, or predictions are present in the provided text. The performance comparison (0.928 on controlled IR vs. 0.767 on AmbientEye) is a direct empirical measurement against external datasets, with no self-referential reduction or load-bearing self-citation. Annotation process is described but does not involve any claimed derivation chain. This is a standard dataset paper with no internal circularity.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Dataset introduction paper; contains no mathematical derivations, fitted parameters, or postulated entities.

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Cite this review

Pith. "Pith review of AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination." pith.science (2026). https://pith.science/paper/3BTBGMSK

@misc{pith2026260603774,
  author       = {Pith},
  title        = {Pith review of: AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3BTBGMSK}},
  note         = {Machine review of arXiv:2606.03774}
}
read the original abstract

Eye tracking is essential for smart glasses, as it provides insight into user attention for ambient intelligence applications. However, most existing eye-tracking systems rely on active infrared (IR) illumination, creating practical barriers to all-day outdoor use due to power consumption. In this paper, we investigate whether passive IR cameras alone, without any active IR light source, can enable reliable pupil detection in unconstrained outdoor environments, where ambient sunlight serves as the sole illumination source. To support this investigation, we introduce AmbientEye, a large-scale dataset of 2,606,225 eye images collected from 35 participants from 19 countries. It is captured outdoors under natural sunlight with two off-axis camera configurations and two sun-orientation conditions. We provide high-quality pupil annotation through SAM2 automatic segmentation, followed by refinement by human annotators. We benchmark a state-of-the-art pupil segmentation algorithm on our dataset and compare its performance with that on existing datasets under controlled IR illumination. Results reveal a substantial drop in pupil segmentation performance from 0.928 on controlled IR datasets to 0.767 on AmbientEye. This performance gap highlights the challenge of the ambient-light setting. This positions AmbientEye as a first benchmark for an unexplored and highly practical eye-tracking scenario.

Figures

Figures reproduced from arXiv: 2606.03774 by the authors.

Figure 1
Figure 1. Overview of the AmbientEye data collection setup. Left: Custom eye-tracking glasses [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Our AmbientEye dataset captures diverse outdoor scenarios under sun-facing and sun [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. (A) Probability density of the pupil aspect ratio (minor/major axis), computed over all [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: IR irradiance analysis. (A) Frame count per IR intensity bin. (B) Pupil IoU across datasets [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Representative success and failure cases (C#) for each two axis ( [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: IoU as a function of pupil aspect ratio across all evaluation sets. (A) OpenEDS, TEyeD, [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: IoU as a function of normalized pupil area (ellipse area as a percentage of image area). (A) [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: IoU as a function of ambient illumination, evaluated on 84,021 annotated frames sampled [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

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Reviewed June 28, 2026 · model on record in the stance chip above.