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REVIEW 4 major objections 4 minor 76 references

Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Defocus blur cues, injected at inference time, turn a pre-trained relative-depth diffusion model into a metric-depth estimator without retraining.

desk verdict Clever training-free idea that deserves a serious referee, but the headline comparison is built on hyperparameters tuned on the same seven test scenes, so the stated edge over baselines isn't established yet. read the letter →

arxiv 2505.17358 v1 pith:TIGRVVBH submitted 2025-05-23 cs.CV

Chinmay Talegaonkar , Nikhil Gandudi Suresh , Zachary Novack , Yash Belhe , Priyanka Nagasamudra , Nicholas Antipa This is my paper · ORCID
classification cs.CV
keywords monocularmetricdepthestimationdefocusblurfromdiffusion-basedpriorinference-timeoptimizationcircleofconfusionzero-shotgeneralizationlatentconsistencymodel
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 tries to establish that a pre-trained relative monocular depth diffusion model, Marigold, can be made to output metric depth at inference time, with no retraining, by feeding it two images of the same scene: one all-in-focus small-aperture image and one blurred large-aperture image. The metric scale and offset, together with Marigold's initial noise latent, are optimized so that the predicted depth, when run through a defocus-blur image formation model, reproduces the observed blurred image. If correct, this would give zero-shot metric depth estimation a physics-based calibration signal that is independent of training-data scale and camera intrinsics, and would attack two known failure modes of learned depth models: wrong absolute scale and texture-depth coupling. On a self-collected seven-scene DSLR and RealSense dataset, the method reports lower average error than MLPro, UniDepth, and Metric3D.

What carries the argument

The load-bearing object is the defocus-blur forward model, $\hat{\mathbf{x}}_b = g(\mathbf{x}, d_m, f, F, N)$, which forms a blurred image as a spatially varying convolution between the all-in-focus image and a depth-dependent disc PSF with a smooth linear edge; the PSF radius is the circle of confusion $c(d) = \frac{f^2}{N}\frac{|d-F|}{d(F-f)s}$, with focal length $f$, focus distance $F$, F-stop $N$, and pixel size $s$ taken from camera metadata. The second ingredient is Marigold-LCM, a single-step latent consistency version of Marigold (a Stable Diffusion-based model trained for monocular relative depth), which acts as a differentiable map from a Gaussian noise latent $z_T^{(d)}$ and the AIF image to a depth map. The paper backpropagates the blur-rendering loss through that map into $z_T^{(d)}$ and through the affine parameters $\alpha = s_{\max}\sigma(a)$, $\beta = s_{\min}\sigma(b)$, rescaling $z_T^{(d)}$ to the Gaussian annulus norm so it stays on the prior manifold. The disc PSF with linear falloff, rather than a Gaussian approximation, is what keeps the gradient signal physically consistent with the real lens.

What would settle it

Capture a new indoor scene containing a strong occlusion boundary and a textured foreground, run the two-aperture optimization, and compare against the RealSense ground truth at the boundary; if the predicted depth is systematically wrong exactly where the depth changes abruptly while the blur-rendering loss stays small, the shift-invariant no-occlusion forward model is fitting a wrong explanation. A second check is to use a lens with strong spherical aberration or a non-circular aperture: the paper's own Fig. 2 predicts model mismatch, so a larger-than-expected error drop versus the Gaussian-PSF baseline would confirm the disc-PSF assumption is load-bearing.

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Extended reading notes

Core claim

At the center of the claim is an affine lift from relative to metric depth, $d_m = \alpha \cdot d + \beta$, whose two parameters are learned per scene at inference time. The paper's discovery is that defocus blur is a sufficient physical signal to determine those parameters and to refine the relative depth map itself, because the blurred image can be synthesized, via a disc point-spread function whose radius is the circle of confusion, from the all-in-focus image and any candidate metric depth. Minimizing the $\ell^2$ distance between the synthesized and the actually captured blurred image, with respect to $\alpha$, $\beta$, and Marigold's initial noise latent $z_T^{(d)}$, yields metric depth that is correctly scaled and less texture-coupled. On the authors' seven-scene real dataset, this yields an average RMSE of 0.273 m and a $\delta_1$ accuracy of 0.879, compared with RMSE 0.459-0.644 and $\delta_1$ 0.259-0.650 for the learned zero-shot baselines.

Load-bearing premise

The optimization relies on the photographed blur being well described by the thin-lens circle-of-confusion model: a depth-dependent, shift-invariant disc PSF, no occlusion, a truly all-in-focus small-aperture image, and radiometrically linear, energy-balanced raw captures; if the real lens's PSF, focus readout, occlusion, or scene radiance violate these assumptions, the gradient signal can be biased.

Editorial extensions

If this is right

  • A relative-only depth foundation model can be converted into a metric estimator at test time with only two aperture settings and known camera parameters, requiring no training data and no camera calibration.
  • Defocus cues do double duty: they fix the global metric scale and offset, and they refine the relative depth map itself, since ablating the noise-latent optimization degrades all reported metrics.
  • The choice of PSF model matters: with a Gaussian PSF the method's average RMSE rises to 0.528, while the disc PSF gives 0.273, so physical fidelity of the forward model is a primary driver of accuracy.
  • The best blur strength is an intermediate F-stop (f/8 on the real data, f/13 in simulation); too little or too much blur makes the inverse problem ill-posed.
  • The approach is zero-shot in the sense that no weights are retrained, but it is not real-time: optimization takes roughly 3.5-4 minutes per scene on an A-40 GPU.

Reading between the lines

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

  • Going beyond the paper, the same inference-time scheme could be adapted to dual-pixel sensors, which encode a similar depth-dependent blur without needing two physical apertures, potentially bringing the physics-based scale correction to ordinary phone cameras.
  • An untested but natural extension is to jointly estimate the all-in-focus image and the depth map from a single blurred capture, removing the small-aperture requirement and making the method usable in light-limited settings.
  • The physics-plus-prior recipe is not limited to visible-light photography: any depth-dependent blur model, such as those in endoscopy, microscopy, or hyperspectral imaging, could be plugged into the same latent-noise optimization, and the authors point to these applications as future directions.
  • A testable prediction of the paper is that on scenes with severe occlusions or reflective and transparent surfaces, the optimization will fit the blur model by distorting depth in the wrong places; measuring per-pixel error against ground truth in those regions would quantify the cost of the no-occlusion assumption.
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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 / 4 minor

Summary. The paper proposes a training-free method that turns Marigold, a pretrained relative monocular depth diffusion model, into a metric depth estimator at inference time. Two raw images are captured at the same viewpoint: an all-in-focus image at a small aperture (F-stop 22) and a blurred image at a larger aperture (F-stop 8). The method optimizes the initial latent noise of Marigold-LCM together with affine scale and offset parameters (alpha and beta) by minimizing an L2 loss between the captured blurred image and a blurred image synthesized from the predicted metric depth through a disc-PSF defocus forward model. The authors collect a seven-scene dataset using a rigidly coupled DSLR and Intel RealSense camera, compare against MLPro, UniDepth, and Metric3D, and report lower average RMSE, REL, and log10 errors as well as higher delta accuracy metrics (Table 1). They also report ablations on the PSF model, aperture choice, alpha/beta initialization, noise-latent initialization, and the number of sampling steps.

Significance. The central idea, using physical defocus blur as an inference-time cue to convert a relative depth prior into metric depth, is original and potentially useful. The paper includes a physically grounded forward model, a clear ablation showing the disc PSF outperforms a Gaussian PSF, robustness results over ten noise-latent initializations with standard deviations (Table 10), and an analysis of aperture sensitivity. If the quantitative claim survives a properly protocoled evaluation, this would be a meaningful contribution to zero-shot metric depth estimation. The main weakness is that the headline numerical claim currently rests on an evaluation protocol in which key hyperparameters were selected on the same seven test scenes used for the final comparison.

major comments (4)
  1. [Appendix G / Table 12] The main result in Table 1 is obtained with the blurred image captured at F-stop N=8, but Appendix G, Table 12 reports performance for N=4, 8, 11, 13, and 16 on the same real test scenes, and the main text chooses N=8 because it is the best on those scenes. This is test-set selection: the reported RMSE of 0.273 is the best of several aperture choices rather than the result of a fixed protocol. To support the headline comparison, the aperture should be selected on held-out scenes or by a pre-registered criterion that does not use the evaluation scenes.
  2. [Appendix A / Eq. (9)] The scene depth bounds smin=1.49 and smax=3.5 are described as 'a conservative upper bound on the potential maximum scale and offset in the real dataset,' meaning they were set using the evaluation dataset itself. Since alpha and beta are parameterized as sigmoid functions of smax and smin in Eq. (9), these dataset-derived bounds directly constrain the metric output. The paper should justify how these bounds are known a priori without access to the test scenes, and ideally include a sensitivity analysis showing that the results are stable over a range of smin and smax values.
  3. [Appendix D / Tables 3-9] The per-scene tables show that baselines outperform the proposed method on several scenes: for example, Metric3D has lower RMSE than the proposed method on BOOKS (0.263 vs. 0.312) and SHOERACK (0.209 vs. 0.251), and both UniDepth and Metric3D beat the proposed method on THORDOG (0.424 and 0.422 vs. 0.505). The average improvements in Table 1 are therefore driven by a small number of favorable scenes, such as PLANE and STAIRS. With only seven scenes, no error bars on Table 1, and no significance tests, the claim of consistent superiority is not established. The authors should report per-scene confidence intervals and a paired significance test, or substantially temper the claim.
  4. [Eq. (6)-(7) / Limitations] The optimization signal for alpha, beta, and the latent depends on the accuracy of the defocus forward model, which assumes a depth-dependent shift-invariant disc PSF, neglects occlusion, and relies on the focus distance F being read manually from the lens's analog scale. The paper acknowledges occlusion-boundary errors in the Limitations section, but it does not quantify the sensitivity of the metric result to errors in F or to PSF falloff parameters. A sensitivity analysis for F and for the PSF shape would help establish that the recovered metric scale is not biased by forward-model mismatch.
minor comments (4)
  1. [Eq. (11) / Section 3] The constraint ||z_T||_2 = sqrt(M) appears in Eq. (11), and Section 3 mentions rescaling to this norm, but the optimization procedure in Section 4 does not state how often or in what form this projection is applied during the 200 iterations; this should be specified for reproducibility.
  2. [Appendix C] The text says 'we use the pyrealsense2library' without a space, and it would be clearer to say 'the pyrealsense2 library.'
  3. [Table 2 caption] The caption 'Optimizing with the noise latent with defocus cues (ours) improves upon optimizing only the affine parameters' should be rephrased for clarity, for example 'Optimizing the noise latent jointly with the affine parameters improves upon optimizing only the affine parameters.'
  4. [Fig. 4 caption] The caption contains a sentence fragment beginning 'recovers sharp details but fails at metric scale and relative depth accuracy for many of the scenes'; the subject of this sentence is missing and should be supplied.

Circularity Check

0 steps flagged · score 1.0 of 10

No equation-level circularity: metric depth is recovered by optimizing against a physical defocus forward model; self-citations are non-load-bearing and the evaluation-selection concern is a correctness risk, not a derivation cycle.

full rationale

The central derivation defines metric depth as d_m = smax*sigma(a)*D(xhat_phi(z_T, z_x, 1)) + smin*sigma(b) (Eq. 9) and solves argmin ||x_b - g(x, d_m, f, F, N)||^2 (Eqs. 10-11). Neither the metric scale/offset nor the refined noise latent is fit to ground-truth depth or to the evaluation metrics; the optimization target is the captured blurred image through the thin-lens/CoC forward model. Marigold is an external pretrained model, and the defocus blur formation model is independent physics. The few self-citations (DITTO/DITTO-2 for noise optimization, and DiffuserCam for lensless imaging) are related-work/inspiration and are not used to justify the central claim. Concerns that scene bounds smin=1.49, smax=3.5 and the F-stop N=8 were selected using the same seven test scenes affect the strength and validity of the reported average improvement (a test-set-selection and overfitting risk), but they do not make any equation reduce to its inputs by construction. No specific circular step of the enumerated kinds can be exhibited from the paper's equations or citations; per the review rule, this is a non-finding with minor caveats.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The method rests on camera physics, the Marigold latent prior, and several hand-set constants. No new physical entities are introduced. The main external inputs are camera parameters from metadata, the frozen pretrained model, and the hyperparameters listed above.

free parameters (5)
  • Scene metric depth bounds (smin, smax) = smin=1.49 m, smax=3.5 m
    Used to bound alpha and beta through sigmoid parameterization in Eq. (9), fixed for all real scenes, and described as a conservative bound on the real dataset. This is a dataset-informed hand choice, not a measured scene quantity.
  • Blurred-image F-stop N = N=8
    Main configuration selected after evaluating N=4,8,11,13,16 on the same real dataset (Appendix G, Table 12) and observing N=8 gives the best average metrics. This is test-set-informed hyperparameter selection.
  • Marigold-LCM sampling steps = 1
    Chosen from ablation in Appendix E (Table 11): one step minimizes runtime with no consistent metric gain from more steps.
  • Optimizer learning rates = 1.5e-3 for z_T, 5e-3 for a,b
    Adam hyperparameters listed in Appendix A; not ablated and likely not load-bearing for the central claim.
  • Optimization iterations = 200
    Fixed iteration count in Section 4; runtime about 3.5 to 4 minutes. No convergence criterion or ablation of iteration count is reported.
assumptions (6)
  • domain assumption Thin-lens circle-of-confusion model, Eq. (2)
    The depth-dependent blur diameter is derived from thin-lens optics, ignoring aberrations, diffraction, and the octagonal aperture; Fig. 2 shows residual PSF mismatch.
  • domain assumption Small-aperture capture is an all-in-focus image with linear radiometric response and balanced energy
    Section 4 assumes N=22 yields negligible blur, raw images are linear, and exposure/aperture scaling equates energy. Violations bias the forward model.
  • domain assumption Depth-wise shift-invariant disc PSF with no occlusion, Eqs. (3) through (6)
    Assumes one depth per pixel and a spatially invariant kernel; the authors acknowledge limitations at occlusion boundaries and from diffraction.
  • domain assumption Marigold latent prior is a valid manifold for gradient-based inference-time optimization
    The method relies on Marigold-LCM's single-step denoiser and on the Gaussian annulus theorem for latent rescaling; the geometric faithfulness of the learned manifold is not established outside its training distribution.
  • ad hoc to paper Known scene depth bounds smin and smax
    The sigmoid parameterization of alpha and beta assumes a priori metric bounds; the chosen values are fixed for all scenes and derived from the authors' dataset rather than from the physical scene.
  • domain assumption RealSense depth maps are accurate ground truth after calibration and averaging
    Evaluation depends on RealSense depth being correct within its range and on the charuco calibration; the paper notes RealSense has up to 4 cm error at 2 m.

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Cite this review

Pith. "Pith review of Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues." pith.science (2026). https://pith.science/paper/TIGRVVBH

@misc{pith2026250517358,
  author       = {Pith},
  title        = {Pith review of: Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TIGRVVBH}},
  note         = {Machine review of arXiv:2505.17358}
}
read the original abstract

Recent monocular metric depth estimation (MMDE) methods have made notable progress towards zero-shot generalization. However, they still exhibit a significant performance drop on out-of-distribution datasets. We address this limitation by injecting defocus blur cues at inference time into Marigold, a \textit{pre-trained} diffusion model for zero-shot, scale-invariant monocular depth estimation (MDE). Our method effectively turns Marigold into a metric depth predictor in a training-free manner. To incorporate defocus cues, we capture two images with a small and a large aperture from the same viewpoint. To recover metric depth, we then optimize the metric depth scaling parameters and the noise latents of Marigold at inference time using gradients from a loss function based on the defocus-blur image formation model. We compare our method against existing state-of-the-art zero-shot MMDE methods on a self-collected real dataset, showing quantitative and qualitative improvements.

Figures

Figures reproduced from arXiv: 2505.17358 by the authors.

Figure 1
Figure 1. Method overview. We capture two images (same viewpoint) from a camera with focal length f and focused at a distance F: an all-in-focus (AIF) image x (F-stop: N = 22) and a blurred image xb (F-stop: N < 22). Using the AIF x and an initial learnable noise vector z (d) T , Marigold predicts the relative depth d. We then affine transform d with learnable parameters (α, β), obtaining the metric depth d m. Given the AIF x… view at source ↗
Figure 2
Figure 2. Comparing simulated PSFs with the PSF captured from our camera setup. (a) A point source placed d distance away from a thin lens focused at a focus distance F produces a blurred image (PSF) with a diameter c, also known as the circle of confusion. The variation of c with source distance d is shown in the plot. (b) The Disc approximation to the camera PSF lies roughly within the same bounds (dotted red circle) as the… view at source ↗
Figure 3
Figure 3. Correcting texture-depth coupling. We assess MMDE performance on textured fronto￾parallel 2D planes with constant ground truth depths (GT). Using an all-in-focus (a) and blurred image (zoom into insets) (b), our method (RMSE: 0.01) recovers the correct depth maps (c) for the two textured planes. We resolve the texture coupling in the Marigold prediction (Ours Init) and recover the correct metric scale. Competing met… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Comparisons on our collected dataset. Our method consistently estimates accurate metric depth across all the scenes. We also observe better relative depth recovery due to leveraging defocus cues (zoom in 4x on blurred) in some regions (blue boxes, STAIRS). While the co…
Figure 5
Figure 5. Figure 5: Analyzing the effect of different aperture sizes and initializations. Left: We use our forward model to simulate blurred images of a scene from the NYU-v2 dataset. We observe minimum depth error at N = 13, with errors increasing at more extreme aperture values. Right: …
Figure 6
Figure 6. Figure 6: Degradation in depth quality on using only a single blurred image. For the TOYS scene, we observe that using a single blurred image as input results in severely inaccurate relative depth (middle), with all toys, guitar, and the monitor at similar relative depths. Our p…
Figure 7
Figure 7. Figure 7: 7x5 ChArUco Board Method RMSE ↓ REL ↓ log10 ↓ δ1 ↑ δ2 ↑ δ3 ↑ Ours 0.505 0.205 0.075 0.772 0.940 0.984 MLPro 0.633 0.284 0.106 0.598 0.921 0.969 UniDepth 0.424 0.182 0.083 0.573 0.952 0.992 Metric3D 0.422 0.177 0.076 0.782 0.936 0.983 [PITH_FULL_IMAGE:figures/full_fig_…
Figure 8
Figure 8. Figure 8: Marigold relative depth after optimization. We show regions (yellow boxes) where we [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.