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REVIEW 4 major objections 6 minor 35 references

SmallGS: Gaussian Splatting-based Camera Pose Estimation for Small-Baseline Videos

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

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Pith's one-line read SmallGS claims that small-baseline dynamic videos can be accurately tracked by freezing a first-frame Gaussian splatting and optimizing batches of camera poses against rendered DINOv2 features, with no feature correspondences or strong…

desk verdict A workmanlike combination of known splatting ingredients that modestly improves pose accuracy on small-baseline dynamic video, but the headline smoothness metric is partly built into the loss and the MonST3R dependence is untested. read the letter →

arxiv 2504.17810 v1 pith:GENZAWMJ submitted 2025-04-22 cs.CV eess.IV

Yuxin Yao , Yan Zhang , Zhening Huang , Joan Lasenby This is my paper · ORCID
classification cs.CVeess.IV
keywords cameraposeestimationsmall-baselinevideoGaussiansplattingDINOv2featuresdynamicscenestrajectorysmoothnessneuralrenderingvisualodometry
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 proposes SmallGS, a camera pose estimation method aimed at the everyday situation of a nearly stationary camera recording a dynamic scene. The core claim is that small baselines, usually a weakness for triangulation-based pose estimation, become a strength: a Gaussian splatting reconstructed from a segment's first frame, with dynamic objects masked out, is a stable reference that the remaining frames can be aligned to. SmallGS freezes that splatting and optimizes the batch of camera poses by minimizing RGB and DINOv2 feature rendering error plus a smoothness term, requiring no explicit feature correspondences or large parallax. On 46 curated TUM-Dynamics clips it reports lower absolute trajectory error and relative translation error than MonST3R and DROID-SLAM, with the 16-channel DINOv2 variant achieving the best trajectory smoothness.

What carries the argument

The mechanism is a frozen first-frame 3D Gaussian splatting: an explicit set of 3D Gaussians, each with mean, rotation, scale, opacity, and color or feature coefficients, rendered by differentiable $\alpha$-blending. The first frame's MonST3R depth is lifted into a dense point cloud, dynamic points are removed with the confidence mask (Eq. 7), and the Gaussians are fitted to that frame; then all Gaussian parameters are held fixed while a batch of camera poses $T_t,\dots,T_{t+b}$ is optimized by minimizing channel-wise MSE between rasterized DINOv2 features and observed features, an RGB/SSIM term, and a smoothness penalty (Eqs. 8--10). This lets pose gradients flow through the projection of each Gaussian, replacing correspondences and triangulation as the source of pose information.

What would settle it

Initialize SmallGS with the ground-truth camera poses on a TUM-Dynamics clip and run the same optimization; if the optimizer moves the poses away from ground truth to reduce the rendered-feature loss, then the objective the paper relies on is not measuring true pose in that scene.

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Extended reading notes

Core claim

The central discovery is that freezing the Gaussian splatting of a segment's first frame and optimizing the $\mathrm{SE}(3)$ poses of the remaining frames against rendered RGB and DINOv2 feature maps yields accurate, smooth trajectories for small-baseline dynamic videos, without feature matching, pointmap alignment, or bundle adjustment. The paper argues that because adjacent frames are highly similar, the splatting remains a valid representation across the segment, and near-field Gaussians dominate the photometric and feature error, so distant depth inaccuracies do not hurt. Dynamic objects are removed by MonST3R confidence masks used as semantic masks in both initialization and loss. DINOv2 features rendered through the same $\alpha$-blending pipeline make the pose gradients more robust than RGB alone, and identity initialization beats refining MonST3R poses because MonST3R's pointmap alignment is jittery in this regime. Reported results with 16-channel DINOv2 features are ATE 0.00228, RPE_t 0.00123, and velocity difference 0.00090, versus 0.00294, 0.00186, and 0.0258 for MonST3R and 0.00268, 0.00197, and 0.0263 for DROID-SLAM.

Load-bearing premise

The load-bearing premise is that the frozen Gaussian splatting built from the first frame, after MonST3R confidence masks remove dynamic regions, is a sufficiently complete and static model of the scene that photometric and feature-rendering error at later viewpoints points the pose gradients toward the true camera motion.

Editorial extensions

If this is right

  • Small-baseline videos can be tracked without feature correspondences or strong parallax, by aligning frames to a frozen first-frame splatting.
  • Pretrained robust visual features such as DINOv2, rendered through the Gaussian field, improve pose accuracy beyond RGB rendering, with more feature channels helping.
  • Batch optimization over a sliding window cuts compute (271 seconds versus 682 seconds for CF-3DGS per 30-frame clip) and reduces drift by reinitializing the splatting each segment.
  • Masking dynamic regions with predicted confidence maps lets the static-splatting assumption hold in dynamic scenes.
  • Identity initialization is preferable to MonST3R pose priors for these clips, because the prior poses are too jittery to refine.

Reading between the lines

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

  • An extension the paper leaves implicit: the same frozen-splatting refinement could be applied to any coarse pose source, including odometry from phone sensors, whenever depth and masks are available.
  • The PCA-based channel selection suggests a tunable tradeoff between feature dimensionality and pose accuracy; other self-supervised features or channel counts may behave differently on longer windows or lower-resolution input.
  • The framework's dependence on MonST3R's depth, masks, and intrinsics is a practical rather than a structural limitation, so a lighter depth-and-segmentation front end could make the approach a real-time refinement stage.
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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 proposes SmallGS, a camera pose estimation method for small-baseline videos. The method uses MonST3R to predict camera intrinsics, depth maps, and confidence masks; lifts the first frame of each sliding window into a 3D Gaussian splatting scene after masking dynamic regions; freezes that scene; and optimizes a batch of camera poses by minimizing the error between rasterized RGB or DINOv2 feature maps and the observed frames, augmented with a camera smoothness loss. The authors evaluate on 46 hand-selected TUM-Dynamics sequences, reporting that SmallGS improves ATE and RPE_t over MonST3R, DROID-SLAM, and CF-3DGS, while also being faster than CF-3DGS.

Significance. If the reported improvements hold, SmallGS is a practical contribution to a real problem: estimating camera poses in casual, small-baseline videos with dynamic content. The paper's strengths include the batched optimization scheme that reduces runtime relative to CF-3DGS, the integration of DINOv2 features into the Gaussian splatting pose optimization, and the clear presentation of the pipeline. However, the evaluation currently rests on an untested dependence on MonST3R's depth and mask quality, a smoothness metric that is partly constructed from the optimization loss, and a single curated benchmark without variance estimates. The independent ATE and RPE_t results provide some support for the central claim, but the paper needs substantial additional validation and careful re-framing before the claims can be considered robust.

major comments (4)
  1. [§3.2, Eq. (7) and §4.3] The central mechanism of SmallGS is that a frozen Gaussian splatting, initialized from MonST3R's depth and confidence masks on the first frame of each window, provides a reliable static reference for pose optimization. If the masks miss dynamic regions or the monocular depth is biased, the frozen scene injects corrupted gradients into the pose update. The paper acknowledges the dependence in §4.3 ('our experiments depend on MonST3R outputs') but never characterizes its magnitude. There is no comparison using ground-truth depth or segmentation, no synthetic perturbation study, and no ablation varying the confidence mask threshold. Without this sensitivity analysis, the reported pose errors cannot be separated from the quality of the upstream MonST3R predictions. Please add experiments that quantify the effect of mask and depth errors, for example by corrupting masks/depth in controlled amounts or by running the pipeline with alternative depth/mask sources, and report how pose error changes.
  2. [§5.1, Eq. (8), Table 1] The velocity-difference metric Δv is directly aligned with the smoothness loss in Eq. (8): L_smooth penalizes the second finite difference of camera position, i.e., changes in velocity, while Δv measures the average difference between predicted and ground-truth translation velocity. Therefore the 25x improvement in Δv (0.0258 to 0.00090 in Table 1) is to a significant degree a consequence of optimizing a smoothness objective, not an independent validation of trajectory accuracy or motion consistency. The paper should either stop presenting Δv as an independent success indicator or provide a metric that is not the direct target of the loss, such as the velocity error computed only on test-time without the smoothness term, or a separate metric like trajectory curvature error.
  3. [§5.1, Table 1] The best-performing variant (16-channel DINOv2) regresses rotation accuracy: RPE_r is 0.340, compared to 0.301 for SmallGS without DINOv2, 0.316 for MonST3R, and 0.320 for DROID-SLAM. The text says 'RPE_r is slightly higher than the baseline methods,' but 0.340 vs. 0.301 is a 13% relative regression, not 'slight.' This contradicts the claim that 'SmallGS with 16-channel DINOv2 features achieves the best overall performance.' The authors should either analyze why the DINOv2 variant hurts rotation and propose a remedy, or temper the claim to specify that the improvement is in translation only.
  4. [§4.1, §4.3, and Tables 1–4] The experimental evaluation has several methodological gaps. First, the 46 sequences are 'selected' without any stated selection criteria, raising the risk of cherry-picking. Second, no error bars, standard deviations, or statistical significance tests are reported, so it is unclear whether ATE differences like 0.00228 vs. 0.00268 are robust to sequence variability. Third, the PCA feature-channel selection is performed on the evaluation data itself (§4.3: 'selecting the top sixteen features'), which can bias results. Please describe the sequence-selection protocol, report per-sequence variance or significance, and use a held-out validation set for channel selection or justify why the PCA selection does not overfit.
minor comments (6)
  1. [Abstract and Conclusion] The abstract misspells 'DROID-SLAM' as 'DORID-SLAM'; the same typo appears in the Conclusion. Please correct.
  2. [§4.3] There is a typo: 'dynamic obejcts' should be 'dynamic objects.'
  3. [§3.2, Eq. (8)] The λ_c schedule is described only as 'increasing with the number of Gaussian splatting optimization iterations.' For reproducibility, the exact schedule (e.g., start/end values and step size) should be given.
  4. [§5.2, Table 3] The comparison 'SmallGS w/ pointmaps' vs. 'SmallGS w/ DINO' would be clearer if the table indicated explicitly that all rows use the same loss except for the initialization or feature modality; the text in §5.2 already says this, but the table caption could be clearer.
  5. [References] Reference [30] appears to have a garbled author list ('Evangelos Ververas Jifei Song ...'); please verify and format correctly.
  6. [§1 and §4.3] The claim that SmallGS does not require 'explicit feature correspondences' should be qualified: the pipeline depends on MonST3R, which internally uses pointmap correspondences for depth and masks. Clarify that the absence of explicit correspondences applies to SmallGS's own optimization, not to the overall system.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SmallGS's pose estimates come from an independent test-time optimization, and its dependence on MonST3R is an upstream input, not a circular re-derivation.

full rationale

SmallGS's derivation chain is self-contained. The frozen Gaussian splatting is initialized from MonST3R depth maps, confidence masks, and intrinsics, but the camera poses are obtained by minimizing a rendering loss (Eqs. 6, 9, 10) against RGB or DINOv2 feature maps, not by reading poses off MonST3R. No fitted parameter is renamed as a prediction: the PCA channel selection is a feature reduction, and the smoothness loss (Eq. 8) penalizes acceleration of the estimated trajectory without using ground-truth velocities, so the reported Delta-v metric is measured, not directly optimized. The acknowledged dependence on MonST3R outputs (Sec. 4.3: 'our experiments depend on MonST3R outputs for consistency') is an upstream dependency and a robustness risk, but it is not circular because MonST3R provides depth, masks, and intrinsics rather than the target poses. The only self-citation is reference [33] in related work, which is not load-bearing. Consequently, no circular step can be exhibited under the required standard.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on preprocessing priors from MonST3R (depth, masks, intrinsics), a frozen first-frame Gaussian scene, DINOv2 feature supervision, and a smoothness regularizer. No genuinely new physical or geometric entity is introduced; the free parameters are mostly hyperparameters, but the PCA channel selection is a data-dependent fit whose details are not disclosed.

free parameters (6)
  • sliding window size b = 15
    Set to 15 for all experiments; no ablation on window size is reported, and it directly controls drift and pose stability.
  • SSIM loss weight λs = 0.2
    Chosen without ablation; contributes to the total pose optimization objective.
  • smoothness weight λc schedule = varies 0 to 1, schedule not specified
    The schedule is not described; this term directly forces smooth trajectories and inflates the Δv metric.
  • number of DINOv2 feature channels f = 3 or 16
    Selected via PCA ranking; whether the PCA is fit on the evaluation sequences is not stated, which could tune to test data.
  • confidence mask binarization threshold = not specified
    MonST3R confidence maps are converted into semantic masks, but the threshold or procedure is not described.
  • optimization iterations and learning rates = GSplat defaults, exact values not stated
    The paper relies on 'default GSplat configuration' without reporting the actual settings, which are needed for replication.
assumptions (4)
  • domain assumption Adjacent frames in each segment have sufficient overlap and small relative motion so that identity-initialized poses can be recovered by local optimization.
    Sec. 3.2: 'We initialize the camera pose using the identity matrix'; Sec. 4.1 selects sequences with 'very small camera baselines' to make this hold.
  • domain assumption MonST3R-provided depth maps, confidence masks, and intrinsics are accurate enough to initialize the Gaussian scene; inaccuracies far from the camera are harmless because splatting emphasizes near-field Gaussians.
    Sec. 1: 'inaccuracies of scene geometries that are far away from the camera will not have significant impact'; Eq. 7 uses MonST3R depth and masks.
  • domain assumption Rendered DINOv2 features are stable under small viewpoint changes and encode enough geometry and semantics for pose supervision.
    Sec. 3.2: 'Visual features are less affected by lighting or perspective changes'; relies on Feature 3DGS [35] that features can be rendered through Gaussians.
  • domain assumption Camera motion in the evaluated small-baseline videos is locally smooth, justifying the smoothness loss.
    Eq. 8 penalizes acceleration between consecutive camera positions; if the true motion is not smooth, the loss introduces bias.

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Cite this review

Pith. "Pith review of SmallGS: Gaussian Splatting-based Camera Pose Estimation for Small-Baseline Videos." pith.science (2026). https://pith.science/paper/GENZAWMJ

@misc{pith2026250417810,
  author       = {Pith},
  title        = {Pith review of: SmallGS: Gaussian Splatting-based Camera Pose Estimation for Small-Baseline Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GENZAWMJ}},
  note         = {Machine review of arXiv:2504.17810}
}
read the original abstract

Dynamic videos with small baseline motions are ubiquitous in daily life, especially on social media. However, these videos present a challenge to existing pose estimation frameworks due to ambiguous features, drift accumulation, and insufficient triangulation constraints. Gaussian splatting, which maintains an explicit representation for scenes, provides a reliable novel view rasterization when the viewpoint change is small. Inspired by this, we propose SmallGS, a camera pose estimation framework that is specifically designed for small-baseline videos. SmallGS optimizes sequential camera poses using Gaussian splatting, which reconstructs the scene from the first frame in each video segment to provide a stable reference for the rest. The temporal consistency of Gaussian splatting within limited viewpoint differences reduced the requirement of sufficient depth variations in traditional camera pose estimation. We further incorporate pretrained robust visual features, e.g. DINOv2, into Gaussian splatting, where high-dimensional feature map rendering enhances the robustness of camera pose estimation. By freezing the Gaussian splatting and optimizing camera viewpoints based on rasterized features, SmallGS effectively learns camera poses without requiring explicit feature correspondences or strong parallax motion. We verify the effectiveness of SmallGS in small-baseline videos in TUM-Dynamics sequences, which achieves impressive accuracy in camera pose estimation compared to MonST3R and DORID-SLAM for small-baseline videos in dynamic scenes. Our project page is at: https://yuxinyao620.github.io/SmallGS

Figures

Figures reproduced from arXiv: 2504.17810 by the authors.

Figure 1
Figure 1. Camera pose estimation for small-baseline videos with SmallGS. Our method focuses on camera pose estimation in small￾baseline videos, updating the camera poses based on the rasterization of Gaussian splatting in the learned camera viewpoints. We achieved better and smoother results compared to the previous SOTA MonST3R. Abstract Dynamic videos with small baseline motions are ubiqui￾tous in daily life, especially on … view at source ↗
Figure 2
Figure 2. Pipeline of SmallGS. Our method follows the CF-3DGS pipeline, estimating camera poses in video segments. The process is: (1) Use MonST3R to predict depth maps, confidence masks, and camera intrinsics; (2) Lift the first frame’s depth map into a dense point cloud, masking dynamic objects using the confidence mask as a semantic mask; (3) Initialize and update Gaussian splatting with the first frame; (4) Freeze the Gau… view at source ↗
Figure 3
Figure 3. Fig. 3b compares the estimated camera trajectories of MonST3R and SmallGS with 16-channel DINOv2 features. Fig. 3a shows the same SmallGS trajectory compared to DROID. The red dashed line represents the ground truth. RP Er is slightly higher than the baseline methods, im￾provements in AT E and RP Et indicate that SmallGS provides high-precision camera position estimation. Fur￾thermore, the lowest ∆v suggests that Sm… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of ground-truth trajectories, MonST3R-predicted trajectories, and the SmallGS-learned trajectory with 16-channel [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Reference graph

Works this paper leans on

35 extracted references · 23 canonical work pages

  1. [1]

    Leap-vo: Long-term effective any point tracking for visual odometry

    Weirong Chen, Le Chen, Rui Wang, and Marc Pollefeys. Leap-vo: Long-term effective any point tracking for visual odometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19844– 19853, 2024. 3

    work page 2024

  2. [2]

    Monoslam: Real-time single camera slam

    Andrew J Davison, Ian D Reid, Nicholas D Molton, and Olivier Stasse. Monoslam: Real-time single camera slam. IEEE transactions on pattern analysis and machine intelli- gence, 29(6):1052–1067, 2007. 3

    work page 2007

  3. [3]

    Superpoint: Self-supervised interest point detection and description

    Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabi- novich. Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pages 224–236, 2018. 3

    work page 2018

  4. [4]

    Lsd- slam: Large-scale direct monocular slam

    Jakob Engel, Thomas Sch ¨ops, and Daniel Cremers. Lsd- slam: Large-scale direct monocular slam. In European con- ference on computer vision, pages 834–849. Springer, 2014. 3

    work page 2014

  5. [5]

    Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024

    Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang, Jian Zhang, Xinghao Ding, Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, Zhangyang Wang, and Yue Wang. Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024. 2, 3

    2024

  6. [6]

    Efros, and Xiaolong Wang

    Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, and Xiaolong Wang. Colmap-free 3d gaussian splat- ting. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 20796– 20805, 2024. 2, 3, 4, 6, 7

    work page 2024

  7. [7]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 3, 4

    2023

  8. [8]

    Ro- bust consistent video depth estimation

    Johannes Kopf, Xuejian Rong, and Jia-Bin Huang. Ro- bust consistent video depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1611–1621, 2021. 3

    work page 2021

Show all 35 references
  1. [9]

    Megasam: Accurate, fast, and ro- bust structure and motion from casual dynamic videos.arXiv preprint arXiv:2412.04463, 2024

    Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holyn- ski, and Noah Snavely. Megasam: Accurate, fast, and ro- bust structure and motion from casual dynamic videos.arXiv preprint arXiv:2412.04463, 2024. 2

    2024 arXiv

  2. [10]

    GS-CPR: Efficient camera pose refinement via 3d gaussian splatting

    Changkun Liu, Shuai Chen, Yash Sanjay Bhalgat, Siyan HU, Ming Cheng, Zirui Wang, Victor Adrian Prisacariu, and Tris- tan Braud. GS-CPR: Efficient camera pose refinement via 3d gaussian splatting. In The Thirteenth International Confer- ence on Learning Representations, 2025. 3

    2025

  3. [11]

    Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

    Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 20654–20664, 2024. 3

    2024

  4. [12]

    Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. arXiv preprint arXiv:2308.09713, 2023. 3

    2023 arXiv

  5. [13]

    Splatfields: Neural gaussian splats for sparse 3d and 4d re- construction

    Marko Mihajlovic, Sergey Prokudin, Siyu Tang, Robert Maier, Federica Bogo, Tony Tung, and Edmond Boyer. Splatfields: Neural gaussian splats for sparse 3d and 4d re- construction. In European Conference on Computer Vision (ECCV). Springer, 2024. 3

    2024

  6. [14]

    A multi- state constraint kalman filter for vision-aided inertial naviga- tion

    Anastasios I Mourikis and Stergios I Roumeliotis. A multi- state constraint kalman filter for vision-aided inertial naviga- tion. In Proceedings 2007 IEEE international conference on robotics and automation, pages 3565–3572. IEEE, 2007. 3

    2007

  7. [15]

    Orb-slam2: An open- source slam system for monocular, stereo, and rgb-d cam- eras

    Raul Mur-Artal and Juan D Tard ´os. Orb-slam2: An open- source slam system for monocular, stereo, and rgb-d cam- eras. IEEE transactions on robotics, 33(5):1255–1262, 2017. 3

    2017

  8. [16]

    Orb-slam: a versatile and accurate monocular slam system

    Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics , 31(5):1147–1163,

  9. [17]

    Dtam: Dense tracking and mapping in real-time

    Richard A Newcombe, Steven J Lovegrove, and Andrew J Davison. Dtam: Dense tracking and mapping in real-time. In 2011 international conference on computer vision , pages 2320–2327. IEEE, 2011. 3

    2011

  10. [18]

    Maxime Oquab, Timoth ´ee Darcet, Theo Moutakanni, Huy V . V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Rus- sell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang- Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Ni...

    2023

  11. [19]

    Vi- sion transformers for dense prediction

    Ren ´e Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vi- sion transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179–12188, 2021. 3, 4

    2021

  12. [20]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Com- puter Vision and Pattern Recognition (CVPR), 2016. 2, 3

    2016

  13. [21]

    Pixelwise view selection for un- structured multi-view stereo

    Johannes Lutz Sch ¨onberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for un- structured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016. 2, 3

    2016

  14. [22]

    Dytanvo: Joint refinement of visual odometry and motion segmentation in dynamic environments

    Shihao Shen, Yilin Cai, Wenshan Wang, and Sebastian Scherer. Dytanvo: Joint refinement of visual odometry and motion segmentation in dynamic environments. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 4048–4055. IEEE, 2023. 3

    2023

  15. [23]

    A benchmark for the evalua- tion of rgb-d slam systems

    J ¨urgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evalua- tion of rgb-d slam systems. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pages 573–580. IEEE, 2012. 2, 6, 9

    2012

  16. [24]

    imap: Implicit mapping and positioning in real-time

    Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J Davi- son. imap: Implicit mapping and positioning in real-time. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6229–6238, 2021. 3

    2021

  17. [25]

    Robust monocular slam in dynamic environ- ments

    Wei Tan, Haomin Liu, Zilong Dong, Guofeng Zhang, and Hujun Bao. Robust monocular slam in dynamic environ- ments. In 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pages 209–218. IEEE, 2013. 3

    2013

  18. [26]

    Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras

    Zachary Teed and Jia Deng. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. Advances in neu- ral information processing systems, 34:16558–16569, 2021. 3, 6

    2021

  19. [27]

    Dust3r: Geometric 3d vi- sion made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vi- sion made easy. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20697– 20709, 2024. 2, 3

    2024

  20. [28]

    4d gaussian splatting for real-time dynamic scene render- ing

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4d gaussian splatting for real-time dynamic scene render- ing. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 2...

    2024

  21. [29]

    gsplat: An open-source library for Gaussian splatting

    Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, and Angjoo Kanazawa. gsplat: An open-source library for Gaussian splatting. arXiv preprint arXiv:2409.06765, 2024. 4, 5, 6

    2024 arXiv

  22. [30]

    Zerogs: Training 3d gaussian splatting from unposed images

    Evangelos Ververas Jifei Song Jiankang Deng Gim Hee Lee Yu Chen, Rolandos Alexandros Potamias. Zerogs: Training 3d gaussian splatting from unposed images. In arXiv, 2024. 2, 3

    2024

  23. [31]

    Monst3r: A simple approach for estimat- ing geometry in the presence of motion

    Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jam- pani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming- Hsuan Yang. Monst3r: A simple approach for estimat- ing geometry in the presence of motion. arXiv preprint arXiv:2410.03825, 2024. 2, 3, 6

    arXiv

  24. [32]

    Flowfusion: Dynamic dense rgb- d slam based on optical flow

    Tianwei Zhang, Huayan Zhang, Yang Li, Yoshihiko Naka- mura, and Lei Zhang. Flowfusion: Dynamic dense rgb- d slam based on optical flow. In 2020 IEEE international conference on robotics and automation (ICRA), pages 7322–

    2020

  25. [33]

    Degrees of freedom matter: Inferring dy- namics from point trajectories

    Yan Zhang, Sergey Prokudin, Marko Mihajlovic, Qianli Ma, and Siyu Tang. Degrees of freedom matter: Inferring dy- namics from point trajectories. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 3

    2024

  26. [34]

    Structure and motion from casual videos

    Zhoutong Zhang, Forrester Cole, Zhengqi Li, Michael Ru- binstein, Noah Snavely, and William T Freeman. Structure and motion from casual videos. In European Conference on Computer Vision, pages 20–37. Springer, 2022. 3

    2022

  27. [35]

    Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields, 2024

    Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Ze- hao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, and Achuta Kadambi. Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields, 2024. 3, 4, 5, 6

    2024

Pith tools

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