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REVIEW 3 major objections 7 minor 55 references

Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video

T0 review · 3 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that one shared per-pixel depth map can drive both haze removal and monocular depth estimation in real hazy driving videos, with the two tasks reinforcing each other and beating separate state-of-the-art methods on both.

desk verdict Plausible new joint dehazing/depth framework, but the static-scene assumption in the BCC loop and the dense-fog depth anomaly keep it from being fully convincing. read the letter →

arxiv 2412.11395 v1 pith:HV5TFZX6 submitted 2024-12-16 cs.CV

Junkai Fan , Kun Wang , Zhiqiang Yan , Xiang Chen , Shangbing Gao , Jun Li , Jian Yang This is my paper · ORCID
classification cs.CV
keywords videodehazingdepthestimationatmosphericscatteringmodelbrightnessconsistencyconstraintself-supervisedlearningrealhazydrivingvideosadversarialregularizationmonocular
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 is trying to establish that haze removal and monocular depth estimation from a moving camera can be solved as one problem rather than two. The proposal is a Depth-Centric Learning framework in which a single depth network feeds both the atmospheric scattering model, which reconstructs the hazy frame from a dehazed estimate, and the brightness consistency constraint, which matches pixels between adjacent dehazed frames. Because the same per-pixel depth appears in both models, cleaner dehazed frames sharpen the depth estimate and sharper depth improves the haze removal, creating a mutually reinforcing loop that can be trained on real hazy driving video without aligned ground truth. The authors report that this joint model beats the previous best methods on real benchmarks, for example lowering the no-reference haze quality score NIQE from 4.45 to 3.44 on GoProHazy and the relative depth error Abs Rel from 0.369 to 0.311 on DENSE-Fog light haze.

What carries the argument

The engine of the paper is the unified ASM-BCC model of Eq. (3), which writes the hazy frame as $I_t(x)=J_t(x)e^{-\beta d(x)}+A_\infty(1-e^{-\beta d(x)})$ and simultaneously writes the dehazed target frame as a reprojection $J_t(x)=S(J_s,y)$ with $y\sim K P_{x\to y}d(x)K^{-1}x$. Here $d$ is the single per-pixel depth map from the shared depth network, $J_t$ and $J_s$ are dehazed frames, $\beta$ is a learned non-uniform scattering coefficient, $P_{x\to y}$ is the relative pose, and $S$ is differentiable bilinear sampling. The depth map is the pivot: it is the variable through which haze physics and temporal geometry exchange information. Two regularizers around this core, a wavelet-frequency discriminator that restores high-frequency detail in dehazed frames and a mean-normalized depth discriminator that suppresses black holes in low-texture regions, use a non-aligned clear video and its estimated depth as supervision.

What would settle it

Render or record a hazy driving sequence with known per-pixel depth and a clearly moving foreground object, then run the model with and without the brightness-consistency branch while measuring depth error only inside the moving object's mask. If the moving region's depth error is systematically worse with the branch enabled, the static-scene assumption is the failure mode and the photometric gradient is corrupting the shared depth estimate.

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Extended reading notes

Core claim

The central discovery is that the atmospheric scattering model and the brightness consistency constraint can be unified by making them both depend on the same depth map, and that this shared dependency is what lets the two tasks improve each other. Given a current hazy frame and its neighbors, the model predicts a dehazed pair, a per-pixel depth $d$, a spatially varying scattering coefficient $\beta$, and a relative pose; the scattering model then reconstructs the hazy frame from the dehazed estimate and depth, while the photometric loss reprojects pixels between the dehazed frames using that same $d$. The paper argues that this coupling, regularized by two discriminators that compare the dehazed video and the estimated depth against a non-aligned clear video, is enough to train directly on real hazy driving footage and that it produces both cleaner frames and more reliable depth than running either task separately.

Load-bearing premise

The load-bearing premise is that the scene between adjacent frames is static and rigid, so every pixel's motion is explained by one camera pose and its own depth; when vehicles or pedestrians move, that link breaks and the brightness-consistency loss sends wrong gradients into the shared depth and dehazing networks.

Editorial extensions

If this is right

  • Dehazing and depth estimation can be trained together on real, unaligned hazy driving video, removing the need for synthetic hazy/clear pairs or aligned depth ground truth.
  • A single per-pixel depth map is the coupling variable: cleaner depth improves haze removal through the scattering model, and cleaner dehazed frames improve depth through reprojection, so gains in either task reinforce the other.
  • A spatially varying scattering coefficient $\beta$, rather than a constant one, is needed for real patchy haze and measurably improves depth estimation accuracy.
  • The coupled model also works on real indoor smoke data, where it reaches the highest PSNR and SSIM among the compared methods.
  • At test time the dehazing and depth branches can be detached and run separately, with inference times of 0.075 s and 0.009 s at $640\times192$.

Reading between the lines

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

  • Extrapolating the paper's architecture, the same shared-depth loop could be applied to other weather degradations such as rain, snow, or low light by swapping the ASM branch for the corresponding forward degradation model; the paper does not test this.
  • The static-scene assumption is the most plausible failure point: extending the auto-mask with an explicit motion-segmentation head, and evaluating depth error separately on dynamic and static regions of a hazy benchmark, would test whether moving objects are the main source of residual error.
  • Because the non-uniform $\beta$ network is free to vary per pixel, it could absorb misestimation of depth or airlight; constraining $\beta$ with a haze-density or polarization prior would reveal whether the reported depth gains come from true scattering estimation or from an overly flexible parameter.
  • The paper's observation that predicted depth visually surpasses the reference depth obtained from a clear-video depth network suggests the ASM reconstruction loss acts as an extra depth cue beyond photometric reprojection; this could be verified by ablating the reconstruction loss and measuring depth error alone.
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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

3 major / 7 minor

Summary. The paper proposes Depth-Centric Learning (DCL), a self-supervised framework for joint video dehazing and monocular depth estimation from real hazy driving videos. The central idea is a unified ASM-BCC model in which a shared depth network feeds both the atmospheric scattering model (ASM), used to reconstruct the hazy frame from predicted depth, scattering coefficient, airlight, and a dehazed frame, and a brightness consistency constraint (BCC), which reprojects dehazed neighboring frames via predicted depth and pose. Because aligned ground truth for real hazy driving video is unavailable, the method uses misaligned clear reference videos with a non-aligned reference frame matching loss and two discriminators: DMFIR for high-frequency detail preservation and DMDR for reducing black-hole artifacts in weak-texture depth. Experiments are reported on GoProHazy, DrivingHazy, InternetHazy, and DENSE-Fog, with a supplementary REVIDE experiment providing an aligned-ground-truth sanity check. The paper claims state-of-the-art performance in both dehazing and depth estimation, with particularly large margins on no-reference dehazing metrics.

Significance. If the claimed results hold, the paper makes a useful contribution: it demonstrates that a physical-model-based self-supervised loop can jointly dehaze and estimate depth from real hazy video, and the two regularizers (DMFIR and DMDR) address real artifacts without paired supervision. The method is efficient (low inference time and parameter count), and the publicly available project page and the REVIDE aligned-GT experiment are concrete strengths that go beyond no-reference evaluation. However, two concerns limit full confidence: the BCC branch in Eq. (3) assumes a static rigid scene, which is violated in the target driving domain and is not analyzed; and the dense-fog depth results in Table 2 are inconsistent with the paper's overall claim of superiority. The ablations in Table 3 and Table 4 do support the importance of BCC, DMFIR, DMDR, and the losses, which is reassuring, but the dense-fog anomaly and the lack of motion handling prevent an unconditional recommendation.

major comments (3)
  1. [A Unified ASM-BCC Model, Eqs. (3), (6)-(7)] The BCC branch assumes that every pixel in frame t is related to frame s by a single rigid pose, i.e., y ~ K P_{x->y} d(x) K^{-1} x. In real driving videos, vehicles and pedestrians violate this assumption, and the auto-mask in Eq. (7), taken from Monodepth2, only suppresses pixels where warping does not reduce the photometric error; it is not a motion-segmentation or scene-flow mechanism. The manuscript provides no experiment or analysis isolating dynamic regions, and the supplement's Limitations section explicitly states that collecting high-quality misaligned data in dynamic scenes is challenging. Because the BCC is the mechanism claimed to make joint training beneficial, this untested static-scene assumption is load-bearing for the paper's real-world driving generalization claim. Please either incorporate a motion mask or instance segmentation to exclude dynamic objects, or provide an evaluation (e.g., using DENSE-Fog's radar ground truth in regions flagged as dynamic, or an object-detector-based split) showing that moving objects do not corrupt the depth and dehazing gradients.
  2. [Experiment Results, Table 2] On the DENSE-Fog dense split, DCL reports the worst Abs Rel (1.182) among all compared methods (e.g., RobustDepth 0.605, Mono-ViFI 0.609, Lite-Mono 0.954) and its RMSE log (0.596) is also worse than RobustDepth (0.515) and Mono-ViFI (0.528). The sentence in Section 'Monocular depth estimation' that DCL 'outperforms the others across nearly all five evaluation metrics' is only true for the light-fog split; the dense-fog results directly contradict the broader claim that DCL excels in real hazy scenes. The parenthetical explanation that 'blurred depth estimates tend to be closer to the mean of the ground truth' is not quantified and does not justify the Abs Rel regression. Please either restrict the claim to light-fog conditions, provide an analysis of why dense-fog depth fails (e.g., predicted depth histograms vs. ground truth), or report per-sequence results to identify whether the failure is systematic or due to a few outliers.
  3. [Datasets and Evaluation Metrics, Table 1] The main dehazing comparisons on GoProHazy, DrivingHazy, and InternetHazy rely exclusively on no-reference metrics (FADE and NIQE) without error bars, repeated runs, or statistical significance tests. These metrics are known to be sensitive to contrast and color statistics, so the reported improvements (e.g., NIQE 3.4412 vs. DVD 4.4473) might not reflect perceptually meaningful gains. The supplementary REVIDE experiment provides aligned ground truth, but on indoor smoke, not driving haze. If aligned driving data is genuinely unavailable, the authors should report standard deviations over multiple training runs, or add a qualitative user study, or evaluate on an additional real hazy driving dataset with post-hoc alignment, to substantiate the claim of state-of-the-art real-world dehazing.
minor comments (7)
  1. [Eqs. (2) and (3)] The symbol '~' is used to denote pixel correspondence, but '~' conventionally means 'distributed as' in statistics; please replace it with a clear arrow or the phrase 'is mapped to' to avoid ambiguity.
  2. [Eqs. (3) and (6)] The symbol 'S' is used both for the differentiable bilinear sampling operation and for the SSIM loss; rename one of them (e.g., use 'B' for bilinear sampling) to prevent confusion.
  3. [Eq. (7)] The bracket notation [Lpe(Jt, bJt) < Lpe(Jt, Js)] should be explicitly defined as an indicator function, and the text should clarify that ma is applied element-wise to the photometric loss in Eq. (11).
  4. [Introduction, Contributions] The claim 'To the best of our knowledge, we are the first' is strong; consider softening to 'To our knowledge' and citing any recent concurrent works on joint depth estimation and dehazing from real video.
  5. [Table 2] The table repeats the five metric headers for light-fog and dense-fog without a clear visual separation; please add subheadings or a blank column so the two splits are immediately distinguishable.
  6. [Supplemental Material, Limitations] The limitations paragraph (dynamic scenes, small object detail) is placed only in the supplement; please refer to it explicitly in the main text, since it directly qualifies the real-world generalization claim.
  7. [Supplemental Material, Sec. D] The 'Discussion on predicted depth surpassing reference depth' (Fig. S4) is based on a single visual example; if this claim is important, provide a quantitative comparison (e.g., mean Abs Rel on a subset) between the predicted depth and MonoDepth2 reference depth.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: the joint ASM-BCC training is a self-supervised model with external evaluation anchors; its limitations concern modeling assumptions, not circular derivation.

full rationale

The paper does not claim to derive a new physical result from first principles; it constructs a joint training objective that combines two known constraints, the atmospheric scattering model (ASM) and the brightness consistency constraint (BCC), through a shared depth network. Equation (3) is a modeling definition, not a result that reduces to its own inputs. The depth and dehazing outputs are not fitted parameters dressed up as predictions: they are network outputs evaluated on held-out benchmarks, including the external DENSE-Fog depth dataset with sparse radar ground truth and the REVIDE smoke dataset with aligned ground truth. The NRFM module, the non-aligned datasets, and the DVD baseline are drawn from the authors' prior work, but these are used as components and comparisons, not as an unverified authority that forces the conclusion; the central claim is independently testable against non-self baselines and external metrics. The main weakness identified by the reader and in the supplement's limitation note is that the BCC rigidity assumption can be violated by dynamic objects in real driving scenes, and the auto-mask may not fully suppress motion-corrupted gradients. This is a correctness and generalization concern about the modeling assumptions, not circularity: the loss functions and evaluation protocols do not make the reported improvement true by construction. Therefore the circularity score is 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on learned components and domain assumptions: ASM as forward model, static-scene BCC, availability of non-aligned clear reference videos, calibrated intrinsics, and reference depth from MonoDepth2. Loss weights and alpha are hand-set. No free physical constants are fitted, but the framework's identifiability depends on the regularization terms.

free parameters (3)
  • Loss weights = eta=1e-1, gamma=2e-1, xi=1e-3, omega1=4e-3, omega2=1e-3
    Hand-chosen weights that balance reconstruction, misaligned reference, smoothness, and adversarial losses; no sensitivity analysis is reported.
  • Photometric weight alpha = 0.85
    Fixed constant in Eq. (6) balancing SSIM and L1 terms in the brightness consistency loss.
  • Input frame window = n=1 (3 frames)
    Selected via ablation in Table S3, with 3 frames giving the best Abs Rel, though only slightly better than 2 frames.
assumptions (5)
  • domain assumption Atmospheric scattering model (ASM) in Eq. (1) describes haze formation with a single-scattering model and a per-pixel scattering coefficient.
    Invoked in Section "A Unified ASM-BCC Model"; standard for dehazing but approximate in patchy, dense, or multiple-scattering haze.
  • domain assumption Brightness consistency holds under a rigid scene assumption with known camera intrinsics and estimated pose, as in Eq. (3).
    The BCC loss in Eq. (6) requires corresponding pixels in adjacent frames to have similar brightness after warping; dynamic objects and non-rigid motion violate this.
  • domain assumption Non-aligned clear reference frames matched by NRFM provide valid supervisory signal despite not being pixel-aligned to hazy frames.
    Used in Eq. (5) for the misaligned reference loss; the matching procedure is inherited from the authors' prior DVD paper and not fully specified here.
  • domain assumption Reference depth from MonoDepth2 trained on clear non-aligned videos is reliable enough for regularization after mean normalization.
    Used in Eq. (10) for DMDR; the normalization mu(d)=d/mean(d) removes scale but assumes relative depth correctness.
  • domain assumption Camera intrinsics K are known or calibrated for all evaluation datasets.
    GoPro 11 intrinsics are calibrated by the authors, and REVIDE intrinsics are obtained via COLMAP; the BCC reprojection in Eq. (3) depends on K.

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Cite this review

Pith. "Pith review of Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video." pith.science (2026). https://pith.science/paper/HV5TFZX6

@misc{pith2026241211395,
  author       = {Pith},
  title        = {Pith review of: Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HV5TFZX6}},
  note         = {Machine review of arXiv:2412.11395}
}
abstract

In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness consistency constraint (BCC). To tackle these intertwined tasks, we propose a novel depth-centric learning framework that integrates the ASM model with the BCC constraint. Our key idea is that both ASM and BCC rely on a shared depth estimation network. This network simultaneously exploits adjacent dehazed frames to enhance depth estimation via BCC and uses the refined depth cues to more effectively remove haze through ASM. Additionally, we leverage a non-aligned clear video and its estimated depth to independently regularize the dehazing and depth estimation networks. This is achieved by designing two discriminator networks: $D_{MFIR}$ enhances high-frequency details in dehazed videos, and $D_{MDR}$ reduces the occurrence of black holes in low-texture regions. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art techniques in both video dehazing and depth estimation tasks, especially in real-world hazy scenes. Project page: https://fanjunkai1.github.io/projectpage/DCL/index.html.

Figures

Figures reproduced from arXiv: 2412.11395 by the authors.

Figure 1
Figure 1. Visual comparisons of DVD (Fan et al. 2024), Lite [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The pipeline of our Depth-Centric Learning (DCL) framework. It effectively integrates the atmospheric scattering [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Comparisons of video dehazing performance across (i) GoProHazy, (ii) DrivingHazy, and (iii) InternetHazy. Our method [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visual results on GoProHazy (i) and DENSE-Fog (ii-dense, iii-light). They demonstrate that our method achieves [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Ablation visualization of BCC, DMFIR and DMDR on DENSE-Fog (light). Method BCC DMF IR DMDR Abs Real↓ RMSE log↓ δ1↑ DCL w/o BCC ✓ ✓ 0.636 0.569 0.439 DCL w/o DMFIR ✓ ✓ 0.320 0.366 0.621 DCL w/o DMDR ✓ ✓ 0.340 0.392 0.562 DCL (Ours) ✓ ✓ ✓ 0.311 0.364 0.623 [PITH_FULL_IM…
Figure 6
Figure 6. Figure 6: Visual comparison of depth estimation across dif [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

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Reference graph

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    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.