Pith. sign in

REVIEW 3 major objections 3 minor 65 references

Active biphasic heat transfer enhancement in vertical natural convection

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A 2% add-on of a low-boiling liquid converts weak vertical natural convection into a self-sustained bubbly circulation that delivers 246% more heat transfer at the same superheat.

desk verdict The record is unverifiable: the full text is an unrelated stereo-matching paper, leaving only an abstract that cannot support the 246% claim. read the letter →

arxiv 2508.01262 v2 pith:7BOZ3MJX submitted 2025-08-02 physics.flu-dyn

Haoran Qi , Chaoben Zhao , Yaning Fan , Yihong Du , Varghese Mathai , Chao Sun This is my paper · ORCID
classification physics.flu-dyn PACS 44.25.+f47.55.dr
keywords verticalnaturalconvectionbiphasicheattransfernucleateboilingHFE-7000enhancementpseudo-turbulencephase-changecirculation
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

Vertical natural convection is inherently weak when the heat transfer direction is horizontal because buoyancy acts vertically, so the flow cannot easily carry heat across the cell. The paper reports a strategy to overcome this by adding 2% by volume of HFE-7000, a low-boiling liquid, and introducing a gas-liquid layer at the top of the cell. This creates a self-sustained pseudo-turbulent biphasic state in which bubbles evaporate, circulate, and condense, and the paper claims this agitation raises the heat flux by 246% at a constant superheat of about 6.4 K compared with single-phase vertical convection. Shadowgraphy and laser Doppler anemometry are used to argue that the bubble-driven agitation is what modifies the temperature field and enhances the heat flux.

What carries the argument

The central mechanism is a self-sustained pseudo-turbulent biphasic state. The low-boiling liquid HFE-7000, at 2% volume fraction, evaporates near the heated wall, the vapor bubbles rise and circulate through the cell, and they condense at the gas-liquid layer on top, continuously returning fluid and closing the loop. This phase-change-driven circulation acts as an internal stirrer: it replaces the buoyancy-aligned, poorly mixing flow of vertical natural convection with agitation that transports heat horizontally, which is what the paper argues produces the 246% enhancement.

What would settle it

A control experiment with the same 2% HFE-7000 loading and the same superheat but with the gas-liquid layer removed, or with the bubbles suppressed, that reproduces a large part of the 246% gain would show the gain is not from bubble-driven agitation. A direct measurement of the mixture's thermal conductivity and viscosity could also test whether property changes alone account for the enhancement.

Watch

Extended reading notes

Core claim

The central claim is that a small addition of a low-boiling-point immiscible liquid plus a gas-liquid layer on top of a vertical natural convection cell converts the normally weak flow into a self-sustained, vigorously stirred biphasic circulation. In the full nucleate boiling state, at a superheat of about 6.4 K, the paper reports a 246% enhancement in heat transfer relative to the ordinary single-phase case. The evidence is flow visualization and laser Doppler anemometry showing that the evaporating, circulating, and condensing bubbles and biphasic particles agitate the flow, and the paper attributes the enhanced heat flux and modified temperature field to this agitation.

Load-bearing premise

The claim rests on the assumption that the 246% enhancement comes from agitation by the evaporating and condensing bubbles, not from changed thermophysical properties of the water–HFE-7000 mixture, and that the baseline is a matched single-phase run at the same superheat.

Editorial extensions

If this is right

  • Heat transfer in vertical natural convection can be increased by about 2.5 times without raising the wall temperature, using only a small volume fraction of a low-boiling liquid.
  • The enhanced state is self-sustained: once the biphasic circulation is established, no external stirring or moving parts are needed to maintain the extra heat flux.
  • The mechanism offers a route to cooling in geometries where the heat flux is perpendicular to gravity and where single-phase natural convection is the only available flow.
  • The reported 246% figure provides a quantitative target that any alternative explanation, such as a change in mixture properties, would have to reproduce to displace the agitation mechanism.

Reading between the lines

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

  • If the result generalizes to other low-boiling liquids and volume fractions, the method could become a design rule for phase-change-driven stirring in inclined or horizontal cells as well.
  • A natural next experiment is to measure the enhancement with the same 2% loading but without the top gas-liquid layer, to isolate the contribution of the condensation-driven circulation from the intrinsic nucleate boiling agitation.
  • The observed state might be modeled by coupling a nucleate boiling heat-transfer correlation to a mean circulation driven by condensation, which would allow predictions of the enhancement factor as a function of superheat and fill fraction.
  • If the agitation mechanism is confirmed, the approach suggests that intentionally introducing a condensable vapor cycle is a way to achieve 'active' heat transfer enhancement with zero external power input.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The paper's abstract claims that adding 2 vol% HFE-7000 to a water-based vertical natural convection cell with a gas-liquid layer creates a self-sustained pseudo-turbulent state of evaporating, circulating, and condensing biphasic bubbles, achieving 246% heat transfer enhancement at T_sup ≈ 6.4 K in the full nucleate boiling state, with validation by shadowgraphy and Laser Doppler Anemometry. The supplied full text, however, is an entirely different manuscript on unsupervised stereo matching, containing no experimental apparatus, no heat-transfer methods, no control experiments, no raw data, and no uncertainty analysis. The abstract's central claim is therefore unsupported in the submitted record.

Significance. If the heat-transfer result were properly documented, the concept of using a low-boiling-point additive to create self-sustained biphasic agitation in vertical natural convection could be of genuine interest to the thermal-fluids community, particularly for horizontal-heat-transfer configurations where buoyancy is misaligned with the heat flux. The abstract states a specific, falsifiable quantitative claim. However, because the submitted manuscript contains none of the supporting methods, data, or validation, the technical significance cannot currently be assessed; the record provides no reproducible evidence and no basis for judging the plausibility of the claimed enhancement.

major comments (3)
  1. [Full text (entire manuscript)] The full text of arXiv:2508.01262 is not the paper described in the abstract; it is a stereo-matching manuscript (Liu et al., 'Integrating Disparity Confidence Estimation into Relative Depth Prior-Guided Unsupervised Stereo Matching') with no experimental apparatus, no heat-transfer measurements, no baseline description, no uncertainty analysis, and no data relevant to vertical natural convection. Consequently, the abstract's central claim of 246% heat transfer enhancement at T_sup ≈ 6.4 K is entirely unverifiable within this record, and the validation by shadowgraphy and LDA mentioned in the abstract is not described anywhere. This is a load-bearing deficiency that prevents any evaluation of the paper's central scientific claim.
  2. [Abstract, third and fourth sentences] Even taking the abstract at face value, the enhancement is attributed to bubble-induced agitation, but no evidence is presented that rules out altered thermophysical properties of the water-HFE-7000 mixture as the cause. The abstract does not report a matched single-phase baseline at the same superheat or an additive-only control; without such a control, the mechanistic attribution to bubble agitation cannot be distinguished from a change in mixture properties, so the central mechanism claim is unsupported.
  3. [Abstract, second sentence] The abstract reports a specific quantitative result but provides no description of the experimental configuration, the definition and measurement of T_sup, the uncertainty of the heat-flux measurement, or the number of repeated trials. These omissions, combined with the absence of the body text, make it impossible to assess whether the reported 246% figure is accurate, reproducible, or even well-defined.
minor comments (3)
  1. [Abstract, third sentence] The phrase 'when the liquid in the full nucleate boiling state' is grammatically incomplete; 'is' appears to be missing before 'in'.
  2. [Abstract, second sentence] The abstract does not define the baseline against which the 246% enhancement is computed; a clear definition of the reference configuration is needed regardless of the body text.
  3. [Abstract, fourth sentence] The mention of shadowgraphy and Laser Doppler Anemometry is not accompanied by any description of the optical setup, measurement volumes, or data processing, even at the level of a figure caption, in the supplied text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the abstract states an empirical enhancement claim with no derivation chain, and the attached full text is an unrelated stereo-matching paper, so no reduction of a prediction to its inputs can be exhibited.

full rationale

The submitted record contains only a four-sentence abstract on biphasic heat transfer followed by an unrelated IEEE TCSVT manuscript on unsupervised stereo matching. The heat-transfer claim ('The system achieves 246% heat transfer enhancement at constant superheat T_sup ≈ 6.4 K') is an empirical measurement claim, not an equation-derived prediction: no fitted parameter is defined in terms of the enhancement, no quantity is renamed, and no result is justified by a self-citation chain. The mechanism sentence ('we validate that the bubbles and biphasic particles induced agitation enhances the heat flux') is a causal interpretation of shadowgraphy/LDA observations; interpreting imagery is not circular reduction. Because the attached full text contains neither the experimental apparatus, the matched single-phase baseline, nor the shadowgraphy/LDA analysis, the enhancement claim is unverifiable in this record, and the absence of the baseline is a correctness/evidence gap; but per the hard rule requiring an explicit Eq. X = Eq. Y reduction before flagging circularity, no circular step can be identified. I therefore score 0, noting that this is a non-finding on circularity, not an endorsement of the empirical claim.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

No derivation or model is present in the reviewable content. The abstract rests on the assumed validity of the baseline comparison, on the mechanistic attribution to bubble agitation rather than mixture property changes, and on the self-sustained character of the state. The HFE-7000 volume fraction and the superheat are selected operating conditions, not fitted constants.

free parameters (2)
  • HFE-7000 volume fraction = 2%
    Chosen operating parameter in the abstract; the enhancement claim is tied to this specific loading and no concentration sweep is reported in the abstract.
  • Superheat operating point = ≈6.4 K
    The 246% enhancement is reported at this constant superheat; the dependence of enhancement on superheat is not given in the abstract.
assumptions (3)
  • domain assumption The 246% enhancement is measured against an appropriate single-phase baseline at the same superheat.
    The abstract reports enhancement at constant superheat but does not describe the baseline condition. If the baseline is not a matched water-only run, the percentage is not interpretable.
  • domain assumption The enhancement is caused by biphasic bubble agitation rather than by changes in the working fluid's thermophysical properties.
    The abstract attributes the heat flux increase to bubble-induced agitation, but no control separating additive effects from agitation effects is mentioned.
  • domain assumption The pseudo-turbulent state is self-sustained with no hidden energy input beyond the maintained superheat.
    The abstract calls the state self-sustained, but the reported enhancement alone does not prove the absence of external driving or geometry-specific assistance.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Active biphasic heat transfer enhancement in vertical natural convection." pith.science (2026). https://pith.science/paper/7BOZ3MJX

@misc{pith2026250801262,
  author       = {Pith},
  title        = {Pith review of: Active biphasic heat transfer enhancement in vertical natural convection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7BOZ3MJX}},
  note         = {Machine review of arXiv:2508.01262}
}
abstract

Vertical natural convection (VC), often cannot meet the high heat transfer demands due to the inherent misalignment of the direction of buoyancy (vertical) with the direction of the heat transfer (horizontal). Here we applied a novel strategy on a water based VC system to enhance the heat transfer. By adding 2% of the total volume with a low-boiling-temperature liquid (HFE-7000) and introducing a gas-liquid layer on top of the VC cell, we create a self-sustained state of pseudo-turbulence with evaporating, circulating and condensing biphasic bubbles. The system achieves 246% heat transfer enhancement at constant superheat $T_{sup}\approx6.4 \text{K}$ when the liquid in the full nucleate boiling state. Using shadowgraphy and Laser Doppler Anemometry (LDA) methods, we validate that the bubbles and biphasic particles induced agitation enhances the heat flux and modifies the temperature field of the heat transfer.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

65 extracted references · 62 canonical work pages

  1. [1]

    Playing to vision foundation model’s strengths in stereo matching,

    C.-W. Liu et al. , “Playing to vision foundation model’s strengths in stereo matching,” IEEE Transactions on Intelligent Vehicles , 2024, DOI:10.1109/TIV .2024.3467287

    arXiv 2024

  2. [2]

    SC-DepthV3: Robust self-supervised monocular depth estimation for dynamic scenes,

    L. Sun et al. , “SC-DepthV3: Robust self-supervised monocular depth estimation for dynamic scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 1, pp. 497–508, 2023

    work page 2023

  3. [3]

    A resource-efficient pipelined architecture for real- time semi-global stereo matching,

    Z. Lu et al. , “A resource-efficient pipelined architecture for real- time semi-global stereo matching,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 660–673, 2021

    work page 2021

  4. [4]

    Deep stereo network with mrf-based cost aggregation,

    K. Zeng et al., “Deep stereo network with mrf-based cost aggregation,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 4, pp. 2426–2438, 2023

    work page 2023

  5. [5]

    Stereo matching: fundamentals, state-of-the-art, and existing challenges,

    C.-W. Liu et al. , “Stereo matching: fundamentals, state-of-the-art, and existing challenges,” in Autonomous Driving Perception: Fundamentals and Applications. Springer, 2023, pp. 63–100

    work page 2023

  6. [6]

    Road surface 3D reconstruction based on dense subpixel disparity map estimation,

    R. Fan et al., “Road surface 3D reconstruction based on dense subpixel disparity map estimation,” IEEE Transactions on Image Processing , vol. 27, no. 6, pp. 3025–3035, 2018

    work page 2018

  7. [7]

    Learning collision-free space detection from stereo im- ages: Homography matrix brings better data augmentation,

    R. Fan et al. , “Learning collision-free space detection from stereo im- ages: Homography matrix brings better data augmentation,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 225–233, 2021

    work page 2021

  8. [8]

    SNE-RoadSeg: Incorporating surface normal information into semantic segmentation for accurate freespace detection,

    R. Fan et al., “SNE-RoadSeg: Incorporating surface normal information into semantic segmentation for accurate freespace detection,” in Euro- pean Conference on Computer Vision (ECCV) . Springer, 2020, pp. 340–356

    work page 2020

Show all 65 references
  1. [9]

    A low-cost and scalable framework to build large-scale localization benchmark for augmented reality,

    H. Liu et al. , “A low-cost and scalable framework to build large-scale localization benchmark for augmented reality,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 4, pp. 2274– 2288, 2023

    2023

  2. [10]

    MASIC: Deep mask stereo image compression,

    X. Deng et al., “MASIC: Deep mask stereo image compression,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 10, pp. 6026–6040, 2023

    2023

  3. [11]

    WHU-Stereo: A challenging benchmark for stereo matching of high-resolution satellite images,

    S. Li et al., “WHU-Stereo: A challenging benchmark for stereo matching of high-resolution satellite images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–14, 2023

    2023

  4. [12]

    Pothole detection based on disparity transformation and road surface modeling,

    R. Fan et al. , “Pothole detection based on disparity transformation and road surface modeling,” IEEE Transactions on Image Processing , vol. 29, pp. 897–908, 2019

    2019

  5. [13]

    Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms,

    R. Fan et al., “Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms,” IEEE Transac- tions on Image Processing , vol. 30, pp. 8144–8154, 2021

    2021

  6. [14]

    A high-throughput depth estimation processor for accurate semiglobal stereo matching using pipelined inter-pixel aggregation,

    Y . Lee and H. Kim, “A high-throughput depth estimation processor for accurate semiglobal stereo matching using pipelined inter-pixel aggregation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 1, pp. 411–422, 2021

    2021

  7. [15]

    Unambiguous pyramid cost volumes fusion for stereo matching,

    Q. Chen et al., “Unambiguous pyramid cost volumes fusion for stereo matching,” IEEE Transactions on Circuits and Systems for Video Tech- nology, vol. 34, no. 10, pp. 9223–9236, 2023

    2023

  8. [16]

    Rethinking road surface 3-D reconstruction and pothole detection: From perspective transformation to disparity map segmenta- tion,

    R. Fan et al., “Rethinking road surface 3-D reconstruction and pothole detection: From perspective transformation to disparity map segmenta- tion,” IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 5799–5808, 2022

    2022

  9. [17]

    The farther the better: Balanced stereo matching via depth-based sampling and adaptive feature refinement,

    H. Zhang et al. , “The farther the better: Balanced stereo matching via depth-based sampling and adaptive feature refinement,” IEEE Transac- tions on Circuits and Systems for Video Technology , vol. 32, no. 7, pp. 4613–4625, 2021

    2021

  10. [18]

    Inter-scale similarity guided cost aggregation for stereo matching,

    P. Li et al. , “Inter-scale similarity guided cost aggregation for stereo matching,” IEEE Transactions on Circuits and Systems for Video Tech- nology, vol. 35, no. 1, pp. 134–147, 2024

    2024

  11. [19]

    Robust scale-aware stereo matching network,

    J. Okae et al. , “Robust scale-aware stereo matching network,” IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 244–253, 2021

    2021

  12. [20]

    A decomposition model for stereo matching,

    C. Yao et al. , “A decomposition model for stereo matching,” in Pro- ceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2021, pp. 6091–6100

    2021

  13. [21]

    Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail,

    L. Bartolomei et al. , “Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail,” in Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) , 2025, pp. 1013–1027

    2025

  14. [22]

    Monster: Marry monodepth to stereo unleashes power,

    J. Cheng et al., “Monster: Marry monodepth to stereo unleashes power,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 6273–6282

    2025

  15. [23]

    Unsupervised stereo matching using confidential correspondence consistency,

    S. Joung et al. , “Unsupervised stereo matching using confidential correspondence consistency,” IEEE Transactions on Intelligent Trans- portation Systems, vol. 21, no. 5, pp. 2190–2203, 2019

    2019

  16. [24]

    SegStereo: Exploiting semantic information for disparity estimation,

    G. Yang et al., “SegStereo: Exploiting semantic information for disparity estimation,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 636–651

    2018

  17. [25]

    SUW-learn: Joint supervised, unsupervised, weakly supervised deep learning for monocular depth estimation,

    H. Ren et al. , “SUW-learn: Joint supervised, unsupervised, weakly supervised deep learning for monocular depth estimation,” in Proceed- 12 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition...

    2020

  18. [26]

    Unsupervised deep event stereo for depth estima- tion,

    S. N. Uddin et al. , “Unsupervised deep event stereo for depth estima- tion,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 11, pp. 7489–7504, 2022

    2022

  19. [27]

    Self-supervised learning of PSMNet via generative adversarial networks,

    X. Yang et al. , “Self-supervised learning of PSMNet via generative adversarial networks,” in International Conference on Intelligent Com- puting (ICIC). Springer, 2024, pp. 469–479

    2024

  20. [28]

    Self-supervised learning for stereo matching with self-improving ability,

    Y . Zhong et al. , “Self-supervised learning for stereo matching with self-improving ability,” Computing Research Repository (CoRR) , vol. abs/1709.00930, 2017. [Online]. Available: https://arxiv.org/abs/1709. 00930

    2017 arXiv

  21. [29]

    Occlusion aware stereo matching via coopera- tive unsupervised learning,

    A. Li and Z. Yuan, “Occlusion aware stereo matching via coopera- tive unsupervised learning,” in Asian Conference on Computer Vision . Springer, 2018, pp. 197–213

    2018

  22. [30]

    Unsupervised occlusion-aware stereo matching with directed disparity smoothing,

    A. Li et al. , “Unsupervised occlusion-aware stereo matching with directed disparity smoothing,” IEEE Transactions on Intelligent Trans- portation Systems, vol. 23, no. 7, pp. 7457–7468, 2021

    2021

  23. [31]

    Co-Teaching: An ark to unsupervised stereo matching,

    H. Wang et al., “Co-Teaching: An ark to unsupervised stereo matching,” in 2021 IEEE International Conference on Image Processing (ICIP) . IEEE, 2021, pp. 3328–3332

    2021

  24. [32]

    Flow2Stereo: Effective self-supervised learning of optical flow and stereo matching,

    P. Liu et al., “Flow2Stereo: Effective self-supervised learning of optical flow and stereo matching,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 6648– 6657

    2020

  25. [33]

    On the confidence of stereo matching in a deep-learning era: a quantitative evaluation,

    M. Poggi et al., “On the confidence of stereo matching in a deep-learning era: a quantitative evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 9, pp. 5293–5313, 2021

    2021

  26. [34]

    A quantitative evaluation of confidence measures for stereo vision,

    X. Hu and P. Mordohai, “A quantitative evaluation of confidence measures for stereo vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2121–2133, 2012

    2012

  27. [35]

    Quantitative evaluation of confidence measures in a machine learning world,

    M. Poggi et al. , “Quantitative evaluation of confidence measures in a machine learning world,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 5228–5237

    2017

  28. [36]

    Learning a general-purpose confidence measure based on O(1) features and a smarter aggregation strategy for semi global matching,

    M. Poggi and S. Mattoccia, “Learning a general-purpose confidence measure based on O(1) features and a smarter aggregation strategy for semi global matching,” in 2016 Fourth International Conference on 3D Vision (3DV). IEEE, 2016, pp. 509–518

    2016

  29. [37]

    Learning a confidence measure in the disparity domain from O(1) features,

    M. Poggi et al., “Learning a confidence measure in the disparity domain from O(1) features,” Computer Vision and Image Understanding , vol. 193, pp. 2905–2913, 2020

    2020

  30. [38]

    Self-adapting confidence estimation for stereo,

    M. Poggi et al. , “Self-adapting confidence estimation for stereo,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 715–733

    2020

  31. [39]

    Learning from scratch a confidence measure,

    M. Poggi and S. Mattoccia, “Learning from scratch a confidence measure,” in Proceedings of 27th British Machine Vision Conference (BMVC), 2016, pp. 1–13

    2016

  32. [40]

    Beyond local reasoning for stereo confidence estimation with deep learning,

    F. Tosi et al., “Beyond local reasoning for stereo confidence estimation with deep learning,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 319–334

    2018

  33. [41]

    U-Net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger et al., “U-Net: Convolutional networks for biomedical image segmentation,” in Proceddings of the Medical Image Computing and Cmputer-Assisted Intervention (MICCAI). Springer, 2015, pp. 234– 241

    2015

  34. [42]

    Learning the distribution of errors in stereo matching for joint disparity and uncertainty estimation,

    L. Chen et al. , “Learning the distribution of errors in stereo matching for joint disparity and uncertainty estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 17 235–17 244

    2023

  35. [43]

    Learning confidence measures in the wild,

    F. Tosi et al., “Learning confidence measures in the wild,” inProceedings of 28th British Machine Vision Conference (BMVC) , 2017, pp. 1–13

    2017

  36. [44]

    Learning and selecting confidence measures for robust stereo matching,

    M.-G. Park and K.-J. Yoon, “Learning and selecting confidence measures for robust stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 6, pp. 1397–1411, 2018

    2018

  37. [45]

    Using self-contradiction to learn confidence mea- sures in stereo vision,

    C. Mostegel et al. , “Using self-contradiction to learn confidence mea- sures in stereo vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 4067– 4076

    2016

  38. [46]

    Vision Transformer adapter for dense predictions,

    Z. Chen et al. , “Vision Transformer adapter for dense predictions,” in International Conference on Learning Representations (ICLR) , 2023

    2023

  39. [47]

    Depth Anything V2,

    L. Yang et al., “Depth Anything V2,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 37, pp. 21 875–21 911, 2024

    2024

  40. [48]

    Segment anything,

    A. Kirillov et al., “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 4015– 4026

    2023

  41. [49]

    Depth Anything: Unleashing the power of large- scale unlabeled data,

    L. Yang et al. , “Depth Anything: Unleashing the power of large- scale unlabeled data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 10 371– 10 381

    2024

  42. [50]

    Stereo without epipolar lines: A maximum-flow formulation,

    S. Roy, “Stereo without epipolar lines: A maximum-flow formulation,” International Journal of Computer Vision , vol. 34, no. 2, pp. 147–161, 1999

    1999

  43. [51]

    These maps are made by propagation: Adapting deep stereo networks to road scenarios with decisive disparity diffusion,

    C.-W. Liu et al., “These maps are made by propagation: Adapting deep stereo networks to road scenarios with decisive disparity diffusion,” IEEE Transactions on Image Processing, vol. 34, pp. 1516–1528, 2025

    2025

  44. [52]

    PVStereo: Pyramid voting module for end-to-end self- supervised stereo matching,

    H. Wang et al., “PVStereo: Pyramid voting module for end-to-end self- supervised stereo matching,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4353–4360, 2021

    2021

  45. [53]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang et al. , “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004

    2004

  46. [54]

    A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,

    N. Mayer et al. , “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 4040–4048

    2016

  47. [55]

    Virtual KITTI 2,

    Y . Cabon et al. , “Virtual KITTI 2,” Computing Research Repository (CoRR), vol. abs/2001.10773, 2020. [Online]. Available: https: //arxiv.org/abs/2001.10773

    2001 arXiv

  48. [56]

    Are we ready for autonomous driving? the KITTI vision benchmark suite,

    A. Geiger et al. , “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2012, pp. 3354–3361

    2012

  49. [57]

    Object scene flow for autonomous vehicles,

    M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3061–3070

    2015

  50. [58]

    High-resolution stereo datasets with subpixel- accurate ground truth,

    D. Scharstein et al. , “High-resolution stereo datasets with subpixel- accurate ground truth,” in Pattern Recognition: 36th German Conference (GCPR). Springer, 2014, pp. 31–42

    2014

  51. [59]

    A multi-view stereo benchmark with high-resolution images and multi-camera videos,

    T. Schops et al., “A multi-view stereo benchmark with high-resolution images and multi-camera videos,” in Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 3260–3269

    2017

  52. [60]

    Iterative geometry encoding volume for stereo matching,

    G. Xu et al., “Iterative geometry encoding volume for stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 21 919–21 928

    2023

  53. [61]

    Practical stereo matching via cascaded recurrent network with adaptive correlation,

    J. Li et al. , “Practical stereo matching via cascaded recurrent network with adaptive correlation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 16 263– 16 272

    2022

  54. [62]

    A permutation model for the self- supervised stereo matching problem,

    P.-A. Brousseau and S. Roy, “A permutation model for the self- supervised stereo matching problem,” in 2022 19th Conference on Robots and Vision (CRV) . IEEE, 2022, pp. 122–131

    2022

  55. [63]

    Parallax attention for unsupervised stereo correspon- dence learning,

    L. Wang et al. , “Parallax attention for unsupervised stereo correspon- dence learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 4, pp. 2108–2125, 2020

    2020

  56. [64]

    Adaptive cost volume representation for unsuper- vised high-resolution stereo matching,

    K. W. Tong et al. , “Adaptive cost volume representation for unsuper- vised high-resolution stereo matching,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 1, pp. 912–922, 2022

    2022

  57. [65]

    A novel cell structure-based disparity estimation for unsupervised stereo matching,

    X. Cheng et al. , “A novel cell structure-based disparity estimation for unsupervised stereo matching,” IET Image Processing , vol. 16, no. 6, pp. 1678–1693, 2022. Chuang-Wei Liu received his B.E. degree in au- tomation from Tongji University in 2020. He is currently pursuing ...

    2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.