Pith. sign in

REVIEW 3 major objections 8 minor 31 references

Large-scale visual SLAM for in-the-wild videos

T0 review · 3 major / 8 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read This paper claims that one pipeline turns casual online videos into single, continuous 3D reconstructions, and reports zero-break maps on 15-minute clips where COLMAP and GLOMAP fragment.

desk verdict Competent system integration with real robustness gains on casual videos, but the evaluation has a dimensionally broken break metric and no ground truth to separate smooth drift from accuracy, so the 'new baseline' claim overreaches. read the letter →

arxiv 2504.20496 v1 pith:GJ6B6E3U submitted 2025-04-29 cs.CV

Shuo Sun , Torsten Sattler , Malcolm Mielle , Achim J. Lilienthal , Martin Magnusson This is my paper · ORCID
classification cs.CV
keywords visualSLAMstructure-from-motionbundleadjustmentmonoculardepthloopclosurein-the-wildvideocameracalibration3Dreconstruction
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 show that a single 3D reconstruction pipeline can take ordinary, casually filmed videos from the internet—handheld walking tours, drone passes, footage with moving pedestrians and sky—and turn them into one continuous camera trajectory and 3D map, rather than the fragmented collection of models that current structure-from-motion systems produce. The authors argue that the key failures on such footage are small-parallax motion, unknown camera calibration, dynamic objects, and long-term drift, and that each can be handled by a specific module: automatic intrinsics recovery, semantic masking, monocular-depth-regularized bundle adjustment, and loop closure. If the claim is right, robot maps and tourist-site reconstructions could be built from existing online video instead of dedicated surveying equipment. The paper evaluates on roughly 15-minute clips without ground-truth poses, using registered-image counts, trajectory breaks, runtime, and NeRF rendering quality, and reports that only its method produces zero-break, single-model reconstructions while running in minutes instead of hours.

What carries the argument

The load-bearing mechanism is bundle adjustment regularized by monocular depth. Patch correspondences come from a recurrent optical-flow network (DPVO), and each new frame's estimated depth map is rescaled to the current reconstruction's median depth and added as a soft constraint in the re-projection error, so the optimizer is told where geometry should lie even when parallax gives no triangulation signal. Around this core, the pipeline uses semantic segmentation masks to suppress dynamic objects and sky, NetVLAD descriptors with a three-consecutive-frame check to trigger loop closures, SIM(3) pose-graph optimization to absorb scale drift, and a final re-triangulation with intrinsic refinement.

What would settle it

Take a video recorded in the same conditions with a known trajectory from GPS or IMU, run the pipeline, and compare the recovered path to the ground truth; if the pipeline reports zero breaks and high rendering PSNR while the absolute trajectory error grows steadily past, say, ten percent of the path length, the central claim of consistent reconstruction is not established.

Watch

Extended reading notes

Core claim

The paper's central claim is that robustness to uncontrolled video is not a matter of a better feature matcher but of combining four safeguards around a deep optical-flow odometer: recover the focal length from an early structure-from-motion pass instead of assuming it; mask out dynamic objects and unconstrained sky before correspondence estimation; add a monocular depth prior as a regularizer in bundle adjustment so that nearly pure rotations and forward motion do not destabilize the optimization; and close loops with place-recognition descriptors refined by scale-aware pose-graph optimization. On seven real online video sequences, the paper reports that this pipeline registers essentially every frame, produces exactly one model, and produces zero detected trajectory breaks, whereas COLMAP splits scenes into multiple models and GLOMAP silently absorbs failed registrations as breaks. The authors state the system establishes a new baseline for reconstruction from casual in-the-wild videos, with the explicit caveat that the method might be less precise where the baselines align well.

Load-bearing premise

The load-bearing premise is that the evaluation metrics actually measure reconstruction quality: if a trajectory drifts smoothly in a consistent wrong direction, the break criterion of a jump larger than ten times the local average, plus the rendering scores, can all look good while the map is wrong, since there are no ground-truth poses.

Editorial extensions

If this is right

  • A 15-minute walking-tour or drone video can be turned into a single 3D model, so maps for robot deployment could be sourced from existing online footage.
  • Downstream tasks that need consistent poses—visual localization, novel view synthesis, and scene understanding—could run on reconstructions that previous structure-from-motion methods fragment.
  • The reported runtime, minutes instead of hours, makes large-scale monocular reconstruction from long videos practical on a single machine.
  • Because the pipeline never assumes known calibration, it extends to arbitrary unknown cameras, including videos shot on uncalibrated phones and action cameras.
  • The proposed break metric and NeRF-rendering evaluation give future methods a way to compare robustness when ground-truth poses are unavailable.

Reading between the lines

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

  • Editorial inference: if a smoothly drifting trajectory can pass the paper's metrics, the most informative next experiment is a GPS or IMU ground-truth comparison on the same videos; that would settle whether the zero-break result implies an accurate map or only a continuous one.
  • Editorial inference: the depth-regularized bundle adjustment is generic enough to be bolted onto other correspondence sources, such as detector-free matchers or learned point maps, which would test whether it is the depth prior or the optical-flow backbone that carries the robustness.
  • Editorial inference: a direct ablation varying the regularization weight would show how much of the gain depends on precisely calibrating the depth prior's influence; the paper reports component ablations but not a weight sweep.
  • Editorial inference: the same pipeline could be pointed at archival footage to reconstruct places as they once were, a use case the paper does not mention but that follows directly from not requiring calibration or controlled motion.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 8 minor

Summary. The paper presents a robust visual SLAM pipeline for long, casual, in-the-wild videos. The pipeline initializes camera intrinsics via structure-from-motion on a few selected frames, uses DPVO for frame-to-frame correspondence, masks dynamic objects and unconstrained regions via a semantic segmentation model, regularizes bundle adjustment with monocular depth estimates, and integrates NetVLAD-based loop closure with SIM(3) pose-graph optimization, followed by post-refinement with re-triangulation and optional global bundle adjustment. Experiments on eight YouTube tour videos compare the method with COLMAP and GLOMAP in terms of registered images, number of models, trajectory breaks, runtime, and NeRF-rendered PSNR. The paper claims that the proposed system establishes a new baseline for visual reconstruction from casual uncontrolled videos, producing more consistent reconstructions over longer sequences than previously achieved.

Significance. If the central claim holds, this is a practically valuable contribution: a pipeline that can reconstruct long, unconstrained video sequences into a single contiguous trajectory far faster than classical SfM, with potential applications in robotics and scene understanding. The paper is transparent about the lack of ground-truth poses and explicitly concedes the possibility of lower precision. The system is well engineered, combining several recent learning-based components into an end-to-end robust pipeline, and the evaluation covers a diverse set of real-world videos. The main weakness is that the proposed evaluation metrics cannot distinguish a smooth but drifting trajectory from a consistently accurate one, which directly affects the strength of the stated claim.

major comments (3)
  1. [Section IV-A.3] The break metric as written is dimensionally inconsistent and scale-dependent. The paper defines cDelta_t = ||t_i - t_{i+1}|| / mean(||Delta_{i-k:i+k}||), which is a dimensionless ratio, but then states 'We define a break if cDelta_t > 10 mean(Delta_t)'. The right-hand side has units of translation (e.g., meters), while the left-hand side is dimensionless. If implemented literally, the threshold changes with the scale of the reconstruction, and the definition is not well posed. Please rewrite the condition (e.g., 'cDelta_t > 10' or 'Delta_t > 10 mean(Delta_t)') and specify the window size k.
  2. [Section IV-A.3 and IV-B] The break metric only detects abrupt jumps in the trajectory; it cannot detect smooth, gradual drift. A trajectory that consistently drifts away from the true path while maintaining locally consistent step sizes would produce zero breaks and a high number of registered images, yet the reconstructed 3D model could be globally distorted. The paper itself concedes in Section IV-B that 'our method might be less precise, but more robust overall', and Table I shows cases where baselines achieve higher PSNR on segments they reconstruct well (e.g., Helsingborg-1 frames 0-500: COLMAP 16.41 vs. Ours 14.05). Without ground-truth poses or an independent drift measure, the central claim of 'more consistent reconstructions' is not fully established. I recommend adding an evaluation on sequences with ground-truth poses (e.g., a standard SLAM benchmark or synthetic in-the-wild-like sequences) reporting ATE/RPE, or, where GPS is available, quantifying drift against those references.
  3. [Table II] The 'Time Average (min)' row is internally inconsistent. Summing the per-sequence times gives 2183 minutes for COLMAP, 960 minutes for GLOMAP, and 98 minutes for Ours, while the table reports 2169, 956, and 12, respectively. The value for Ours (12) appears to be the average (98/8 = 12.25), but the values for COLMAP and GLOMAP are close to the totals, not the averages. This inconsistency undermines the reported runtime comparison and must be corrected.
minor comments (8)
  1. [Section IV-A.3] The notation for the break metric is inconsistent: the text uses cDelta_t, Delta_t, and Delta_{i-k:i+k} with varying subscripts. Please standardize the notation and define the local window k explicitly.
  2. [Figure 3] The caption contains a typo: 'PNSR' should be 'PSNR'.
  3. [Section III-B.1] The text refers to 'the original DVPO paper'; this should be 'DPVO' (Deep Patch Visual Odometry).
  4. [Section IV-D] The focal length comparison in Figure 6 is purely qualitative and does not report ground-truth focal lengths or errors. Please provide quantitative errors or state that no ground truth is available.
  5. [Section V] The conclusion states that the method handles sequences 'over 1 km in length', but no measurement or reference supports this number. Please substantiate or remove the claim.
  6. [Table II] The YouTube URLs contain spaces and line breaks; please ensure the links are properly formatted and verified.
  7. [Section III-B.2] The paper mentions masking dynamic objects and 'less-constrained areas' but does not specify the semantic classes used. Please list the classes for reproducibility.
  8. [Section IV-A.3] The PSNR evaluation relies only on NeRF rendering, which is known to struggle with large-scale outdoor scenes (as the paper acknowledges). Consider reporting additional perceptual metrics (e.g., LPIPS, SSIM) or a simpler rendering-based consistency metric.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical evaluation against external baselines; break metric is an evaluation limitation, not a circular derivation.

full rationale

This paper is an empirical systems paper rather than a derivation chain. The proposed pipeline combines off-the-shelf components (DPVO, Mask2Former, Metric3D, NetVLAD, and GLOMAP only for initial intrinsics) and is evaluated against external baselines COLMAP and GLOMAP on in-the-wild videos. No parameter is fitted to the evaluation data and then renamed as a prediction. The only self-referential element is the 'break' definition in Section IV-A.3, where a jump ratio is thresholded using statistics from the same local trajectory; this is a metric-definition concern, and the written formula even compares a dimensionless ratio to a quantity with units, but it is not a case where a claimed result reduces by construction to its own input. The pipeline does not optimize that metric. The central claims are supported by external comparisons: registered-image counts, number of models, NeRF rendering PSNR, runtime, GPS comparisons where available, and ablations. The authors' explicit concession that 'our method might be less precise, but more robust overall' is an honest limitation statement, not evidence of circularity. There are no load-bearing self-citations, no imported uniqueness theorems, and no ansatz smuggled in via citation. Accordingly, the circularity score is 0.

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

The central claim rests on several off-the-shelf components, each with unverified assumptions for this new domain, and on hand-set evaluation parameters. No new physical or conceptual entities are introduced; the break metric is an evaluation construct, not an entity.

free parameters (4)
  • depth regularization weight mu = not reported
    Introduced in Eq. (2) to balance reprojection error and the depth prior; no value, range, or sensitivity analysis is given.
  • break threshold multiplier = 10
    Section IV-A.3 defines a break when cDelta_t exceeds 10 times the local mean translation; the choice is not justified or tested for sensitivity.
  • minimum loop-closure frame count N = not reported
    Section III-C requires N consecutive NetVLAD matches to register a loop closure, but N is never specified.
  • semantic mask class set = not reported
    Section III-B.2 masks dynamic objects and sky, but the exact class list and segmentation confidence thresholds are not given.
assumptions (6)
  • domain assumption Monocular depth from Metric3D is metrically meaningful and can be aligned by median scaling.
    Eq. (2) adds a depth residual with scale alpha_i from median(D_i)/median(P[d](i-3:i)); if this alignment fails, the depth term biases the reconstruction.
  • domain assumption Semantic masking removes only harmful regions.
    Section III-B.2 excludes patches on dynamic objects and sky; if segmentation misses moving objects or removes static structure, correspondence quality changes unpredictably.
  • domain assumption DPVO patch correspondences are reliable under large rotation and low texture.
    The pipeline inherits DPVO's optical-flow-based data association without re-validation on long in-the-wild videos; this is the main data source for bundle adjustment.
  • domain assumption NetVLAD loop closures with three consecutive frame matches are correct.
    Section III-C registers loops using this rule; false positives would corrupt SIM(3) pose-graph optimization.
  • domain assumption A pinhole camera model without distortion is sufficient for these videos.
    Section III-A assumes pinhole intrinsics; real phone and drone videos may have lens distortion, which can bias long-range reconstruction.
  • domain assumption COLMAP and GLOMAP are run in representative configurations.
    Section IV-A.4 uses a fast COLMAP variant and SIFT matching with 20 neighbors; the fairness of the comparison depends on this tuning.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Large-scale visual SLAM for in-the-wild videos." pith.science (2026). https://pith.science/paper/GJ6B6E3U

@misc{pith2026250420496,
  author       = {Pith},
  title        = {Pith review of: Large-scale visual SLAM for in-the-wild videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GJ6B6E3U}},
  note         = {Machine review of arXiv:2504.20496}
}
read the original abstract

Accurate and robust 3D scene reconstruction from casual, in-the-wild videos can significantly simplify robot deployment to new environments. However, reliable camera pose estimation and scene reconstruction from such unconstrained videos remains an open challenge. Existing visual-only SLAM methods perform well on benchmark datasets but struggle with real-world footage which often exhibits uncontrolled motion including rapid rotations and pure forward movements, textureless regions, and dynamic objects. We analyze the limitations of current methods and introduce a robust pipeline designed to improve 3D reconstruction from casual videos. We build upon recent deep visual odometry methods but increase robustness in several ways. Camera intrinsics are automatically recovered from the first few frames using structure-from-motion. Dynamic objects and less-constrained areas are masked with a predictive model. Additionally, we leverage monocular depth estimates to regularize bundle adjustment, mitigating errors in low-parallax situations. Finally, we integrate place recognition and loop closure to reduce long-term drift and refine both intrinsics and pose estimates through global bundle adjustment. We demonstrate large-scale contiguous 3D models from several online videos in various environments. In contrast, baseline methods typically produce locally inconsistent results at several points, producing separate segments or distorted maps. In lieu of ground-truth pose data, we evaluate map consistency, execution time and visual accuracy of re-rendered NeRF models. Our proposed system establishes a new baseline for visual reconstruction from casual uncontrolled videos found online, demonstrating more consistent reconstructions over longer sequences of in-the-wild videos than previously achieved.

Figures

Figures reproduced from arXiv: 2504.20496 by the authors.

Figure 1
Figure 1. Overview of our method. Given a video stream, we extract frames from the video sequentially. We first run an efficient global SfM process to estimate the camera intrinsic parameters Kinit (Section III-A). Using an off-the-shelf semantic segmentation model, we prune the potential objects in the image (Section III-B.2) when estimating correspondence between frames. Correspondences are estimated across frames by DPVO; … view at source ↗
Figure 2
Figure 2. The camera poses (red in the map) on the sequence “Helsingborg Seq-1” across frames 500–1000. COLMAP has a break in the trajectory (circled in blue); GLOMAP tacitly fails to register images; our method produces smooth and continuous trajectories. breaks in the trajectory, resulting in low rendering results. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The rendering results at the camera pose where COLMAP breaks. TABLE III: NeRF rendering results on smaller clipped parts on “Helsingborg Seq-1”. Sequence COLMAP [2] GLOMAP [3] Ours frame 0–500 16.41 16.22 14.05 frame 500–1000 14.99 9.26 15.74 pipeline [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Reconstruction result visualization. Our method generally achieves smooth and continuous trajectories without breaks. COLMAP often produces a model only for part of the path. GLOMAP struggles to produce consistent results in these large-scale environments. GPS tracks a…
Figure 5
Figure 5. Figure 5: Ablation study: on the “Yanshan Park” sequence, we show the effectiveness of the proposed modules. To handle in-the-wild videos, prior depth and pruning dynamics in the view greatly help to improve the robustness. (a) MoGE (f = 396.69) (b) Mast3R (f = 338.61) (c) GeoCa…
Figure 6
Figure 6. Figure 6: Reconstruction results by different ways of estimating focals. our proposed method robustly produces longer sequences without breaks, and does so in a fraction of the time. Future research directions include methods for further reducing drift while maintaining consiste…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 25 canonical work pages

  1. [1]

    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”. In: IEEE trans- actions on robotics 31.5 (2015), pp. 1147–1163

    work page 2015

  2. [2]

    Structure-from-motion revisited

    Johannes L Schonberger and Jan-Michael Frahm. “Structure-from-motion revisited”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 4104–4113

    work page 2016

  3. [3]

    Global structure-from-motion revisited

    Linfei Pan et al. “Global structure-from-motion revisited”. In: European Conference on Computer Vision . Springer. 2024, pp. 58–77

    work page 2024

  4. [4]

    VGGSfM: Visual geometry grounded deep structure from motion

    Jianyuan Wang et al. “VGGSfM: Visual geometry grounded deep structure from motion”. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2024, pp. 21686–21697

    work page 2024

  5. [5]

    MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion

    Bardienus Duisterhof et al. “MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion”. In: arXiv preprint arXiv:2409.19152 (2024)

    arXiv 2024

  6. [6]

    Deep patch visual odometry

    Zachary Teed, Lahav Lipson, and Jia Deng. “Deep patch visual odometry”. In: Advances in Neural Information Processing Systems 36 (2024)

    work page 2024

  7. [7]

    Masked-attention mask transformer for universal image segmentation

    Bowen Cheng et al. “Masked-attention mask transformer for universal image segmentation”. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2022, pp. 1290–1299

    work page 2022

  8. [8]

    Metric3d: Towards zero-shot metric 3d prediction from a single image

    Wei Yin et al. “Metric3d: Towards zero-shot metric 3d prediction from a single image”. In: Proceedings of the IEEE/CVF International Conference on Computer Vision . 2023, pp. 9043–9053

    work page 2023

Show all 31 references
  1. [9]

    Distinctive image features from scale-invariant keypoints

    David G Lowe. “Distinctive image features from scale-invariant keypoints”. In: International journal of computer vision 60 (2004), pp. 91–110

    2004

  2. [10]

    Superpoint: Self-supervised interest point detection and description

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

    2018

  3. [11]

    R2d2: Reliable and repeatable detector and descriptor

    Jerome Revaud et al. “R2d2: Reliable and repeatable detector and descriptor”. In: Advances in neural information processing systems 32 (2019)

    2019

  4. [12]

    Detector-free structure from motion

    Xingyi He et al. “Detector-free structure from motion”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2024, pp. 21594–21603

    2024

  5. [13]

    Theia Multiview Geometry Library: Tutorial & Reference

    Chris Sweeney. Theia Multiview Geometry Library: Tutorial & Reference . http://theia-sfm.org

  6. [14]

    Depth-guided sparse structure- from-motion for movies and TV shows

    Sheng Liu, Xiaohan Nie, and Raffay Hamid. “Depth-guided sparse structure- from-motion for movies and TV shows”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2022, pp. 15980– 15989

    2022

  7. [15]

    MegaSaM: Accurate, fast, and robust structure and motion from casual dynamic videos

    Zhengqi Li et al. “MegaSaM: Accurate, fast, and robust structure and motion from casual dynamic videos”. In: arXiv preprint arXiv:2412.04463 (2024)

    2024 arXiv

  8. [16]

    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”. In: Advances in neural information processing systems 34 (2021), pp. 16558–16569

    2021

  9. [17]

    Dust3r: Geometric 3d vision made easy

    Shuzhe Wang et al. “Dust3r: Geometric 3d vision made easy”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2024, pp. 20697–20709

    2024

  10. [18]

    Grounding image matching in 3d with MASt3R

    Vincent Leroy, Yohann Cabon, and J ´erˆome Revaud. “Grounding image matching in 3d with MASt3R”. In: European Conference on Computer Vision. Springer. 2024, pp. 71–91

    2024

  11. [19]

    Light3R-SfM: Towards Feed-forward Structure-from- Motion

    Sven Elflein et al. “Light3R-SfM: Towards Feed-forward Structure-from- Motion”. In: arXiv preprint arXiv:2501.14914 (2025)

    2025 arXiv

  12. [20]

    MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors

    Riku Murai, Eric Dexheimer, and Andrew J Davison. “MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors”. In: arXiv preprint arXiv:2412.12392 (2024)

    2024 arXiv

  13. [21]

    NetVLAD: CNN architecture for weakly supervised place recognition

    Relja Arandjelovic et al. “NetVLAD: CNN architecture for weakly supervised place recognition”. In: Proceedings of the IEEE conference on computer vision and pattern recognition . 2016, pp. 5297–5307

    2016

  14. [22]

    RAFT: Recurrent all-pairs field transforms for optical flow

    Zachary Teed and Jia Deng. “RAFT: Recurrent all-pairs field transforms for optical flow”. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer. 2020, pp. 402–419

    2020

  15. [23]

    Scale Drift-Aware Large Scale Monocular SLAM

    Hauke Strasdat, JMM Montiel, and Andrew J Davison. “Scale Drift-Aware Large Scale Monocular SLAM”. In: Robotics: Science and Systems . V ol. 2

  16. [24]

    EGO-SLAM: A robust monocular SLAM for egocentric videos

    Suvam Patra et al. “EGO-SLAM: A robust monocular SLAM for egocentric videos”. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE. 2019, pp. 31–40

    2019

  17. [25]

    A benchmark for the evaluation of RGB-D SLAM systems

    J ¨urgen Sturm et al. “A benchmark for the evaluation of RGB-D SLAM systems”. In: 2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE. 2012, pp. 573–580

    2012

  18. [26]

    The Replica dataset: A digital replica of indoor spaces

    Julian Straub et al. “The Replica dataset: A digital replica of indoor spaces”. In: arXiv preprint arXiv:1906.05797 (2019)

    2019 arXiv

  19. [27]

    Scene coordinate reconstruction: Posing of image col- lections via incremental learning of a relocalizer

    Eric Brachmann et al. “Scene coordinate reconstruction: Posing of image col- lections via incremental learning of a relocalizer”. In: European Conference on Computer Vision . Springer. 2024, pp. 421–440

    2024

  20. [28]

    Nerfstudio: A modular framework for neural radiance field development

    Matthew Tancik et al. “Nerfstudio: A modular framework for neural radiance field development”. In: ACM SIGGRAPH 2023 Conference Proceedings . 2023, pp. 1–12

    2023

  21. [29]

    Block-NeRF: Scalable large scene neural view synthesis

    Matthew Tancik et al. “Block-NeRF: Scalable large scene neural view synthesis”. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2022, pp. 8248–8258

    2022

  22. [30]

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

    Ruicheng Wang et al. “MoGe: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision”. In: arXiv preprint arXiv:2410.19115 (2024)

    2024 arXiv

  23. [31]

    GeoCalib: Learning Single-image Calibration with Geometric Optimization

    Alexander Veicht et al. “GeoCalib: Learning Single-image Calibration with Geometric Optimization”. In: European Conference on Computer Vision . Springer. 2024, pp. 1–20

    2024

Pith tools

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