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REVIEW 4 major objections 6 minor 51 references

VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read VGLD adds visual semantics to caption-based scale recovery and claims this grounding stabilizes and improves metric depth estimates from relative depth maps.

desk verdict A plausible incremental extension of RSA that delivers better benchmark numbers, but the headline disambiguation story is not supported by the paper's own ablations. read the letter →

arxiv 2505.02704 v3 pith:IRUGJXCK submitted 2025-05-05 cs.CV

Bojin Wu , Jing Chen This is my paper · ORCID
classification cs.CV
keywords monoculardepthestimationmetricscalerecoveryvisually-guideddisambiguationCLIPembeddingsdomainroutingrelativezero-shotgeneralization
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

VGLD claims that the unreliable scale information in natural-language captions can be stabilized by adding the image itself: a CLIP text encoder and a CLIP image encoder are fused and fed to a small network that predicts the global scale and shift needed to turn a relative depth map into metric depth. The paper shows that this fused approach outperforms the text-only language-guided baseline RSA on NYUv2 and KITTI across four relative depth backbones, and that the gain is largest when captions vary for the same image. It also introduces a domain router that sends the fused features to separate indoor and outdoor prediction heads, allowing one module trained on both datasets to work zero-shot on other indoor and outdoor benchmarks. The result matters because it suggests monocular depth scale can be recovered from language plus visual semantics without LiDAR, IMUs, or camera intrinsics.

What carries the argument

The load-bearing machinery is the reciprocal-linear scale transformation $\hat{d}_{\mathrm{pred}} = 1/(\hat{k}\,x + \hat{b})$, in which the scale-less relative depth map $x$ is converted to metric depth by two global scalars. The scalars come from a fused representation formed by concatenating CLIP image and text embeddings, passed through GlobalNet, a three-layer MLP that produces a 256-dimensional latent vector. A Domain Router Mechanism, an MLP classifier inspired by ZoeDepth, predicts indoor versus outdoor from that latent vector and routes it to the corresponding domain-specific prediction head. Training is stabilized by pseudo-labels $(\hat{k}_{\mathrm{lm}}, \hat{b}_{\mathrm{lm}})$ obtained from Levenberg-Marquardt fitting of ground-truth depth, added to the metric L1 loss and the domain cross-entropy loss.

What would settle it

Run VGLD on the same image paired with several captions, including a semantically wrong one such as describing an indoor scene as a street; if the predicted scale and shift remain essentially unchanged, the text branch is inert and the disambiguation mechanism is not what produces the reported accuracy.

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Extended reading notes

Core claim

The paper's central discovery is that jointly encoding the image with the caption removes most of the sensitivity that pure language-guided scale recovery has to wording. VGLD takes the relative depth map from a frozen backbone, extracts CLIP embeddings of both the caption and the image, concatenates them, and regresses two scalars $\hat{k}$ and $\hat{b}$ so that $\hat{d}_{\mathrm{pred}} = 1/(\hat{k}\,x + \hat{b})$ matches metric depth. Supervised by a pixel-wise L1 loss, a domain classification loss, and regression targets obtained by Levenberg-Marquardt fitting of ground-truth depth, the model learns to output the same scale and shift for semantically equivalent paraphrases of a scene. Across MiDaS and Depth Anything backbones on NYUv2 and KITTI, VGLD's fused text-plus-image variant reports lower Abs Rel and RMSE than the RSA baseline, and zero-shot results on SUNRGBD, DIML, and DDAD come close to LM fitting on ground truth. The paper interprets this as evidence that visual semantics disambiguate the text rather than replace it.

Load-bearing premise

The whole mechanism rests on the premise that captions carry scale-relevant information and that the image can pick the right interpretation of that information, rather than simply supplying the scale by itself.

Editorial extensions

If this is right

  • VGLD converts scale-less relative depth maps into metric depth using only a caption plus the image, without ground-truth depth at inference time.
  • Because it learns a single set of global scalars and a domain router, the same lightweight module transfers across MiDaS and Depth Anything backbones and across indoor and outdoor scenes.
  • Training on both NYUv2 and KITTI costs little accuracy per domain compared with single-dataset training, so the module can act as a universal alignment layer.
  • Zero-shot results on SUNRGBD, DIML, and DDAD indicate the alignment generalizes to datasets not seen in training, approaching the quality of nonlinear least-squares fitting on ground-truth depth.
  • Replacing text-only input with fused image-plus-text input reduces variance across different captions of the same image, particularly for diverse indoor captions.

Reading between the lines

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

  • The paper's zero-shot tables show image-only variants often matching or beating the text-plus-image variants, which suggests the practical driver of accuracy may be visual appearance itself rather than visual disambiguation of the caption; a stronger test would be to ablate the text branch entirely.
  • Because the predicted parameters are global scalars per image, VGLD could be attached to newer relative depth backbones, including diffusion-based zero-shot depth models, without retraining the backbone.
  • The domain routing idea could be extended beyond indoor and outdoor to finer scene categories, which might further reduce scale ambiguity in mixed-domain deployment.
  • The paper's stated limitation that wrong captions still shift predictions suggests a natural next step: use visual-textual similarity to reject or down-weight captions that do not match the image.
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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 / 6 minor

Summary. The paper introduces VGLD, a lightweight post-hoc module that recovers metric depth scale and shift from a relative depth map by jointly encoding a CLIP text embedding of a caption and a CLIP image embedding, then predicting a global affine transformation. The method adds a domain router (indoor/outdoor) and a Levenberg-Marquardt pseudo-label loss to stabilize training. Experiments compare VGLD against RSA and other baselines on NYUv2, KITTI, and zero-shot SUN-RGBD, DIML, and DDAD, with ablations of the text-only, image-only, and text-plus-image variants. The stated central claim is that visual semantics disambiguate inconsistent textual descriptions, improving both accuracy and stability of scale recovery.

Significance. If the disambiguation claim were fully supported, VGLD would be a practical, low-cost contribution to monocular metric depth recovery, since it makes relative depth models metric without retraining them. The paper has clear strengths: it evaluates four relative-depth backbones, includes a domain-router ablation and an LM-loss ablation, reports computational cost, and is written with enough detail to be reproduced. However, the load-bearing mechanistic claim is currently under-supported: the image-only variant is competitive with or better than the text-plus-image variant in several zero-shot settings, the caption-robustness evidence is limited to three images per dataset, and the main tables lack error bars. The paper therefore establishes VGLD as a reasonably strong scale-recovery method, but it does not yet establish visually-guided linguistic disambiguation as an operating mechanism.

major comments (4)
  1. [Supplementary Material, Tables 11-13 (zero-shot results)] The central claim that adding the text branch to the image branch (TCI) improves over image-only (I) is not consistently supported. On DIML Indoor (Table 12), VGLD-NK-I outperforms VGLD-NK-TCI for MiDas-1 (AbsRel 0.188 vs 0.212) and for DA V1-vits (0.193 vs 0.196). On SUN-RGBD (Table 11) the MiDas-1 I variant also beats TCI (0.259 vs 0.262), while on DDAD (Table 13) TCI is better for all backbones. The margins are small and no paired significance tests or confidence intervals are reported, so the ranking of I vs TCI is not statistically meaningful. The authors should provide per-dataset paired tests across the full test sets, or explicitly temper the claim that visual grounding of text yields consistent gains.
  2. [Sensitivity to Variations in Linguistic Descriptions (Figures 3-4; Supplementary Tables 7-10)] The only direct evidence for caption robustness is based on three images per dataset with three captions each, reported without summary statistics over a broader sample. The Limitations section concedes that VGLD 'may output inconsistent results' under different descriptions and deteriorates under incorrect descriptions. This is insufficient to support the abstract's claim that VGLD 'significantly mitigates scale estimation bias caused by inconsistent or ambiguous language.' The authors should run a caption-variance study on a random subset (or the full set) of test images, reporting the distribution of predicted (k,b) and of depth errors across captions, and test whether the text-plus-image model reduces variance relative to the image-only model.
  3. [Method (Figure 2; Eq. 5)] The architecture concatenates CLIP image and text embeddings and feeds them to an MLP, with no gating, cross-attention, or other mechanism that forces the text branch to be modulated by visual content. Consequently, the network can in principle learn an image-only scale predictor while ignoring the text branch. The ablations show that the image-only variant is close to or better than the fused variant in several settings, so the 'disambiguation' claim is currently an interpretation rather than an observed property. A concrete test would be to measure, for a fixed image, the variance of predicted (k,b) across captions for the I and TCI variants, and to verify that TCI reduces this variance while also being at least as accurate; alternatively, the architecture should be revised so that visual features explicitly modulate or gate the text representation.
  4. [Experimental Settings and Tables 1, 5-6, 11-13] The main tables report point estimates without error bars or confidence intervals, even though the differences between I and TCI variants are often only a few thousandths of AbsRel (e.g., Table 1, MiDas-1 NYUv2: 0.121 vs 0.119; MiDas-1 KITTI: 0.120 vs 0.120). Supplementary Figure 10 reports seed variance for a subset of models, but this is not carried into the principal comparison tables. Without uncertainty quantification, the claimed advantage of TCI over I cannot be evaluated, and the DIML results where I beats TCI cannot be dismissed as noise. The authors should add confidence intervals (or multiple-seed mean/std) to all main and zero-shot tables, and perform paired significance tests for the I-vs-TCI and VGLD-vs-RSA comparisons.
minor comments (6)
  1. [Table 1, MiDas-2 KITTI row] The D1 value 0.942 reported for VGLD-N/K-I on KITTI is inconsistent with the more detailed Table 6, where the corresponding values are 0.733 (VGLD-K-I) and 0.740 (VGLD-NK-I); please correct the main table.
  2. [Table 4 and surrounding text] The text states a '32.1% improvement in Abs Rel on NYUv2' for the DA V1-vits backbone, but recomputing from Table 1 (RSA-N/K 0.147 vs VGLD-NK-TCI 0.112) gives approximately 23.8%, and comparing against RSA-NK (0.148) gives 24.3%; please verify the arithmetic and the reference.
  3. [Table 11] The VGLD-NK-TCI rows for DA V2-vits and DA V1-vits are identical across all metrics (0.241, 0.545, 0.433, 0.189, 0.067, 0.779, 0.967, 0.993); this is likely a copy-paste error and should be corrected.
  4. [Supplementary Tables 7 and 9] The scalar columns (pred shift, LM shift, pred scale, LM scale) are visually garbled, with values from different rows running together; the tables need reformatting so each row has one aligned value per column.
  5. [Eq. (5) and hyperparameters] The sentence 'In our experiments, we set α and β to 0.1, as is customary' is vague; since β is ablated in Table 3, the choice for α should be justified or referenced, and 'customary' should be removed unless a citation is given.
  6. [Throughout] The manuscript contains several presentation issues: the typo 'Tabel 1' in Table 4, inconsistent spelling of 'MiDaS' vs 'MiDas', and the table footnote 'robust depth estimation‡' which ends mid-phrase; please proofread the final version.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: LM pseudo-labels are a supervised training target, not a hidden reuse of the evaluation output.

full rationale

The paper's derivation chain is self-contained against external benchmarks. VGLD predicts global scalars (k, b), transforms relative depth via Eq. (1), and is trained with Lmetric against ground-truth depth plus Llm against Levenberg-Marquardt pseudo-labels (klm, blm). These pseudo-labels are fitted per training sample from ground-truth depth and relative depth; using them as regression targets is standard supervised training, not a circular definition of the output. Evaluation is on held-out NYUv2/KITTI test splits and zero-shot SUN-RGBD/DIML/DDAD sets, so the reported predictions are not constructed from the test labels. The paper invokes no uniqueness theorem and no load-bearing self-citation; references to RSA, ZoeDepth, and CLIP are external prior work. The ablations showing image-only VGLD sometimes matching or beating text+image weaken the 'disambiguation' mechanism empirically, and the limitation section concedes that inconsistent outputs can still occur, but these are correctness concerns rather than circularity. No equation reduces to its own input, and no fitted parameter is renamed as a prediction.

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

The central claim relies on supervised learning from data rather than a mathematical derivation. The main hand-chosen quantities are the loss weights and the scale-term weight in the LM loss. The key assumptions are the global linear transform model and the informativeness of CLIP features. No new physical entities are introduced.

free parameters (3)
  • Loss weight alpha for domain loss = 0.1
    Set in Eq. 5 as 'customary'; controls the balance between metric depth loss and domain classification loss. No ablation is shown for alpha.
  • Loss weight beta for LM pseudo-label loss = 0.1
    Ablated in Table 3 and shown best at 0.1. This is a hand-tuned hyperparameter that affects final accuracy.
  • Weight 10 on scale term in L_lm = 10
    Eq. 4 uses a 10x weight on the scale term because the authors observe the model is more sensitive to scale errors than shift errors. This is a hand-set constant.
assumptions (4)
  • domain assumption A single global linear transform (k,b) can map the inverse relative depth x of a frozen backbone to metric depth.
    Used throughout, Eq. 1. The LM-fit oracle rows in Tables 5-6 support this approximately, but it is not proven for all scenes or backbones.
  • domain assumption CLIP image and text embeddings contain enough information to predict the optimal (k,b).
    The entire predictor rests on this. It is empirically validated only on the two training datasets and three zero-shot sets, with no theoretical guarantee.
  • ad hoc to paper Visual features can resolve inconsistencies in text captions without discarding useful text information.
    This is the 'visually-guided disambiguation' premise. The paper's ablations provide indirect and partly contradictory support, since image-only variants nearly match fused variants in several tables.
  • domain assumption Ground-truth domain labels (indoor/outdoor) are available during training and predictable at test time.
    The DRM is supervised with domain labels in Eq. 3. If the predicted domain is wrong at test time, the input is routed to the wrong prediction head.

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Cite this review

Pith. "Pith review of VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery." pith.science (2026). https://pith.science/paper/IRUGJXCK

@misc{pith2026250502704,
  author       = {Pith},
  title        = {Pith review of: VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IRUGJXCK}},
  note         = {Machine review of arXiv:2505.02704}
}
read the original abstract

Monocular depth estimation can be broadly categorized into two directions: relative depth estimation, which predicts normalized or inverse depth without absolute scale, and metric depth estimation, which aims to recover depth with real-world scale. While relative methods are flexible and data-efficient, their lack of metric scale limits their utility in downstream tasks. A promising solution is to infer absolute scale from textual descriptions. However, such language-based recovery is highly sensitive to natural language ambiguity, as the same image may be described differently across perspectives and styles. To address this, we introduce VGLD (Visually-Guided Linguistic Disambiguation), a framework that incorporates high-level visual semantics to resolve ambiguity in textual inputs. By jointly encoding both image and text, VGLD predicts a set of global linear transformation parameters that align relative depth maps with metric scale. This visually grounded disambiguation improves the stability and accuracy of scale estimation. We evaluate VGLD on representative models, including MiDaS and DepthAnything, using standard indoor (NYUv2) and outdoor (KITTI) benchmarks. Results show that VGLD significantly mitigates scale estimation bias caused by inconsistent or ambiguous language, achieving robust and accurate metric predictions. Moreover, when trained on multiple datasets, VGLD functions as a universal and lightweight alignment module, maintaining strong performance even in zero-shot settings. Code will be released upon acceptance.

Figures

Figures reproduced from arXiv: 2505.02704 by the authors.

Figure 1
Figure 1. As observed in the figure above, a single image [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview. We infer the scale ˆk and shift ˆb from the linguistic description and the corresponding image to transform the relative depth from the depth model into a metric depth (absolute depth in meters) prediction. Language Modality for Metric Depth Estimation Recent advances in vision-language models(Li et al. 2022; Radford et al. 2021; Jia et al. 2022), driven by large￾scale pretraining, have enabled strong cros… view at source ↗
Figure 3
Figure 3. Sensitivity to variations in linguistic descriptions on the NYUv2 dataset. We focus on the estimation results under [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Sensitivity to variations in linguistic descriptions on the KITTI dataset. Similar to Figure 3, we focus on the differences [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visualization of depth estimation on the NYUv2 dataset. The LM Fit represents the result obtained using the [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Visualization of depth estimation on the KITTI dataset. The LM Fit represents the result obtained using the Levenberg [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Zero-shot generalization on the SUN-RGBD dataset(Indoor). The models are evaluated without any fine-tuning. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Zero-shot generalization on the DIML Indoor dataset(Indoor). The models are evaluated without any fine-tuning. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Zero-shot generalization on the DDAD dataset(Outdoor). The models are evaluated without any fine-tuning. Bene [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]
Figure 10
Figure 10. Figure 10: Error bars showing performance variations across different random seeds (0, 1, 2) for Abs Rel, RMSE, and D1 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

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Reference graph

Works this paper leans on

51 extracted references · 25 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    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 mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    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 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Auty, D.; and Mikolajczyk, K. 2023. Learning to prompt clip for monocular depth estimation: Exploring the limits of human language. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2039--2047

    work page 2023

  4. [4]

    F.; Alhashim, I.; and Wonka, P

    Bhat, S. F.; Alhashim, I.; and Wonka, P. 2021. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 4009--4018

    work page 2021

  5. [5]

    F.; Alhashim, I.; and Wonka, P

    Bhat, S. F.; Alhashim, I.; and Wonka, P. 2022. Localbins: Improving depth estimation by learning local distributions. In European Conference on Computer Vision, 480--496. Springer

    work page 2022

  6. [6]

    F.; Birkl, R.; Wofk, D.; Wonka, P.; and M \"u ller, M

    Bhat, S. F.; Birkl, R.; Wofk, D.; Wonka, P.; and M \"u ller, M. 2023. Zoedepth: Zero-shot transfer by combining relative and metric depth. In arXiv preprint arXiv:2302.12288

    arXiv 2023

  7. [7]

    Cho, J.; Min, D.; Kim, Y.; and Sohn, K. 2021. DIML/CVL RGB-D dataset: 2M RGB-D images of natural indoor and outdoor scenes. In arXiv preprint arXiv:2110.11590

    arXiv 2021

  8. [8]

    Eigen, D.; Puhrsch, C.; and Fergus, R. 2014. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, volume 27

    work page 2014

Show all 51 references
  1. [9]

    Fu, X.; Yin, W.; Hu, M.; Wang, K.; Ma, Y.; Tan, P.; Shen, S.; Lin, D.; and Long, X. 2024. Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image. In European Conference on Computer Vision, 241--258. Springer

    2024

  2. [10]

    Ganj, A.; Zhao, Y.; Su, H.; and Guo, T. 2023. Mobile AR Depth Estimation: Challenges & Prospects--Extended Version. In arXiv preprint arXiv:2310.14437

    2023 arXiv

  3. [11]

    Geiger, A.; Lenz, P.; and Urtasun, R. 2012. Are we ready forAutonomous Driving. In The KITTI vision benchmark suite. InCVPR, volume 2, 5

    2012

  4. [12]

    Guizilini, V.; Ambrus, R.; Pillai, S.; Raventos, A.; and Gaidon, A. 2020. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2485--2494

    2020

  5. [13]

    Guizilini, V.; Vasiljevic, I.; Chen, D.; Ambruș, R.; and Gaidon, A. 2023. Towards zero-shot scale-aware monocular depth estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 9233--9243

    2023

  6. [14]

    Hu, M.; Yin, W.; Zhang, C.; Cai, Z.; Long, X.; Chen, H.; Wang, K.; Yu, G.; Shen, C.; and Shen, S. 2024 a . Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation. In arXiv preprint arXiv:2404.15506

    2024 arXiv

  7. [15]

    Hu, X.; Zhang, C.; Zhang, Y.; Hai, B.; Yu, K.; and He, Z. 2024 b . Learning to adapt clip for few-shot monocular depth estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 5594--5603

    2024

  8. [16]

    Ji, Y.; Chen, Z.; Xie, E.; Hong, L.; Liu, X.; Liu, Z.; Lu, T.; Li, Z.; and Luo, P. 2023. Ddp: Diffusion model for dense visual prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 21741--21752

    2023

  9. [17]

    Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022. Visual prompt tuning. In European conference on computer vision, 709--727. Springer

    2022

  10. [18]

    C.; and Schindler, K

    Ke, B.; Obukhov, A.; Huang, S.; Metzger, N.; Daudt, R. C.; and Schindler, K. 2024. Repurposing diffusion-based image generators for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9492--9502

    2024

  11. [19]

    Kim, D.; and Lee, S. 2024. CLIP Can Understand Depth. In arXiv preprint arXiv:2402.03251

    2024

  12. [20]

    Kondapaneni, N.; Marks, M.; Knott, M.; Guimaraes, R.; and Perona, P. 2024. Text-image alignment for diffusion-based perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13883--13893

    2024

  13. [21]

    F.; M \"u ller, M.; and Wonka, P

    Lavreniuk, M.; Bhat, S. F.; M \"u ller, M.; and Wonka, P. 2023. EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment. In arXiv preprint arXiv:2312.08548

    2023 arXiv

  14. [22]

    H.; Han, M.-K.; Ko, D

    Lee, J. H.; Han, M.-K.; Ko, D. W.; and Suh, I. H. 2019. From big to small: Multi-scale local planar guidance for monocular depth estimation. In arXiv preprint arXiv:1907.10326

    2019 arXiv

  15. [23]

    Li, J.; Li, D.; Xiong, C.; and Hoi, S. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, 12888--12900. PMLR

    2022

  16. [24]

    Li, Z.; Wang, X.; Liu, X.; and Jiang, J. 2024. Binsformer: Revisiting adaptive bins for monocular depth estimation. In IEEE Transactions on Image Processing. IEEE

    2024

  17. [25]

    Lin, H.; Peng, S.; Chen, J.; Peng, S.; Sun, J.; Liu, M.; Bao, H.; Feng, J.; Zhou, X.; and Kang, B. 2024. Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation. In arXiv preprint arXiv:2412.14015

    2024

  18. [26]

    Mescheder, L.; Oechsle, M.; Niemeyer, M.; Nowozin, S.; and Geiger, A. 2019. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 4460--4470

    2019

  19. [27]

    Ning, J.; Li, C.; Zhang, Z.; Wang, C.; Geng, Z.; Dai, Q.; He, K.; and Hu, H. 2023. All in tokens: Unifying output space of visual tasks via soft token. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 19900--19910

    2023

  20. [28]

    Piccinelli, L.; Yang, Y.-H.; Sakaridis, C.; Segu, M.; Li, S.; Van Gool, L.; and Yu, F. 2024. UniDepth: Universal Monocular Metric Depth Estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10106--10116

    2024

  21. [29]

    Qi, X.; Liao, R.; Liu, Z.; Urtasun, R.; and Jia, J. 2018. Geonet: Geometric neural network for joint depth and surface normal estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 283--291

    2018

  22. [30]

    W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al

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

    2021

  23. [31]

    Ranftl, R.; Bochkovskiy, A.; and Koltun, V. 2021. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, 12179--12188

    2021

  24. [32]

    Ranftl, R.; Lasinger, K.; Hafner, D.; Schindler, K.; and Koltun, V. 2020. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. In IEEE transactions on pattern analysis and machine intelligence, volume 44, 1623--1637. IEEE

    2020

  25. [33]

    Reiner; Birkl, D.; Wofk, M.; and M \"u ller. 2023. Midas v3. 1--a model zoo for robust monocular relative depth estimation. In arXiv preprint arXiv:2307.14460

    2023 arXiv

  26. [34]

    Sch \"o n; Markus, B.; Michael, D.; and Klaus. 2021. Mgnet: Monocular geometric scene understanding for autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 15804--15815

    2021

  27. [35]

    Shao, S.; Pei, Z.; Chen, W.; Li, R.; Liu, Z.; and Li, Z. 2023. Urcdc-depth: Uncertainty rectified cross-distillation with cutflip for monocular depth estimation. In IEEE Transactions on Multimedia. IEEE

    2023

  28. [36]

    Silberman, N.; Hoiem, D.; Kohli, P.; and Fergus, R. 2012. Indoor segmentation and support inference from rgbd images. In Computer Vision--ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V 12, 746--760. Springer

    2012

  29. [37]

    Song; Shuran, L.; Samuel P, X.; and Jianxiong. 2015. Sun rgb-d: A rgb-d scene understanding benchmark suite. In Proceedings of the IEEE conference on computer vision and pattern recognition, 567--576

    2015

  30. [38]

    Song, Z.; Wang, Z.; Li, B.; Zhang, H.; Zhu, R.; Liu, L.; Jiang, P.-T.; and Zhang, T. 2025. DepthMaster: Taming Diffusion Models for Monocular Depth Estimation. In arXiv preprint arXiv:2501.02576

    2025 arXiv

  31. [39]

    Uhrig, J.; Schneider, N.; Schneider, L.; Franke, U.; Brox, T.; and Geiger, A. 2017. Sparsity invariant cnns. In 2017 international conference on 3D Vision (3DV), 11--20. IEEE

    2017

  32. [40]

    Viola, M.; Qu, K.; Metzger, N.; Ke, B.; Becker, A.; Schindler, K.; and Obukhov, A. 2024. Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion. In arXiv preprint arXiv:2412.13389

    2024 arXiv

  33. [41]

    Wofk, D.; Ranftl, R.; M \"u ller, M.; and Koltun, V. 2023. Monocular Visual-Inertial Depth Estimation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), 6095--6101. IEEE

    2023

  34. [42]

    Yang, L.; Kang, B.; Huang, Z.; Xu, X.; Feng, J.; and Zhao, H. 2024 a . Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10371--10381

    2024

  35. [43]

    Yang, L.; Kang, B.; Huang, Z.; Zhao, Z.; Xu, X.; Feng, J.; and Zhao, H. 2024 b . Depth Anything V2. In arXiv preprint arXiv:2406.09414

    2024 arXiv

  36. [44]

    Yin, W.; Wang, X.; Shen, C.; Liu, Y.; Tian, Z.; Xu, S.; Sun, C.; and Renyin, D. 2020. Diversedepth: Affine-invariant depth prediction using diverse data. In arXiv preprint arXiv:2002.00569

    2020 arXiv

  37. [45]

    Yin, W.; Zhang, C.; Chen, H.; Cai, Z.; Yu, G.; Wang, K.; Chen, X.; and Shen, C. 2023. Metric3d: Towards zero-shot metric 3d prediction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 9043--9053

    2023

  38. [46]

    Zeng, Z.; Wang, D.; Yang, F.; Park, H.; Soatto, S.; Lao, D.; and Wong, A. 2024 a . Wordepth: Variational language prior for monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9708--9719

    2024

  39. [47]

    Zeng, Z.; Wu, Y.; Park, H.; Wang, D.; Yang, F.; Soatto, S.; Lao, D.; Hong, B.-W.; and Wong, A. 2024 b . Rsa: Resolving scale ambiguities in monocular depth estimators through language descriptions. In arXiv preprint arXiv:2410.02924

    2024 arXiv

  40. [48]

    Zhang, R.; Zeng, Z.; Guo, Z.; and Li, Y. 2022. Can language understand depth? In Proceedings of the 30th ACM International Conference on Multimedia, 6868--6874

    2022

  41. [49]

    Zhang, X.; Ke, B.; Riemenschneider, H.; Metzger, N.; Obukhov, A.; Gross, M.; Schindler, K.; and Schroers, C. 2024. Betterdepth: Plug-and-play diffusion refiner for zero-shot monocular depth estimation. In arXiv preprint arXiv:2407.17952

    2024 arXiv

  42. [50]

    Zhao, W.; Rao, Y.; Liu, Z.; Liu, B.; Zhou, J.; and Lu, J. 2023. Unleashing text-to-image diffusion models for visual perception. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 5729--5739

    2023

  43. [51]

    Zhu, R.; Wang, C.; Song, Z.; Liu, L.; Zhang, T.; and Zhang, Y. 2024. Scaledepth: Decomposing metric depth estimation into scale prediction and relative depth estimation. In arXiv preprint arXiv:2407.08187

    2024

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.