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REVIEW 3 major objections 3 minor 43 references

EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images

T0 review · 3 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read EndoUFM claims that adapting two pretrained foundation models with lightweight random-vector low-rank adapters produces state-of-the-art unsupervised monocular depth estimates for endoscopic scenes while keeping the model small.

desk verdict EndoUFM is a plausible assembly of known components with a SOTA claim on four endoscopic benchmarks, but our copy is illegible and contains a stray arXiv header, so the claim rests entirely on an abstract with no numbers. read the letter →

arxiv 2508.17916 v2 pith:VDDF2MEA submitted 2025-08-25 cs.CV

Xinning Yao , Bo Liu , Bojian Li , Jingjing Wang , Jinghua Yue , Fugen Zhou This is my paper · ORCID
classification cs.CV
keywords monoculardepthestimationendoscopicimagingfoundationmodelslow-rankadaptationunsupervisedlearningdepthwiseseparableconvolutionsurgicalnavigation3Dreconstruction
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 sets out to prove that monocular depth estimation inside endoscopic surgery can be made substantially more accurate by exploiting two pretrained foundation models rather than training a depth network from scratch or from a single generic backbone. It introduces EndoUFM, a fully unsupervised framework that adapts the foundation models with Random Vector Low-Rank Adaptation (RVLoRA), adds a residual depthwise-separable convolution block for local detail, and regularizes with a mask-guided smoothness loss. If the reported results hold, surgeons would get denser and more reliable depth cues from ordinary endoscope video, which matters for AR overlays, navigation, and safer minimally invasive procedures.

What carries the argument

The framework pairs two pretrained foundation models as feature extractors, adapts them with Random Vector Low-Rank Adaptation (RVLoRA), a low-rank adaptation scheme that keeps the number of trainable parameters small while letting the pretrained features shift toward endoscopic statistics, and then feeds the adapted features through a Residual block built on Depthwise Separable Convolutions (Res-DSC) to capture fine local detail. A mask-guided smoothness loss regularizes depth within anatomical regions. RVLoRA preserves pretrained priors while limiting trainable parameters, Res-DSC sharpens local structure, and the mask-guided loss discourages depth jumps inside coherent tissue regions.

What would settle it

Retrain EndoUFM from randomly initialized encoders with the same RVLoRA, Res-DSC, and mask-guided smoothness loss on the same four datasets; if the randomly initialized model matches the pretrained-backbone version on SCARED and Hamlyn metrics, the transferability premise is falsified. Alternatively, test on an endoscopic domain outside the training distribution, such as a different surgical instrument or patient cohort, and check whether the margin over baselines collapses.

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Extended reading notes

Core claim

EndoUFM is an unsupervised monocular depth estimation framework that takes endoscopic frames as input and produces dense depth maps without ground-truth depth supervision. Its central claim is that two pretrained foundation models, adapted with the lightweight RVLoRA fine-tuning strategy, supply semantic and geometric priors that bridge the gap between natural-image pretraining and endoscopic scenes; the Res-DSC residual block then recovers fine-grained local features, and a mask-guided smoothness loss enforces depth consistency inside anatomical structures. The authors report that this combination achieves state-of-the-art results on the SCARED, Hamlyn, SERV-CT, and EndoNeRF datasets while keeping the model size efficient.

Load-bearing premise

The whole method leans on the assumption that representations learned from natural images remain useful for endoscopic scenes after only lightweight RVLoRA adaptation; if that transfer does not hold, the reported gains would disappear.

Editorial extensions

If this is right

  • If EndoUFM's reported accuracy holds, monocular endoscopic video alone can support dense 3D reconstruction, removing the need for stereo endoscopes or structured-light hardware in AR-assisted surgery.
  • Because RVLoRA leaves most foundation-model weights frozen, the framework's efficient footprint makes it plausible to run depth estimation in near-real-time on clinical hardware.
  • The mask-guided smoothness loss ties depth estimates to anatomical regions, which should reduce spurious depth jumps at tissue boundaries and improve the stability of 3D overlays.
  • On the four benchmark datasets, EndoUFM is claimed to outperform existing monocular depth estimators while using fewer parameters, giving a practical baseline for future endoscopic depth methods.

Reading between the lines

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

  • Editorial inference: The same dual-foundation-model plus RVLoRA recipe could plausibly transfer to other narrow-domain medical imagery such as capsule endoscopy, arthroscopy, or fetoscopy, where illumination and texture also differ sharply from natural images; the paper does not test this.
  • Editorial inference: Because the loss is mask-guided, EndoUFM's benefit depends on the availability or quality of anatomical masks, so a natural extension would measure how performance degrades as mask noise increases.
  • Editorial inference: The benchmark claim is limited to the four datasets tested; a harder, untested question is whether the margin survives cross-patient, cross-instrument, or unseen-disease generalization.
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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 / 3 minor

Summary. The paper proposes EndoUFM, an unsupervised monocular depth estimation framework for endoscopic images that combines two foundation models with a Random Vector Low-Rank Adaptation (RVLoRA) fine-tuning strategy, a Residual block based on Depthwise Separable Convolution (Res-DSC), and a mask-guided smoothness loss. The abstract claims state-of-the-art performance on SCARED, Hamlyn, SERV-CT, and EndoNeRF datasets while maintaining an efficient model size, and it provides a public code repository. However, the supplied full text is corrupted and unreadable, and the abstract contains no quantitative results, comparison tables, or ablations, so the central empirical claims cannot be verified from the materials provided.

Significance. If the claimed state-of-the-art results are reproducible, EndoUFM would be a practically relevant contribution to surgical depth estimation, offering a concrete recipe for transferring natural-image foundation models to the endoscopic domain. The release of code at https://github.com/RealMindyY/EndoUFM is a positive step for reproducibility. The problem of domain shift between pretraining and endoscopic images is well identified, and the combination of low-rank adaptation, depthwise separable residual blocks, and a mask-guided smoothness loss is a plausible design. Nevertheless, because the contribution is empirical and the supporting evidence is currently inaccessible, the significance of the paper cannot be assessed until a legible manuscript with actual numbers is provided.

major comments (3)
  1. [Abstract] The central claim that the method "achieves state-of-the-art performance" is unsupported in the abstract and in the supplied text: no numerical metrics (e.g., Abs Rel, RMSE, delta thresholds), no baseline comparisons, no ablations, and no error bars are reported anywhere. Since the paper's contribution is empirical, the abstract should state concrete results for at least one dataset and the body must contain full comparison tables with the same evaluation protocols as the cited baselines.
  2. [Full text (unreadable copy)] The provided full text is corrupted mojibake that cannot be parsed into equations, tables, or experimental descriptions. In addition, the body embeds an unrelated arXiv identifier, "arXiv:2508.17908v2 [cond-mat.stat-mech] 3 Mar 2026," which indicates that the supplied copy is not a trustworthy version of the submission. This makes it impossible to check the method details, training protocol, implementation, or the claimed experiments. The authors must resubmit a clean, completely readable manuscript before the technical content can be reviewed.
  3. [Abstract (efficiency claim)] The phrase "maintaining an efficient model size" is not quantified. To support this claim, the paper must report parameter counts, FLOPs, or inference time for EndoUFM and for all compared baselines. Additionally, the individual contribution of RVLoRA, Res-DSC, and the mask-guided smoothness loss should be demonstrated through an ablation study; no such ablation is visible in the supplied materials.
minor comments (3)
  1. [Abstract] The underlined letter in "U̲tilizes" appears to be a formatting artifact; the abstract should be plain text.
  2. [Abstract] The term "Random Vector Low-Rank Adaptation (RVLoRA)" is not defined in the abstract; a one-sentence description or a reference is needed for readers unfamiliar with this variant.
  3. [General] The supplied document lacks a clear section structure with page numbers, making it hard to navigate even after decoding; the final version should follow the journal's formatting guidelines.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the empirical SOTA claim rests on external benchmarks and no fit-to-prediction loop is visible; the corrupted full text is a verifiability issue, not circularity.

full rationale

The supplied material contains only the abstract in readable form; the full text is mojibake and even carries an unrelated arXiv identifier (arXiv:2508.17908v2 [cond-mat.stat-mech]). No derivation chain is recoverable, so no equation-level circularity can be exhibited. The central claim is empirical: 'Extensive experiments on the SCARED, Hamlyn, SERV-CT, and EndoNeRF datasets confirm that our method achieves state-of-the-art performance while maintaining an efficient model size.' That claim is benchmark-against-external-datasets in nature. The named components, RVLoRA, Res-DSC, and the mask-guided smoothness loss, are presented as architectural or regularization elements rather than as quantities defined in terms of the predicted depth output. Nothing in the abstract or the readable text shows a fitted parameter renamed as a prediction, a self-citation used to force a uniqueness claim, or an ansatz smuggled in via citation. The corrupted text and inserted unrelated header are paper-integrity and verifiability concerns, not circularity. Under the hard rule that circularity must be demonstrated by quoted equations or explicit reduction, no such demonstration is possible here. I therefore report no significant circularity.

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

Only the abstract was available. The listed axioms are implicit domain assumptions the method depends on; no free parameters or invented entities are named in the abstract.

assumptions (3)
  • domain assumption Foundation models pretrained on natural images encode priors that transfer to endoscopic scenes after lightweight adaptation.
    The abstract states the method 'leverages the powerful pre-learned priors' and focuses on reducing the domain gap, implying this transfer is assumed to be beneficial.
  • domain assumption Anatomical structures in endoscopic images have locally consistent depth that can be regularized by a mask-guided smoothness loss.
    The mask-guided smoothness loss is introduced to 'enforce depth consistency within anatomical structures', which presumes the ground-truth or estimated structure boundaries are known and correspond to smooth depth regions.
  • domain assumption The four evaluation datasets (SCARED, Hamlyn, SERV-CT, EndoNeRF) are representative of general endoscopic surgical conditions.
    The abstract claims generalization to 'minimally invasive endoscopic surgeries' based on these datasets alone, without discussing potential biases across different surgical sites or imaging systems.

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Cite this review

Pith. "Pith review of EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images." pith.science (2026). https://pith.science/paper/VDDF2MEA

@misc{pith2026250817916,
  author       = {Pith},
  title        = {Pith review of: EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VDDF2MEA}},
  note         = {Machine review of arXiv:2508.17916}
}
read the original abstract

Depth estimation is a foundational component for 3D reconstruction in minimally invasive endoscopic surgeries. However, existing monocular depth estimation techniques often exhibit limited performance to the varying illumination and complex textures of the surgical environment. While applying foundation models offers a promising approach to enhance the depth estimation performance, the domain gap between the natural images used for pre-training and the target endoscopic images leads to significant semantic perception deficiencies. In this study, EndoUFM is introduced as an unsupervised monocular depth estimation framework that innovatively \underline{U}tilizes dual Foundation Models for Endoscopic images, thereby enhancing the depth estimation performance by leveraging the powerful pre-learned priors. The framework features a novel adaptive fine-tuning strategy that incorporates Random Vector Low-Rank Adaptation (RVLoRA) to enhance model adaptability, and a Residual block based on Depthwise Separable Convolution (Res-DSC) to improve the capture of fine-grained local features. A mask-guided smoothness loss is also introduced to enforce depth consistency within anatomical structures. Extensive experiments on the SCARED, Hamlyn, SERV-CT, and EndoNeRF datasets confirm that our method achieves state-of-the-art performance while maintaining an efficient model size. This work contributes to augmenting surgeons' spatial perception during minimally invasive procedures, thereby enhancing surgical precision and safety, with crucial implications for augmented reality and navigation systems. Our code is available at https://github.com/RealMindyY/EndoUFM.

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Works this paper leans on

43 extracted references · 40 canonical work pages

  1. [1]

    #l unVL :V )!9!`RZ`@B_ ^ Q D? _ R2 1ڜNUgҴ jO +J e2Ƣɓ : Uǧ VQR u@WF fFB_FW ]7m2*t1l l9FC< 5`J a_GZH2 ;)vsݚfg`Lj c D? o/dx/ = <

    11em plus .33em minus .07em @technote 4000 4000 100 4000 4000 500 `\.=1000 = #1 #1 #1 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEauthorblockAstyle \@IEEEauthordefaulttextstyle \@IEEEauthorblockconfadjspace -0.25em \@IEEEauthorblockNtopspace 0.0ex \@IEEEauthorblockAtopspace 0.0ex \@IEEEauthorblockNinterlinespace 2.6ex \@IEEEauthorblockAinte...

    work page 1947

  2. [2]

    A. K. Sharma, R. G. de Oliveira, S. Suvithayasiri, P. Chavalparit, C. C. Chang, Y. H. Kim, C. R. Fischer, S. Lee, S. Cho, J.-S. Kim, et al. , ``The utilization of navigation and emerging technologies with endoscopic spine surgery: A narrative review,'' Neurospine , vol. 22, no. 1, p. 105, 2025

    work page 2025

  3. [3]

    Z. Yang, J. Pan, J. Dai, Z. Sun, and Y. Xiao, ``Self-supervised lightweight depth estimation in endoscopy combining cnn and transformer,'' IEEE Transactions on Medical Imaging , vol. 43, no. 5, pp. 1934--1944, 2024

    work page 1934

  4. [4]

    Z. Liu, C. Song, J. Cheng, J. Luo, and X. Wang, ``Self-supervised monocular depth estimation with effective feature fusion and self distillation,'' in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 7160--7166, IEEE, 2024

    work page 2024

  5. [5]

    T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, ``Unsupervised learning of depth and ego-motion from video,'' in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 1851--1858, 2017

    work page 2017

  6. [6]

    L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, ``Depth anything: Unleashing the power of large-scale unlabeled data,'' in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 10371--10381, 2024

    work page 2024

  7. [7]

    K. B. Ozyoruk, G. I. Gokceler, T. L. Bobrow, G. Coskun, K. Incetan, Y. Almalioglu, F. Mahmood, E. Curto, L. Perdigoto, M. Oliveira, et al. , ``Endoslam dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos,'' Medical image analysis , vol. 71, p. 102058, 2021

    work page 2021

  8. [8]

    B. Li, B. Liu, M. Zhu, X. Luo, and F. Zhou, ``Image intrinsic-based unsupervised monocular depth estimation in endoscopy,'' IEEE Journal of Biomedical and Health Informatics , 2024

    work page 2024

Show all 43 references
  1. [9]

    Godard, O

    C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, ``Digging into self-supervised monocular depth estimation,'' in Proceedings of the IEEE/CVF international conference on computer vision , pp. 3828--3838, 2019

    2019

  2. [10]

    S. Shao, Z. Pei, W. Chen, W. Zhu, X. Wu, D. Sun, and B. Zhang, ``Self-supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue,'' Medical image analysis , vol. 77, p. 102338, 2022

    2022

  3. [11]

    J. J. Han, A. Acar, C. Henry, and J. Y. Wu, ``Depth anything in medical images: A comparative study,'' arXiv preprint arXiv:2401.16600 , 2024

    2024 arXiv

  4. [12]

    J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, ``Segment anything in medical images,'' Nature Communications , vol. 15, no. 1, p. 654, 2024

    2024

  5. [13]

    E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, ``Lo RA : Low-rank adaptation of large language models,'' in International Conference on Learning Representations , 2022

    2022

  6. [14]

    Z. Wang, Y. Zhou, S. He, T. Li, F. Huang, Q. Ding, X. Feng, M. Liu, and Q. Li, ``Monopcc: Photometric-invariant cycle constraint for monocular depth estimation of endoscopic images,'' Medical Image Analysis , vol. 102, p. 103534, 2025

    2025

  7. [15]

    V. M. Batlle, J. M. Montiel, and J. D. Tard \'o s, ``Photometric single-view dense 3d reconstruction in endoscopy,'' in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 4904--4910, IEEE, 2022

    2022

  8. [16]

    Ranftl, K

    R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, ``Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,'' IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 3, pp. 1623--1637, 2020

    2020

  9. [17]

    Oquab, T

    M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, et al. , ``Dinov2: Learning robust visual features without supervision,'' Transactions on Machine Learning Research Journal , pp. 1--31, 2024

    2024

  10. [18]

    Q. Tian, Z. Chen, H. Liao, X. Huang, L. Li, S. Ourselin, and H. Liu, ``Endoomni: Zero-shot cross-dataset depth estimation in endoscopy by robust self-learning from noisy labels,'' arXiv preprint arXiv:2409.05442 , 2024

    2024 arXiv

  11. [19]

    B. Cui, M. Islam, L. Bai, and H. Ren, ``Surgical-dino: adapter learning of foundation models for depth estimation in endoscopic surgery,'' International Journal of Computer Assisted Radiology and Surgery , vol. 19, no. 6, pp. 1013--1020, 2024

    2024

  12. [20]

    B. Cui, M. Islam, L. Bai, A. Wang, and H. Ren, ``Endodac: Efficient adapting foundation model for self-supervised depth estimation from any endoscopic camera,'' in International Conference on Medical Image Computing and Computer-Assisted Intervention , pp. 208--218, Springer, 2024

    2024

  13. [21]

    Z. Yang, J. Pan, J. Dai, Z. Sun, and Y. Xiao, ``Self-supervised endoscopy depth estimation framework with clip-guidance segmentation,'' Biomedical Signal Processing and Control , vol. 95, p. 106410, 2024

    2024

  14. [22]

    Radford, J

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , ``Learning transferable visual models from natural language supervision,'' in International conference on machine learning , pp. 8748--8763, PmLR, 2021

    2021

  15. [23]

    Hayou, N

    S. Hayou, N. Ghosh, and B. Yu, ``Lora+: Efficient low rank adaptation of large models,'' in International Conference on Machine Learning , pp. 17783--17806, PMLR, 2024

    2024

  16. [24]

    Zhang, M

    Q. Zhang, M. Chen, A. Bukharin, P. He, Y. Cheng, W. Chen, and T. Zhao, ``Adaptive budget allocation for parameter-efficient fine-tuning,'' in 11th International Conference on Learning Representations, ICLR 2023 , 2023

    2023

  17. [25]

    D. J. Kopiczko, T. Blankevoort, and Y. M. Asano, ``Ve RA : Vector-based random matrix adaptation,'' in 12th International Conference on Learning Representations , 2024

    2024

  18. [26]

    Kirillov, E

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. , ``Segment anything,'' in Proceedings of the IEEE/CVF international conference on computer vision , pp. 4015--4026, 2023

    2023

  19. [27]

    Aghajanyan, S

    A. Aghajanyan, S. Gupta, and L. Zettlemoyer, ``Intrinsic dimensionality explains the effectiveness of language model fine-tuning,'' in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural...

    2021

  20. [28]

    K. Lu, A. Grover, P. Abbeel, and I. Mordatch, ``Frozen pretrained transformers as universal computation engines,'' in Proceedings of the AAAI conference on artificial intelligence , vol. 36, pp. 7628--7636, 2022

    2022

  21. [29]

    Frankle, D

    J. Frankle, D. J. Schwab, and A. S. Morcos, ``Training batchnorm and only batchnorm: On the expressive power of random features in \ cnn \ s,'' in International Conference on Learning Representations , 2021

    2021

  22. [30]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,'' in Proceedings of the IEEE international conference on computer vision , pp. 1026--1034, 2015

    2015

  23. [31]

    Shvets, D

    M. Shvets, D. Zhao, M. Niethammer, R. Sengupta, and A. C. Berg, ``Joint depth prediction and semantic segmentation with multi-view sam,'' in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pp. 1328--1338, 2024

    2024

  24. [32]

    S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, ``Cbam: Convolutional block attention module,'' in Proceedings of the European conference on computer vision (ECCV) , pp. 3--19, 2018

    2018

  25. [33]

    Krebs, Y

    A. Krebs, Y. Benezeth, and F. Marzani, ``Intrinsic image decomposition as two independent deconvolution problems,'' Signal Processing: Image Communication , vol. 86, p. 115872, 2020

    2020

  26. [34]

    Allan, J

    M. Allan, J. Mcleod, C. Wang, J. C. Rosenthal, Z. Hu, N. Gard, P. Eisert, K. X. Fu, T. Zeffiro, W. Xia, et al. , ``Stereo correspondence and reconstruction of endoscopic data challenge,'' arXiv preprint arXiv:2101.01133 , 2021

    2021 arXiv

  27. [35]

    Mountney, D

    P. Mountney, D. Stoyanov, and G.-Z. Yang, ``Three-dimensional tissue deformation recovery and tracking,'' IEEE Signal Processing Magazine , vol. 27, no. 4, pp. 14--24, 2010

    2010

  28. [36]

    P. E. Edwards, D. Psychogyios, S. Speidel, L. Maier-Hein, and D. Stoyanov, ``Serv-ct: A disparity dataset from cone-beam ct for validation of endoscopic 3d reconstruction,'' Medical image analysis , vol. 76, p. 102302, 2022

    2022

  29. [37]

    Y. Wang, Y. Long, S. H. Fan, and Q. Dou, ``Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery,'' in International conference on medical image computing and computer-assisted intervention , pp. 431--441, Springer, 2022

    2022

  30. [38]

    Recasens, J

    D. Recasens, J. Lamarca, J. M. F \'a cil, J. M. Montiel, and J. Civera, ``Endo-depth-and-motion: Reconstruction and tracking in endoscopic videos using depth networks and photometric constraints,'' IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7225--7232, 2021

    2021

  31. [39]

    Z. Li, X. Liu, N. Drenkow, A. Ding, F. X. Creighton, R. H. Taylor, and M. Unberath, ``Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers,'' in Proceedings of the IEEE/CVF international conference on computer vision , pp. 6197--6206, 2021

    2021

  32. [40]

    Paszke, S

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. , ``Pytorch: An imperative style, high-performance deep learning library,'' Advances in neural information processing systems , vol. 32, 2019

    2019

  33. [41]

    D. P. Kingma and J. Ba, ``Adam: A method for stochastic optimization,'' in 3rd International Conference on Learning Representa tions, ICLR 2015, ConferenceTrackProceedings , 2015

    2015

  34. [42]

    Mildenhall, P

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, ``Nerf: Representing scenes as neural radiance fields for view synthesis,'' Communications of the ACM , vol. 65, no. 1, pp. 99--106, 2021

    2021

  35. [43]

    Kerbl, G

    B. Kerbl, G. Kopanas, T. Leimk \"u hler, and G. Drettakis, ``3d gaussian splatting for real-time radiance field rendering,'' ACM Transactions on Graphics , vol. 42, no. 4, pp. 1--14, 2023

    2023

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Reviewed August 15, 2026 · model on record in the stance chip above.