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REVIEW 4 major objections 5 minor 25 references

FlatTrack: Eye-tracking with ultra-thin lensless cameras

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A flat, mask-based lensless camera tracks gaze on par with lensed trackers, at over 125 fps.

desk verdict First real lensless near-eye gaze dataset and a sound real-data evaluation; the 'parity with lensed' claim rests on simulation and should be tempered. read the letter →

arxiv 2501.15450 v1 pith:VTKS35YE submitted 2025-01-26 eess.IV cs.CV

Purvam Jain , Althaf M. Nazar , Salman S. Khan , Kaushik Mitra , Praneeth Chakravarthula This is my paper · ORCID
classification eess.IVcs.CV
keywords lenslessimagingeyetrackinggazeestimationPhlatCamphasemaskcameranear-eyedatasetFlatNetreconstructionaugmentedreality
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

This paper tries to establish that eye tracking does not need a focusing lens: a mask-based lensless camera placed a few centimetres from the eye can estimate gaze with accuracy comparable to conventional lens-based trackers, while being flat enough to sit inside an eyeglasses frame. To make this case, the authors built a near-infrared PhlatCam phase-mask camera, collected the first real lensless gaze dataset of 20,475 captures from 13 subjects with calibrated gaze directions, and paired it with a two-stage pipeline that first reconstructs an eye image and then regresses the gaze vector. On this dataset the best configuration, FlatNet reconstruction with a ResNet-18 regressor, reports an average angular error of 1.92 degrees at more than 125 frames per second. A separate simulated comparison on the Davis-GS dataset shows lensless and lensed imaging within roughly 0.1 degrees of each other, which is the evidence behind the 'on par' claim.

What carries the argument

The load-bearing object is the PhlatCam phase-mask camera: a thin phase mask placed slightly under 1.5 mm from the sensor, designed for 700 nm near-infrared light, produces a point-spread function $P$ such that the measurement is $Y = P \ast X + N$, with the scene $X$ globally multiplexed. Because the mask-to-sensor distance is tiny, the camera has a very large depth of field, so it can image the eye from roughly 4 cm away without a focusing element. The argument is carried by a two-stage pipeline: FlatNet, a learned reconstruction network trained on simulated measurements of natural images, reconstructs a visible eye image from the lensless capture; then ResNet-18 regresses a 3D gaze unit vector, projected to 2D monitor pixels where an $\ell^1$ loss is applied. Subject-specific fine-tuning of the last two layers of the regressor handles the user calibration that real AR/VR deployments need.

What would settle it

Set up a rig with a lensed camera and the PhlatCam imaging the same eye from the same 4 cm distance on the same 15 by 15 gaze grid with the same illumination, and compare per-subject angular errors. If the real lensless error is substantially larger than the simulated 1.62 to 1.84 degrees and no longer sits within the lensed error band of roughly 1.6 to 1.8 degrees, the paper's central on-par claim fails. A cheaper check is to capture FlatTrack-style images with two NIR illuminators placed to cover the dark corners identified in Section 6.5 and see whether the average error drops below 1.92 degrees.

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Extended reading notes

Core claim

The central claim is that a thin phase-mask lensless camera can replace a conventional lensed camera for near-eye gaze estimation without a meaningful accuracy penalty. The paper reports that on its own FlatTrack dataset, the FlatNet plus ResNet-18 pipeline achieves 1.92 degrees average angular error (best subject 0.91 degrees) with inference at 7.81 ms, i.e., over 125 fps, and that a 5 percent accuracy gain from the EyeCOD method is not worth its 3x inference cost. To support the parity claim, the paper simulates lensless captures from the lensed Davis-GS dataset by convolving with the PhlatCam PSF and adding noise, then reconstructs with FlatNet; across three test subjects the lensed error is 1.79, 1.72, and 1.67 degrees while the lensless error is 1.84, 1.81, and 1.62 degrees. The paper interprets this as minimal difference, indicating that lensless imaging does not introduce significant performance loss while enabling a compact form factor.

Load-bearing premise

The load-bearing premise is that the simulated lensless images used for the lensed-versus-lensless comparison, Davis-GS lens images convolved with the PhlatCam PSF plus noise, faithfully represent what a real PhlatCam would record; real captures include uneven illumination, sensor noise correlations, head motion, and calibration drift that the simulation omits.

Editorial extensions

If this is right

  • AR/VR headsets can place the eye-tracking camera inside the eyeglasses frame rather than at a lens's focusing distance, cutting thickness and weight.
  • At over 125 fps with 7.81 ms inference, the pipeline is fast enough for interactive gaze-based interfaces and foveated rendering.
  • The 20,475-capture dataset gives the lensless-gaze community a real-data training and benchmarking target instead of relying only on simulated lensless images.
  • User-specific fine-tuning with a small calibration set keeps accuracy near 1 to 2 degrees without per-subject retraining from scratch.
  • The parity result predicts that progress on lensed gaze trackers can transfer to lensless hardware through the same reconstruction-plus-regression pipeline.

Reading between the lines

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

  • The paper does not collect a real lensed-versus-lensless comparison on the same rig; its parity numbers come from simulated lensless Davis-GS images, so a same-setup real comparison could confirm or narrow the claimed parity.
  • Section 6.5 attributes high-error grid points to dark reconstructions with poor pupil contrast, so adding NIR illuminators aimed at those corners is a direct, testable fix that could push the 1.92 degree average lower.
  • The globally multiplexed raw measurements suggest a privacy property beyond the form-factor win, but the paper does not quantify how much harder identity or screen-content reconstruction becomes from FlatTrack data.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes FlatTrack, a near-eye gaze tracking system built from an NIR PhlatCam lensless camera and a two-stage pipeline: lensless reconstruction (Wiener deconvolution or FlatNet) followed by a gaze regression network (ResNet-18, EyeCOD, MobileNetV2). The authors collect a 20,475-capture dataset from 13 subjects with calibrated gaze labels, evaluate per-subject held-out rounds, report a best average angular error of 1.92 degrees for FlatNet plus ResNet-18 at over 125 fps, and present a simulated lensed-versus-lensless comparison on the Davis-GS dataset that shows roughly a 0.1 degree difference. The central claim is that lensless eye tracking performs on par with conventional lens-based trackers while enabling a flatter and more compact form factor.

Significance. If the parity claim holds, the contribution is significant: it demonstrates that a mask-based lensless camera placed about 4 cm from the eye can support usable gaze estimation, potentially removing the focusing-distance constraint in AR/VR headsets. The paper's concrete strengths include a new real lensless gaze dataset (the first of its kind, to the authors' knowledge), a sound per-subject held-out evaluation protocol for the real-data experiments, comparisons of two reconstruction methods and three regressors, and clearly reported inference-speed measurements. The significance is tempered by the fact that the headline parity result rests on a simulation whose fidelity to real captures is not established, and the dataset and code are promised only upon acceptance.

major comments (4)
  1. [Sec. 6.4 / Abstract] The abstract's statement that the proposed system 'performs on par with conventional lens-based trackers' is supported only by the simulated Davis-GS comparison in Sec. 6.4, where lensless measurements are generated via Y = P*X + N (Eq. 1) with a single PSF and added noise. The real FlatTrack experiments (Secs. 6.2-6.3) achieve 1.92 degrees average angular error but include no lensed baseline on the same setup, subjects, and protocol. The simulation omits spatially varying illumination, correlated sensor noise, head motion, and calibration drift, which Sec. 6.5 identifies as error sources in the real captures. The parity claim therefore needs a same-setup real lensed-versus-lensless comparison, or the claim should be restricted to the simulated setting.
  2. [Table 3 / Sec. 6.4] The lensed-versus-lensless comparison is reported for only three subjects, with differences of 0.02-0.09 degrees and no confidence intervals or per-subject variance. With n=3, the claim of 'minimal difference' is statistically fragile, especially because Subject 27 is slightly better in the lensless condition. The text also says 21 of the 27 Davis-GS subjects are used for pre-training and 'the remaining 3' for fine-tuning and evaluation, leaving 3 subjects unaccounted for. Please report variability over held-out rounds, include more test subjects, and clarify the subject allocation.
  3. [Sec. 6.5 / Sec. 6.4] Sec. 6.5 shows that the largest real-data gaze errors occur at grid points where reconstructed images are degraded by 'harsher illumination' and 'lack of proper lighting.' These effects are not represented in the Sec. 6.4 simulation, which adds only synthetic noise to PSF-convolved images. Consequently, the simulated lensed-versus-lensless gap of about 0.1 degrees may understate the real gap. The paper should quantify how many of the 225 grid points fall into the high-error category and, ideally, evaluate the system under controlled illumination to separate lighting artifacts from lensless-specific degradation.
  4. [Sec. 6.1 / Sec. 6.4] The validity of the Sec. 6.4 simulation depends on the PSF and noise model used to synthesize lensless inputs, but the manuscript does not state whether the PSF is measured from the physical PhlatCam prototype or simulated, nor how the added noise level was set to match real captures. Since the same frozen FlatNet pretrained on simulated MIRFLICKR measurements is used for reconstruction, a PSF or noise mismatch could bias the simulated comparison in favor of the proposed pipeline. Please provide the PSF calibration procedure, the noise-level justification, and a quantitative comparison of simulated and real lensless captures (e.g., residual statistics or paired examples).
minor comments (5)
  1. [Sec. 6.2 / Table 1] The manuscript uses both 'Wiener' and 'Weiner' spellings; please standardize to 'Wiener' throughout.
  2. [Table 2] The 'best-case error' metric reports only the best subject's held-out error, which is not a standard evaluation statistic; please report per-subject errors or a distribution instead of, or in addition to, the best-case value.
  3. [Sec. 6.4] The paper should clarify that the 'lensed' condition comes from the Davis-GS dataset and describe the exact capture device for those grayscale frames; the current text reads as if a conventional lens camera and the PhlatCam were compared directly, which is not the case.
  4. [Abstract / Sec. 5] The abstract describes the algorithm as 'co-designed,' but Sec. 5 freezes the reconstruction stage and trains only the gaze regressor; please either demonstrate co-design (e.g., end-to-end training or joint optimization) or reword the claim.
  5. [Sec. 4 / Fig. 3] The text gives the vertical stimulus spacing as 66.3 pixels and the vertical field of view as 29.6 degrees, while the Fig. 3 caption says 65.3 pixels and 29.80 degrees; please reconcile these numbers.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the gaze regressor is trained on held-out real labels, and the lensed-vs-lensless simulation is a benchmark rather than a derivation that reduces to its own inputs.

full rationale

The paper's derivation chain is self-contained. Lensless captures are modeled as Y = P*X + N (Eq. 1), reconstructions are obtained via a frozen pretrained FlatNet or Wiener deconvolution (Eqs. 2-4), and gaze is trained with an L1 loss against calibrated held-out labels (Eq. 5). The reported 1.92-degree average angular error is measured on a held-out test round per subject after subject-specific fine-tuning; it is not a fitted parameter renamed as a prediction. The lensed-vs-lensless comparison in Sec 6.4 does use the authors' own PSF and FlatNet to synthesize lensless images from Davis-GS, but this is an external benchmark simulation, not a reduction of the conclusion to its inputs: the gaze regressor is trained and evaluated on these synthesized data, and the comparison could in principle have favored either imaging system. The use of FlatNet [13], a prior publication by some of the same authors, is an invocation of an externally published, frozen reconstruction tool rather than a load-bearing self-citation; the paper does not claim to derive FlatNet from the present dataset. Limitations documented in Sec 6.5, such as uneven illumination and dark images lacking pupil resolution, are correctness concerns about real-data generalization of the simulated parity claim, not circularity. No exhibited step makes the output equivalent to its input by construction, so the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central result rests on the lensless forward model, the transferability of a frozen natural-image reconstruction network to eye images, the accuracy of monitor-based gaze labels, and the representativeness of simulated lensless data for the parity claim. No new physical entities are introduced. The main free parameters are standard training hyperparameters and the Wiener regularization coefficient.

free parameters (3)
  • Wiener regularization coefficient gamma = 1e-5
    Manually set in Eq. 4 for Tikhonov reconstruction; not swept, and affects reconstruction quality and downstream gaze error.
  • Training hyperparameters = LR 0.0001, weight decay 0.0005, 50 epochs, StepLR decay 0.5 every 5 epochs
    Fixed across all models in Sec 6.1; chosen without reported ablation.
  • Affine augmentation ranges = not specified
    Random affine transformations are applied to reconstructed eye images in Sec 6.1, but the exact rotation, scale, and translation ranges are not reported.
assumptions (4)
  • domain assumption Lensless forward model Y = P * X + N with an accurate PSF P (Eq. 1).
    Used for reconstruction and for the simulated lensless benchmark in Sec 6.4; if the real PSF deviates in wavelength or mask-sensor distance, all downstream estimates shift.
  • domain assumption FlatNet pretrained on MIRFLICKR natural images transfers to near-eye eye images.
    The reconstruction stage is frozen (Sec 6.1) and never trained on eye images; the gaze results depend on this transfer succeeding.
  • domain assumption Ground-truth gaze vectors from a 15x15 monitor grid at 50 cm are accurate.
    Labels assume the eye is aligned to the grid origin, the head is stable, and the screen distance is exactly 50 cm; small misalignments add label noise.
  • ad hoc to paper Simulated lensless Davis-GS images represent real lensless captures.
    Sec 6.4 generates lensless inputs by convolving lens images with the PhlatCam PSF and adding noise; real captures include illumination gradients, sensor noise correlations, and motion not modeled, so this assumption is tailored to enable the parity comparison.

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Cite this review

Pith. "Pith review of FlatTrack: Eye-tracking with ultra-thin lensless cameras." pith.science (2026). https://pith.science/paper/VTKS35YE

@misc{pith2026250115450,
  author       = {Pith},
  title        = {Pith review of: FlatTrack: Eye-tracking with ultra-thin lensless cameras},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VTKS35YE}},
  note         = {Machine review of arXiv:2501.15450}
}
read the original abstract

Existing eye trackers use cameras based on thick compound optical elements, necessitating the cameras to be placed at focusing distance from the eyes. This results in the overall bulk of wearable eye trackers, especially for augmented and virtual reality (AR/VR) headsets. We overcome this limitation by building a compact flat eye gaze tracker using mask-based lensless cameras. These cameras, in combination with co-designed lightweight deep neural network algorithm, can be placed in extreme close proximity to the eye, within the eyeglasses frame, resulting in ultra-flat and lightweight eye gaze tracker system. We collect a large dataset of near-eye lensless camera measurements along with their calibrated gaze directions for training the gaze tracking network. Through real and simulation experiments, we show that the proposed gaze tracking system performs on par with conventional lens-based trackers while maintaining a significantly flatter and more compact form-factor. Moreover, our gaze regressor boasts real-time (>125 fps) performance for gaze tracking.

Figures

Figures reproduced from arXiv: 2501.15450 by the authors.

Figure 1
Figure 1. FlatTrack Gaze Estimation. (a) PhlatCam lensless camera allows development of small form-factor gaze tracker. (b) We propose a large dataset of paired lensless captures and gaze directions, which we use to evaluate the efficacy of (c) various lensless gaze estimation techniques. Abstract Existing eye trackers use cameras based on thick com￾pound optical elements, necessitating the cameras to be placed at focusing di… view at source ↗
Figure 2
Figure 2. PhlatCam Imaging Pipeline. (a) Shows the forward imaging process and the lensless capture. Note that the capture doesn’t resemble the scene it is imaging. (b) Reconstructing the scene back involves solving an inverse problem computationally. etc. Although there exist different methods of lensless scene reconstruction in the literature, we will primarily focus on the two most popular methods – FlatNet [13] and Wiener… view at source ↗
Figure 3
Figure 3. FlatTrack Dataset.(a) The capture setup used to collect the FlatTrack dataset. Note the small (∼ 4cm) distance between the camera and the eye. (b)The 15x15 grid on the monitor used as stimulus points to capture data has an FoV of 53.03 degree and 29.80 degree along x & y axis. The pixel distance along x and y directions are 121.3 and 65.3 pixels respectively. (c) FlatNet reconstruction of 6 images each across the gr… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Lensless Gaze Estimation Pipeline. We follow a two-stage approach. In the first stage, we use a fixed lensless reconstruction algorithm to obtain the scene estimate. In the second stage, a gaze regression neural network predicts the gaze vector given the reconstruc￾tio…
Figure 5
Figure 5. Figure 5: Different Lensless Reconstruction Methods. (a) Flat￾Net reconstruction. (b) Wiener deconvolution. FlatNet provides cleaner reconstruction. 6.2. Comparison of Lensless Reconstruction Meth￾ods In this section, we conduct a comparative analysis of the latency and performa…
Figure 6
Figure 6. Figure 6: Error Across the Grid. The average per-pixel error for all 225 points in the grid across all subjects is displayed as a circle. Six grid points are circled, and their corresponding images from a subject dataset are displayed. The larger error corresponds to reconstruct…

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