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REVIEW 3 major objections 6 minor 1 cited by

Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Mono3R claims that matching-based multi-view reconstruction can be made reliable in textureless and low-light regions by injecting aligned single-image geometry priors through an iterative refinement module.

desk verdict A plausible but under-controlled fusion of monocular priors into DUSt3R; indoor gains are real, but the central attribution to monocular guidance is not yet established. read the letter →

arxiv 2504.13419 v1 pith:EHRXO3K2 submitted 2025-04-18 cs.CV

Wenyu Li , Sidun Liu , Peng Qiao , Yong Dou This is my paper · ORCID
classification cs.CV
keywords 3Dreconstructionmonoculargeometrypriorsmulti-viewposeestimationpointmapregressionDUSt3RMoGeiterativerefinementtexturelessregions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that matching-based multi-view reconstruction loses accuracy precisely where image correspondence is ambiguous, such as textureless walls, low light, and thin structures, and that a monocular geometry estimator can supply the missing cues. Mono3R augments DUSt3R's pairwise pointmap branch with MoGe's single-image pointmaps and features, first through a global similarity alignment and then through iterative refinement of the pairwise pointmap. On five benchmarks it reports consistent gains over DUSt3R, Spann3R, and Fast3R, including a 13% improvement in indoor pose accuracy (mAA30). If the claim holds, it offers a practical way to make feed-forward reconstruction reliable in hard regions without retraining the entire matching model.

What carries the argument

The mono-guided refinement module pairs a confidence-weighted similarity transform least-squares fit with a ConvGRU-based iterative update. The alignment maps each monocular pointmap into the pairwise pointmap's coordinate system; the ConvGRU then takes the aligned monocular pointmap, monocular features, pairwise features, the original image, and a confidence map as a condition, and updates the pairwise pointmap in residual increments over a fixed number of iterations. The module's work is to preserve the multi-view consistency learned by the pairwise branch while letting single-image priors correct geometry in regions the matching branch gets wrong.

What would settle it

At inference, replace the aligned monocular pointmap in the refinement condition with a plane fitted to the same depth statistics; if pose accuracy (mAA30) and point-cloud completeness remain unchanged, then the monocular pointmap itself is not what produces the reported gain.

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Extended reading notes

Core claim

The central claim is that matching-based multi-view reconstruction and monocular geometry estimation are complementary, and that fusing them through a global similarity alignment followed by learned iterative refinement produces feed-forward pointmaps that are both multi-view consistent and stable in regions where correspondence is ambiguous. The paper argues this in terms of DUSt3R's pairwise pointmap and MoGe's monocular pointmap, and reports consistent gains over five benchmarks.

Load-bearing premise

The method assumes that one global similarity transform per image brings MoGe's monocular pointmap close enough to DUSt3R's pairwise pointmap that the learned ConvGRU refinement can correct residual misalignment, so if the two geometries differ by per-pixel scale drift rather than a global scale, the refinement starts from a poor initialization.

Editorial extensions

If this is right

  • If the central claim is right, feed-forward multi-view reconstruction can gain monocular robustness without retraining the matching backbone from scratch, since only the later decoder blocks, head, and refinement module need optimization.
  • Camera pose estimation from unordered frames should improve most in indoor scenes, with mAA30 rising from 0.576 to 0.728 on 7Scenes and from 0.772 to 0.887 on Neural-RGBD relative to DUSt3R.
  • Point-cloud quality should improve on object-level scenes as well, with DTU completeness mean falling from 3.623 to 3.433 and median from 2.407 to 2.274.
  • Additional refinement iterations beyond two mostly improve translation accuracy rather than rotation, suggesting the residual errors the module corrects are geometric rather than orientational.

Reading between the lines

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

  • The recipe is likely portable: any matching-based pointmap model could be paired with any monocular pointmap predictor, so monocular-guided refinement could become a general add-on rather than a DUSt3R-specific fix.
  • The method's ceiling is set by how close the monocular and pairwise representations are up to a similarity transform, so a monocular estimator with strong per-pixel scale drift would probably need non-rigid alignment before refinement helps.
  • A testable extension is to swap MoGe for a weaker monocular depth estimator under the same alignment and refinement, predicting that gains concentrate in textureless and low-light regions if the paper's mechanism is correct.
  • For video streams, one could check whether the refinement reduces temporal flicker on flat surfaces, since the paper evaluates frame-based metrics only.
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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 / 6 minor

Summary. The paper proposes Mono3R, a feed-forward multi-view 3D reconstruction method that augments the DUSt3R matching-based pairwise pipeline with monocular geometric cues from MoGe. A global Sim(3) alignment registers per-image monocular pointmaps with the pairwise prediction, and a ConvGRU-based refinement module iteratively updates the pairwise pointmaps using monocular pointmaps and features. The method is trained by fine-tuning only the last two decoder blocks and head of DUSt3R while freezing the monocular branch, and is evaluated on camera pose estimation and point cloud accuracy across five benchmarks (7Scenes, Neural-RGBD, DTU, ETH3D, Tanks and Temples) against DUSt3R, Spann3R, and Fast3R. The paper reports substantial gains on indoor benchmarks and on DTU, and claims consistent improvement over DUSt3R-family methods.

Significance. The idea of combining monocular geometric priors with matching-based multi-view reconstruction is timely and potentially useful, particularly for textureless and weakly matched regions. The paper's strengths include a clear architecture, a broad evaluation across multiple benchmark types, and ablation studies on fusion strategies and refinement iterations. If the central attribution claim were cleanly supported, the work would provide a practical recipe for improving DUSt3R-style models. However, as presented, the empirical evidence does not isolate the effect of monocular guidance from the effect of fine-tuning on the training mixture, and some claims in the paper overstate the results.

major comments (3)
  1. [Section 4, Implementation Details and Tables 1-4] The central claim that monocular geometric cues, rather than additional fine-tuning, drive the reported improvements is not cleanly supported. Mono3R initializes from DUSt3R and then fine-tunes the last two decoder blocks and head on a multi-dataset mixture at 224px, while the DUSt3R baseline is used as a released checkpoint without equivalent fine-tuning. The 7Scenes mAA30 jump from 0.576 to 0.728 and the DTU gains could in principle be caused by adapting DUSt3R to the training data alone. To support the attribution, the paper needs a DUSt3R-only control trained under the identical protocol (same data, resolution, iteration count) with the monocular branch and refinement module removed. This control should appear alongside the main tables and in the ablation study, where Table 5 currently has no variant without monocular guidance.
  2. [Section 2 and Section 4.1] MASt3R is cited as a stronger matching-based extension of DUSt3R, but it is never included in the experiments. Since the paper's narrative is that matching-based methods are limited and that monocular cues provide a specific advantage, comparing against the strongest available DUSt3R-family matching method is important for calibrating the claimed improvement. Without a MASt3R baseline, the reader cannot tell whether the gains over DUSt3R reflect monocular guidance or simply a difference in the matching backbone strength. Please add MASt3R results or explicitly justify its omission.
  3. [Table 3 and Section 4.1] The ETH3D results are in tension with the paper's generalization claims. Table 3 shows mAA30 of 0.511 for Mono3R versus 0.520 for DUSt3R, and RTA5, RTA10, and RTA15 are all lower for Mono3R (0.265 vs 0.307, 0.462 vs 0.488, 0.585 vs 0.607). The text in Section 4.1 states that the model 'consistently outperforms competing methods in all metrics,' and the abstract claims consistent improvement across benchmarks, but the ETH3D data show a degradation on translation metrics. These results need to be reported and discussed honestly, and the claims in the abstract and introduction should be qualified accordingly.
minor comments (6)
  1. [Abstract and Figure 3 caption] The abstract contains the typo 'mutli-view', and Figure 3's caption spells 'Comparision' instead of 'Comparison'. Please proofread the manuscript.
  2. [Section 3.4, Eq. (5)] The notation in Eq. (5) is inconsistent: the refinement iterations are indexed by j in the main text, but the loss uses v with weights gamma^(N-v), and the confidence term w^v_0 is undefined in the refinement loss. Please clarify the iteration indexing and define all variables, including the normalizing factors z and z-bar.
  3. [Section 4, Implementation Details] The justification that testing at 224px 'does not affect the fairness of our evaluation' is not fully argued. DUSt3R was trained with mixed resolutions including 512, and evaluating its released checkpoint at 224 may disadvantage it; Mono3R is trained at 224. Please provide evidence or a more nuanced discussion of resolution effects on the comparison.
  4. [References and Table 1] The Neural-RGBD dataset appears to be cited as reference [47], which is the TUM RGB-D benchmark paper. Please verify that the correct dataset reference is used and that the abbreviation NRGBD matches the cited source.
  5. [Table 2] The point cloud accuracy and completeness values in Table 2 are reported without units or scale. Please state the metric definition (e.g., mean distance in centimeters) and the alignment procedure used for the metric computation.
  6. [Tables 1-4] The very low Spann3R results on NRGBD (mAA30 = 0.004) and Tanks and Temples (mAA30 = 0.000) are surprising and suggest a possible protocol mismatch. Please describe how each baseline was invoked, including offline and online modes and input resolution, so that the reader can assess the fairness of these comparisons.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the method is an empirically evaluated combination of published components, with no predicted quantity constructed from its own fit.

full rationale

The paper's central claims are empirical: Mono3R combines a frozen pretrained monocular estimator (MoGe) with a fine-tuned DUSt3R-style pairwise branch and a learned ConvGRU refinement module, then evaluates the resulting pointmaps and poses on held-out benchmarks (7Scenes, NRGBD, DTU, ETH3D, Tanks & Temples). There is no derivation in which an output is defined in terms of the quantity it is claimed to predict, and no fitted parameter is renamed as a prediction. Equation 1 is a preprocessing Sim(3) alignment that coarsely registers monocular pointmaps to pairwise predictions; it is not the source of the reported accuracy numbers, and the paper explicitly acknowledges and tries to correct its residual errors with the learned refinement. The baselines are external published systems, not self-citations by the present authors, and no load-bearing premise is justified solely by a citation to the authors' own prior work. The one substantive concern is that DUSt3R, Spann3R, and Fast3R are used as released checkpoints while Mono3R is fine-tuned on a multi-dataset training mixture, so part of the reported gain could in principle come from additional training rather than from monocular guidance. That is a fairness and attributability threat to the empirical claim, but it is not circularity under the stated criteria: the reported metrics are externally measured against ground truth, and no equation in the paper reduces the result to its inputs by construction. Accordingly, the appropriate finding is no significant circularity, score 0, with the baseline-control issue flagged as a soundness concern outside this pass.

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

No new physical entities are introduced. The method is a neural architecture combination that assumes the frozen monocular model provides reliable geometry and that a global similarity transform can bridge the gap between the two representations.

free parameters (3)
  • N (number of refinement iterations) = 2
    Chosen by hand; the main experiment uses N=2, but the ablation (Table 5) shows N=1 yields slightly higher mAA30 on DTU (0.724 vs 0.714), so the choice is not clearly optimal.
  • gamma (loss weighting) = 0.9
    Exponential weighting factor for the refinement loss, set to 0.9 following common practice in iterative refinement networks.
  • Training resolution = 224 px
    The model is trained and tested at 224x224 due to computational constraints; this may affect fairness compared to baselines trained at mixed resolutions.
assumptions (4)
  • domain assumption MoGe's monocular pointmaps and features are robust in textureless and low-light regions.
    The paper relies on this to motivate the method and to claim improvements in such regions; it is not independently validated in the experiments because the benchmarks are not specifically designed for textureless or low-light scenes.
  • domain assumption DUSt3R's pointmaps provide a consistent multi-view coordinate frame.
    The method registers monocular pointmaps to DUSt3R's output; if DUSt3R's coordinate frame is inconsistent across pairs, the global alignment will be unreliable.
  • domain assumption The confidence weights w0 from the pairwise branch reliably identify unreliable predictions (sky, depth extremes).
    These weights are used in Eq. 1 for weighted least-squares alignment and in the loss; incorrect weights could corrupt the alignment.
  • ad hoc to paper A single global Sim(3) per image is sufficient to align monocular and pairwise pointmaps.
    This is the core modeling choice of the refinement module, supported only by qualitative evidence that residual misalignment remains (Fig. 3).

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Cite this review

Pith. "Pith review of Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction." pith.science (2026). https://pith.science/paper/EHRXO3K2

@misc{pith2026250413419,
  author       = {Pith},
  title        = {Pith review of: Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EHRXO3K2}},
  note         = {Machine review of arXiv:2504.13419}
}
read the original abstract

Recent advances in data-driven geometric multi-view 3D reconstruction foundation models (e.g., DUSt3R) have shown remarkable performance across various 3D vision tasks, facilitated by the release of large-scale, high-quality 3D datasets. However, as we observed, constrained by their matching-based principles, the reconstruction quality of existing models suffers significant degradation in challenging regions with limited matching cues, particularly in weakly textured areas and low-light conditions. To mitigate these limitations, we propose to harness the inherent robustness of monocular geometry estimation to compensate for the inherent shortcomings of matching-based methods. Specifically, we introduce a monocular-guided refinement module that integrates monocular geometric priors into multi-view reconstruction frameworks. This integration substantially enhances the robustness of multi-view reconstruction systems, leading to high-quality feed-forward reconstructions. Comprehensive experiments across multiple benchmarks demonstrate that our method achieves substantial improvements in both mutli-view camera pose estimation and point cloud accuracy.

Figures

Figures reproduced from arXiv: 2504.13419 by the authors.

Figure 1
Figure 1. In this paper, we reveal the limitations of DUSt3R in reconstructing textureless regions and fine structures, as [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Our framework consists of two complementary branches and a refinement module. The pairwise branch pro [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Comparision between aligned monocular pointmaps [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of our predicted depthmaps and 3D points to DUSt3R on in-the-wild captured images. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Qualitative examples of Mono3R’s output. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Additional visualizations of depth map estimation. Compared to DUSt3R, our prediction is high-quality when [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Depth map visualization comparison between DUSt3R, two ablation variants, and our proposed method. Our [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self-Geometry: GT-Free and Plug-and-Play Test-Time Adaptation for Geometrically Consistent 3D Vision Foundation Models

    cs.CV 2026-08 conditional novelty 7.0 of 10

    Self-Geometry adapts frozen 3D vision foundation models per scene by enforcing epipolar and reprojection consistency on pseudo-correspondences from an off-the-shelf matcher, improving pose and depth accuracy.

Reference graph

Works this paper leans on

90 extracted references · 53 canonical work pages · cited by 1 Pith paper

  1. [1]

    Raymvsnet: Learning ray-based 1d implicit fields for accurate multi-view stereo

    Junhua Xi, Yifei Shi, Yijie Wang, Yulan Guo, and Kai Xu. Raymvsnet: Learning ray-based 1d implicit fields for accurate multi-view stereo. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8585–8595, 2022

    2022

  2. [2]

    Geometric transformer for fast and robust point cloud registration

    Zheng Qin, Hao Yu, Changiian Wang, Yulan Guo, Yuxing Peng, and Kai Xu. Geometric transformer for fast and robust point cloud registration. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11133–11142, 2022

    2022

  3. [3]

    Tsai and Thomas S

    Roger Y . Tsai and Thomas S. Huang. Uniqueness and estimation of three-dimensional motion parameters of rigid objects with curved surfaces. PAMI, 6(1), 1984

    1984

  4. [4]

    Camera pose and calibration from 4 or 5 known 3D points

    Bill Triggs. Camera pose and calibration from 4 or 5 known 3D points. In Proceedings of the International Conference on Computer Vision (ICCV), 1999

    1999

  5. [5]

    Factorization methods for projective structure and motion

    Bill Triggs. Factorization methods for projective structure and motion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1996

    1996

  6. [6]

    Mihran Tüceryan and Anil K. Jain. Texture analysis. In The Handbook of Pattern Recognition and Computer Vision (2nd Edition). World Scientific Publishing Co., 1998

    1998

  7. [7]

    Tuytelaars, M

    T. Tuytelaars, M. Vergauwen, M. Pollefeys, and Luc J. Van Gool. Image matching for wide baseline stereo. In Int’l Conf. on Forensic Human Identification, 1999

    1999

  8. [8]

    Silpa-Anan and R

    C. Silpa-Anan and R. Hartley. Localization using an image-map. In Australasian Conf. on Robotics and Automation, 2004

    2004

Show all 90 references
  1. [9]

    Vision meets robotics: The KITTI dataset

    Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The KITTI dataset. International Journal of Robotics Research (IJRR), 2013

    2013

  2. [10]

    Milad Ramezani, Matías Mattamala, and Maurice F. Fallon. AEROS: adaptive robust least-squares for graph-based SLAM. Frontiers Robotics AI, 9, 2022

    2022

  3. [11]

    DM-VIO: delayed marginalization visual-inertial odometry

    Lukas von Stumberg and Daniel Cremers. DM-VIO: delayed marginalization visual-inertial odometry. IEEE Robotics Autom. Lett., 7(2), 2022

    2022

  4. [12]

    R. A. Smith, Andrew W. Fitzgibbon, and Andrew Zisserman. Improving augmented reality using image and scene constraints. In Proceedings of the British Machine Vision Conference (BMVC). BMV A Press, 1999

    1999

  5. [13]

    A survey of augmented reality

    Ronald Azuma. A survey of augmented reality. Presence: Teleoperators and Virtual Environments, 6(4), 1997

    1997

  6. [14]

    R. A. Brooks. Elephants don’t play chess. In P. Maes, editor, Designing autonomous agents. Bradford Books, MIT Press, Cambridge, 1991

    1991

  7. [15]

    Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger. Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios? In Proceedings o...

    2017

  8. [16]

    NEAT: neural attention fields for end-to-end autonomous driving

    Kashyap Chitta, Aditya Prakash, and Andreas Geiger. NEAT: neural attention fields for end-to-end autonomous driving. In Proceedings of the International Conference on Computer Vision (ICCV), 2021

    2021

  9. [17]

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

    Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In Conference on Computer Vision and Pattern Recognition (CVPR), 2012

    2012

  10. [18]

    Self- supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue

    Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu, Dianmin Sun, and Baochang Zhang. Self- supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue. Medical Image Anal., 77, 2022. 11 A PREPRINT - A PRIL 21, 2025

    2022

  11. [19]

    Texture bags: Anomaly retrieval in medical images based on local 3D-Texture similarity

    Andreas Burner, Rene Donner, Marius Mayerhoefer, Markus Holzer, Franz Kainberger, and Georg Langs. Texture bags: Anomaly retrieval in medical images based on local 3D-Texture similarity. In Proc. MCBR-CDS, 2011

    2011

  12. [20]

    Structure-from-motion revisited

    Johannes Lutz Schönberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016

    2016

  13. [21]

    Hsfm: Hybrid structure-from-motion

    Hainan Cui, Xiang Gao, Shuhan Shen, and Zhanyi Hu. Hsfm: Hybrid structure-from-motion. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1212–1221, 2017

    2017

  14. [22]

    Deepsfm: Structure from motion via deep bundle adjustment

    Xingkui Wei, Yinda Zhang, Zhuwen Li, Yanwei Fu, and Xiangyang Xue. Deepsfm: Structure from motion via deep bundle adjustment. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pages 230–247. Springer, 2020

    2020

  15. [23]

    DeepSFM: structure from motion via deep bundle adjustment

    Xingkui Wei, Yinda Zhang, Zhuwen Li, Yanwei Fu, and Xiangyang Xue. DeepSFM: structure from motion via deep bundle adjustment. In Proceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  16. [24]

    VGGSfM: visual geometry grounded deep structure from motion

    Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny. VGGSfM: visual geometry grounded deep structure from motion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  17. [25]

    Choon Hui Teo, S. V . N. Vishwanathan, Alexander J. Smola, and Quoc V . Le. Bundle methods for regularized risk minimization. JMLR, 11, 2010

    2010

  18. [26]

    McLauchlan, Richard I

    Bill Triggs, Philip F. McLauchlan, Richard I. Hartley, and Andrew W. Fitzgibbon. Bundle adjustment - A modern synthesis. In Proc. ICCV Workshop, 2000

    2000

  19. [27]

    BundleSDF: Neural 6-DoF tracking and 3D reconstruction of unknown objects

    Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Müller, Alex Evans, Dieter Fox, Jan Kautz, and Stan Birchfield. BundleSDF: Neural 6-DoF tracking and 3D reconstruction of unknown objects. arXiv.cs, abs/2303.14158, 2023

    2023 arXiv

  20. [28]

    Bundle min-hashing for logo recognition

    Stefan Romberg and Rainer Lienhart. Bundle min-hashing for logo recognition. In Proc. ICMR, 2013

    2013

  21. [29]

    Pixelwise view selection for unstructured multi-view stereo

    Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for unstructured multi-view stereo. In Proceedings of the European Conference on Computer Vision (ECCV), 2016

    2016

  22. [30]

    Mvsnet: Depth inference for unstructured multi-view stereo

    Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In ECCV, 2018

    2018

  23. [31]

    C. Slama. Manual of Photogrammetry. American Society of Photogrammetry, 1980

    1980

  24. [32]

    Thrun, D

    S. Thrun, D. Koller, Z. Ghahmarani, and H. Durrant-Whyte. SLAM updates require constant time. In Proc. of the Fifth Int’l Workshop on Algorithmic Foundations of Robotics, 2002

    2002

  25. [33]

    GS-SLAM: Dense visual SLAM with 3D Gaussian splatting

    Chi Yan, Delin Qu, Dong Wang, Dan Xu, Zhigang Wang, Bin Zhao, and Xuelong Li. GS-SLAM: Dense visual SLAM with 3D Gaussian splatting. arXiv.cs, 2024

    2024

  26. [34]

    DUSt3R: Geometric 3D vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. DUSt3R: Geometric 3D vision made easy. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  27. [35]

    Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass

    Jianing Yang, Alexander Sax, Kevin J Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass. arXiv preprint arXiv:2501.13928, 2025

    2025 arXiv

  28. [36]

    Efros, and Angjoo Kanazawa

    Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, and Angjoo Kanazawa. Continuous 3d perception model with persistent state, 2025

    2025

  29. [37]

    Grounding image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. arXiv preprint arXiv:2406.09756, 2024

    2024 arXiv

  30. [38]

    MASt3R-SfM: a fully-integrated solution for unconstrained structure-from-motion

    Bardienus Duisterhof, Lojze Zust, Philippe Weinzaepfel, Vincent Leroy, Yohann Cabon, and Jerome Revaud. MASt3R-SfM: a fully-integrated solution for unconstrained structure-from-motion. arXiv preprint, 2409.19152, 2024

    2024 arXiv

  31. [39]

    3d reconstruction with spatial memory

    Hengyi Wang and Lourdes Agapito. 3d reconstruction with spatial memory. arXiv preprint arXiv:2408.16061, 2024

    2024 arXiv

  32. [40]

    Recovering 3D human pose from monocular images

    Ankur Agarwal and Bill Triggs. Recovering 3D human pose from monocular images. PAMI, 28(1), 2006

    2006

  33. [41]

    Dense monocular depth estimation in complex dynamic scenes

    Rene Ranftl, Vibhav Vineet, Qifeng Chen, and Vladlen Koltun. Dense monocular depth estimation in complex dynamic scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 12 A PREPRINT - A PRIL 21, 2025

    2016

  34. [42]

    UniDepth: universal monocular metric depth estimation

    Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu. UniDepth: universal monocular metric depth estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  35. [43]

    Saurabh Saxena, Charles Herrmann, Junhwa Hur, Abhishek Kar, Mohammad Norouzi, Deqing Sun, and David J. Fleet. The surprising effectiveness of diffusion models for optical flow and monocular depth estimation. arXiv.cs, abs/2306.01923, 2023

    2023 arXiv

  36. [44]

    MoGe: unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision

    Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang. MoGe: unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. arXiv preprint, 2410.19115, 2024

    arXiv

  37. [45]

    Large scale multi-view stereopsis evaluation

    Rasmus Jensen, Anders Dahl, George V ogiatzis, Engil Tola, and Henrik Aanæs. Large scale multi-view stereopsis evaluation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 406–413. IEEE, 2014

    2014

  38. [46]

    Scene coordinate regression forests for camera relocalization in rgb-d images

    Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene coordinate regression forests for camera relocalization in rgb-d images. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 2930–2937, 2013

    2013

  39. [47]

    A benchmark for the evaluation of rgb-d slam systems

    Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of rgb-d slam systems. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pages 573–580. IEEE, 2012

    2012

  40. [48]

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

    Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multi-camera videos. In Proceedings of the IEEE conference on computer vision and patte...

    2017

  41. [49]

    Tanks and temples: Benchmarking large-scale scene reconstruction

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics, 36(4), 2017

    2017

  42. [50]

    Structure-from-motion revisited

    Johannes Lutz Schönberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

    2016

  43. [51]

    Pixelwise view selection for unstructured multi-view stereo

    Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016

    2016

  44. [52]

    Ce Liu, Jenny Yuen, Antonio Torralba, Josef Sivic, and William T. Freeman. SIFT flow: Dense correspondence across different scenes. In Proceedings of the European Conference on Computer Vision (ECCV), 2008

    2008

  45. [53]

    D. Lowe. Implementation of the scale invariant feature transform. http://www.cs.ubc.ca/~lowe/ keypoints/, 2007

    2007

  46. [54]

    Tsay and M.-S

    J.-R. Tsay and M.-S. Lee. SIFT for dense point cloud matching and aero triangulation. In International Archives of the Photogrammetry,, 2012

    2012

  47. [55]

    Ba-net: Dense bundle adjustment network

    Chengzhou Tang and Ping Tan. Ba-net: Dense bundle adjustment network. arXiv preprint arXiv:1806.04807, 2018

    2018 arXiv

  48. [56]

    Bundle adjustment in the large

    Sameer Agarwal, Noah Snavely, Steven M Seitz, and Richard Szeliski. Bundle adjustment in the large. InComputer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part II 11, pages 29–42. Springer, 2010

    2010

  49. [57]

    Baillard and A

    C. Baillard and A. Zisserman. A plane-sweep strategy for the 3D reconstruction of buildings from multiple images. In ISPRS Congress and Exhibition, 2000

    2000

  50. [58]

    Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B. Goldman. PatchMatch: a randomized corre- spondence algorithm for structural image editing. ACM Transaction on Graphics (Proc. SIGGRAPH), 28(3), 2009

    2009

  51. [59]

    Seitz, and Richard Szeliski

    Yasutaka Furukawa, Brian Curless, Steven M. Seitz, and Richard Szeliski. Towards internet-scale multi-view stereo. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010

    2010

  52. [60]

    SuperPoint: self-supervised interest point detection and description

    DeTone Daniel, Malisiewicz Tomasz, and Rabinovich Andrew. SuperPoint: self-supervised interest point detection and description. arXiv preprint, 1712.07629, 2017

    2017 arXiv

  53. [61]

    LIFT: learned invariant feature transform

    Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. LIFT: learned invariant feature transform. In Proceedings of the European Conference on Computer Vision (ECCV), 2016

    2016

  54. [62]

    SuperGlue: learning feature matching with graph neural networks

    Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. SuperGlue: learning feature matching with graph neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 13 A PREPRINT - A PRIL 21, 2025

    2020

  55. [63]

    Loftr: Detector-free local feature matching with transformers

    Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8922–8931, 2021

    2021

  56. [64]

    Aspanformer: Detector-free image matching with adaptive span transformer

    Hongkai Chen, Zixin Luo, Lei Zhou, Yurun Tian, Mingmin Zhen, Tian Fang, David Mckinnon, Yanghai Tsin, and Long Quan. Aspanformer: Detector-free image matching with adaptive span transformer. In European Conference on Computer Vision, pages 20–36. Springer, 2022

    2022

  57. [65]

    Geomvsnet: Learning multi-view stereo with geometry perception

    Zhe Zhang, Rui Peng, Yuxi Hu, and Ronggang Wang. Geomvsnet: Learning multi-view stereo with geometry perception. In CVPR, 2023

    2023

  58. [66]

    MonST3R: a simple approach for estimating geometry in the presence of motion

    Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. MonST3R: a simple approach for estimating geometry in the presence of motion. arXiv preprint, 2410.03825, 2024

    arXiv

  59. [67]

    Pifu: Pixel- aligned implicit function for high-resolution clothed human digitization

    Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel- aligned implicit function for high-resolution clothed human digitization. In Proceedings of the International Conference on Computer Vision (ICCV), 2019

    2019

  60. [68]

    Depth anything v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. arXiv:2406.09414, 2024

    2024 arXiv

  61. [69]

    RealMonoDepth: Self-supervised monocular depth estimation for general scenes

    Mertalp Ocal and Armin Mustafa. RealMonoDepth: Self-supervised monocular depth estimation for general scenes. arXiv.cs, abs/2004.06267, 2020

    2004 arXiv

  62. [70]

    Megadepth: Learning single-view depth prediction from internet photos

    Zhengqi Li and Noah Snavely. Megadepth: Learning single-view depth prediction from internet photos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2041–2050, 2018

    2018

  63. [71]

    Automatic camera calibration from a single manhattan image

    Jonathan Deutscher, Michael Isard, and John MacCormick. Automatic camera calibration from a single manhattan image. In Computer Vision—ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV 7, pages 175–188. Springer, 2002

    2002

  64. [72]

    A perceptual measure for deep single image camera calibration

    Yannick Hold-Geoffroy, Kalyan Sunkavalli, Jonathan Eisenmann, Matthew Fisher, Emiliano Gambaretto, Sunil Hadap, and Jean-François Lalonde. A perceptual measure for deep single image camera calibration. InProceedings of the IEEE Conference on Computer Vision and Pattern Recogni...

    2018

  65. [73]

    Learning to recover 3d scene shape from a single image

    Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204–213, 2021

    2021

  66. [74]

    Monster: Marry monodepth to stereo unleashes power

    Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, and Xin Yang. Monster: Marry monodepth to stereo unleashes power. In CVPR, 2025

    2025

  67. [75]

    Depthsplat: Connecting gaussian splatting and depth

    Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. Depthsplat: Connecting gaussian splatting and depth. In CVPR, 2025

    2025

  68. [76]

    Mvsnet: Depth inference for unstructured multi-view stereo

    Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV), pages 767–783, 2018

    2018

  69. [77]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at...

    2021

  70. [78]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  71. [79]

    Vision transformers for dense prediction

    René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179–12188, 2021

    2021

  72. [80]

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V . V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rab...

    2024

  73. [81]

    RAFT: recurrent all-pairs field transforms for optical flow

    Zachary Teed and Jia Deng. RAFT: recurrent all-pairs field transforms for optical flow. In Proceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  74. [82]

    Raft-stereo: Multilevel recurrent field transforms for stereo matching

    Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recurrent field transforms for stereo matching. In Proceedings of the International Conference on 3D Vision (3DV), pages 218–227, 2021. 14 A PREPRINT - A PRIL 21, 2025

    2021

  75. [83]

    Least-squares estimation of transformation parameters between two point patterns

    Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376–380, 1991

    1991

  76. [84]

    Delving deeper into convolutional networks for learning video representations

    Nicolas Ballas, Li Yao, Chris Pal, and Aaron Courville. Delving deeper into convolutional networks for learning video representations. arXiv preprint arXiv:1511.06432, 2015

    2015 arXiv

  77. [85]

    ARKitscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile RGB-d data

    Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, and Elad Shulman. ARKitscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile RGB-d data. In Thirty-fifth Con...

    2021

  78. [86]

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

    Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. 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 ...

    2016

  79. [87]

    Blendedmvs: A large-scale dataset for generalized multi-view stereo networks

    Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1790–1799, 2020

    2020

  80. [88]

    Scannet++: A high-fidelity dataset of 3d indoor scenes

    Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, and Angela Dai. Scannet++: A high-fidelity dataset of 3d indoor scenes. In Proceedings of the International Conference on Computer Vision (ICCV), 2023

    2023

  81. [89]

    Common Objects in 3D: Large-scale learning and evaluation of real-life 3D category reconstruction

    Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common Objects in 3D: Large-scale learning and evaluation of real-life 3D category reconstruction. InProceedings of the International Conference on Computer Vision (ICCV), 2021

    2021

  82. [90]

    Scalability in perception for autonomous driving: Waymo open dataset

    Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurélien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang,...

    2020

Pith tools

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