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Fusing RGB, depth and event cues produces sharper interpolated RGB-D frames with better geometric consistency.

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UniRED proposes a multimodal RGB-D video interpolation method guided by event data that extracts fused cues, refines bidirectional flow for RGB and depth, and synthesizes frames via warping, plus releases a new RGB-D-Event dataset.

T0 review reviewed 2026-06-26 challenge →

load-bearing objection UniRED adds event cues to RGB-D interpolation via a fused pipeline and new dataset, with a design that targets the under-constrained motion problem and no major internal issues visible.

arxiv 2606.24282 v1 pith:7ONYYEL3 submitted 2026-06-23 cs.CV

UniRED: Unified RGB-D Video Frame Interpolation with Event Guidance

Yinuo Zhang , Guangshun Wei , Yuanfeng Zhou , Yiran Shen This is my paper
classification cs.CV
keywords RGB-D video interpolationevent camera guidancemultimodal fusionbidirectional flow estimationdepth map synthesisframe interpolationdynamic scene reconstruction
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper seeks to solve low frame-rate limits in RGB-D cameras by developing a single framework that interpolates missing frames using appearance from RGB, geometry from depth, and dense motion from events. It claims this joint use overcomes the under-constrained problem of estimating motion from only two boundary frames in complex scenes. Existing RGB methods produce blurry boundaries and inconsistent depth when applied to RGB-D data, while the new approach refines bidirectional flows separately for each modality before warping and blending. The authors also release a new RGB-D-Event dataset to enable training. A reader would care because accurate high-rate RGB-D video directly supports motion analysis, dynamic scene understanding, and 3D reconstruction.

Core claim

The UniRED method first extracts and fuses RGB, depth and event cues, estimates bidirectional flow with motion basis refinement for RGB and Z-axial refinement for depth, then synthesizes the target RGB-D frame through bidirectional warping and soft blending, yielding higher photometric fidelity in RGB and stronger geometric accuracy in depth than prior approaches on both public benchmarks and the new dataset.

What carries the argument

Multimodal cue fusion followed by bidirectional flow estimation with modality-specific refinements (motion basis for RGB, Z-axial for depth) and soft blending synthesis.

Load-bearing premise

Combining RGB, depth and event signals plus the bidirectional flow refinements is sufficient to constrain motion estimation when two-frame interpolation alone is under-determined.

What would settle it

Quantitative results on a test set of rapid non-rigid motion scenes showing no gain in RGB PSNR or depth error metrics over strong two-frame RGB baselines would falsify the central performance claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • RGB interpolation reaches superior photometric fidelity compared with existing RGB-only methods.
  • Depth interpolation achieves stronger geometric accuracy than methods that ignore event cues.
  • The approach handles complex dynamic scenes where two-frame motion estimation is ambiguous.
  • The released RGB-D-Event dataset enables supervised training of tri-modal interpolation models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Higher-rate RGB-D output could improve downstream tasks such as real-time object tracking that rely on consistent depth over time.
  • The same fusion-plus-refinement pattern might extend to other sensor pairs that combine slow dense measurements with fast sparse events.
  • Testing the method on longer sequences would reveal whether accumulated warping errors remain controlled across multiple interpolated frames.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 2 minor

Summary. The paper introduces UniRED, a unified multimodal framework for RGB-D video frame interpolation that integrates RGB appearance, depth geometry, and event-based temporal cues. The method extracts and fuses these cues, estimates bidirectional flow with motion basis refinement for RGB and Z-axial refinement for depth, and synthesizes the target frame using bidirectional warping and soft blending. A new RGB-D-Event dataset is constructed, and experiments on public benchmarks and the new dataset show superior photometric fidelity and geometric accuracy compared to existing approaches.

Significance. If the empirical results hold, this work provides a practical solution to the under-constrained problem of two-frame RGB-D interpolation in dynamic scenes by leveraging event cameras' high temporal resolution. The construction of a new tri-modal dataset is a valuable contribution that can facilitate future research in multimodal video processing. The approach directly addresses limitations of RGB-only methods in producing blurry boundaries and degraded geometric consistency.

minor comments (2)
  1. [Abstract] The abstract summarizes the method and performance claims but does not include any quantitative metrics, ablation summaries, or dataset statistics; adding one or two key numbers would strengthen the summary paragraph.
  2. Figure captions and axis labels should explicitly distinguish the RGB, depth, and event input channels to improve readability when comparing against baselines.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our work, the recognition of its practical value for RGB-D interpolation in dynamic scenes, and the recommendation for minor revision. We are pleased that the contributions of the unified multimodal framework and the new tri-modal dataset are acknowledged.

Circularity Check

0 steps flagged

No circularity; empirical method with no derivation chain

full rationale

The paper presents an empirical neural architecture for RGB-D event-guided interpolation (cue fusion, bidirectional flow with motion-basis and Z-axial refinements, soft blending) plus a new dataset. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation load-bearing uniqueness theorems appear in the provided text. All claims reduce to reported benchmark performance rather than any internal reduction to inputs by construction. This is the normal case for a methods paper; the derivation chain is empty and therefore cannot be circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; ledger left empty.

reviewed 2026-06-26 · how reviews work

0 comments
Cite this review

Pith. "Pith review of UniRED: Unified RGB-D Video Frame Interpolation with Event Guidance." pith.science (2026). https://pith.science/paper/7ONYYEL3

@misc{pith2026260624282,
  author       = {Pith},
  title        = {Pith review of: UniRED: Unified RGB-D Video Frame Interpolation with Event Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7ONYYEL3}},
  note         = {Machine review of arXiv:2606.24282}
}
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read the original abstract

High frame-rate RGB-D videos are crucial for a variety of downstream tasks, including motion analysis, dynamic scene understanding, and 3D reconstruction. However, due to hardware and sensing constraints, practical RGB-D cameras are typically limited to low frame rates, making it difficult to capture rapid scene dynamics. Existing video interpolation methods have achieved strong performance on RGB data, but they are not readily applicable to RGB-D scenarios, where they often yield blurry boundaries, visible artifacts, and degraded geometric consistency. Furthermore, motion estimation from only two boundary frames is inherently under-constrained in complex dynamic scenes. Event cameras, by contrast, provide asynchronous measurements with ultra-high temporal resolution, offering dense motion cues. In this paper, we propose a unified multimodal framework for RGB-D video interpolation that jointly exploits RGB appearance, depth geometry, and event-based temporal cues. Specifically, it first extracts and fuses RGB, depth and event cues, then estimates bidirectional flow with motion basis refinement for RGB and Z-axial refinement for depth, and finally synthesizes the target RGB-D frame via bidirectional warping and soft blending. In addition, we construct a new RGB-D-Event dataset to alleviate the scarcity of tri-modal training data. Extensive experiments on a public benchmark and the proposed dataset demonstrate that our method achieves superior photometric fidelity for RGB interpolation and stronger geometric accuracy for depth interpolation than existing approaches.

Figures

Figures reproduced from arXiv: 2606.24282 by Guangshun Wei, Yinuo Zhang, Yiran Shen, Yuanfeng Zhou.

Figure 1
Figure 1. Figure 1: The pipeline of UniRED: it consists of three major modules as shaded in different colors along with the input modalities. From left to right, the model takes consecutive RGB frames, depth maps, and the event stream as input. Modality-specific encoders and a tri-modal fusion module first extract and exchange complementary features across RGB, depth, and events. The motion-aware flow estimation module then p… view at source ↗
Figure 3
Figure 3. Figure 3: Left: our multimodal data capture platform, consisting of a DVSense [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison on VECtor and SyncRDE-60 at [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

56 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    Real- time camera tracking: When is high frame-rate best?

    A. Handa, R. A. Newcombe, A. Angeli, and A. J. Davison, “Real- time camera tracking: When is high frame-rate best?” inEuropean Conference on Computer Vision. Springer, 2012, pp. 222–235

    2012

  2. [2]

    Dynamicfusion: Recon- struction and tracking of non-rigid scenes in real-time,

    R. A. Newcombe, D. Fox, and S. M. Seitz, “Dynamicfusion: Recon- struction and tracking of non-rigid scenes in real-time,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 343–352

    2015

  3. [3]

    Texture mapping for 3d reconstruction with rgb-d sensor,

    Y . Fu, Q. Yan, L. Yang, J. Liao, and C. Xiao, “Texture mapping for 3d reconstruction with rgb-d sensor,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4645–4653

    2018

  4. [4]

    Monge-ampere regularization for learning arbitrary shapes from point clouds,

    C. Yang, Y . Zhou, G. Wei, L. Ma, J. Hou, Y . Liu, and W. Wang, “Monge-ampere regularization for learning arbitrary shapes from point clouds,”IEEE Transactions on Pattern Analysis and Machine Intelli- gence, vol. 47, no. 8, pp. 6809–6822, 2025

    2025

  5. [5]

    Vision meets robotics: The kitti dataset,

    A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,”The international journal of robotics research, vol. 32, no. 11, pp. 1231–1237, 2013

    2013

  6. [6]

    nuscenes: A multimodal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11 621–11 631

    2020

  7. [7]

    Acevfi: A comprehen- sive survey of advances in video frame interpolation,

    D. Kye, C. Roh, S. Ko, C. Eom, and J. Oh, “Acevfi: A comprehen- sive survey of advances in video frame interpolation,”arXiv preprint arXiv:2506.01061, 2025

    work page arXiv 2025

  8. [8]

    Video frame interpolation via adaptive convolution,

    S. Niklaus, L. Mai, and F. Liu, “Video frame interpolation via adaptive convolution,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 670–679

    2017

  9. [9]

    Video frame interpolation via adaptive separable convolution,

    ——, “Video frame interpolation via adaptive separable convolution,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 261–270

    2017

  10. [10]

    Adacof: Adaptive collaboration of flows for video frame interpolation,

    H. Lee, T. Kim, T.-y. Chung, D. Pak, Y . Ban, and S. Lee, “Adacof: Adaptive collaboration of flows for video frame interpolation,” inPro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 5316–5325

    2020

  11. [11]

    Super slomo: High quality estimation of multiple intermediate frames for video interpolation,

    H. Jiang, D. Sun, V . Jampani, M.-H. Yang, E. Learned-Miller, and J. Kautz, “Super slomo: High quality estimation of multiple intermediate frames for video interpolation,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 9000–9008. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 10

    2018

  12. [12]

    Real-time intermediate flow estimation for video frame interpolation,

    Z. Huang, T. Zhang, W. Heng, B. Shi, and S. Zhou, “Real-time intermediate flow estimation for video frame interpolation,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 624–642

    2022

  13. [13]

    Softmax splatting for video frame interpolation,

    S. Niklaus and F. Liu, “Softmax splatting for video frame interpolation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 5437–5446

    2020

  14. [14]

    Upsampling range data in dynamic environments,

    J. Dolson, J. Baek, C. Plagemann, and S. Thrun, “Upsampling range data in dynamic environments,” in2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010, pp. 1141– 1148

    2010

  15. [15]

    Texture aided depth frame interpo- lation,

    Y . Zhang, J. Zhang, and Q. Dai, “Texture aided depth frame interpo- lation,”Signal Processing: Image Communication, vol. 29, no. 8, pp. 864–874, 2014

    2014

  16. [16]

    Temporal upsampling of depth maps using a hybrid camera,

    M.-Z. Yuan, L. Gao, H. Fu, and S. Xia, “Temporal upsampling of depth maps using a hybrid camera,”IEEE transactions on visualization and computer graphics, vol. 25, no. 3, pp. 1591–1602, 2018

    2018

  17. [17]

    Pointinet: Point cloud frame interpolation network,

    F. Lu, G. Chen, S. Qu, Z. Li, Y . Liu, and A. Knoll, “Pointinet: Point cloud frame interpolation network,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 3, 2021, pp. 2251– 2259

    2021

  18. [18]

    Idea-net: Dynamic 3d point cloud interpolation via deep embedding alignment,

    Y . Zeng, Y . Qian, Q. Zhang, J. Hou, Y . Yuan, and Y . He, “Idea-net: Dynamic 3d point cloud interpolation via deep embedding alignment,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6338–6347

    2022

  19. [19]

    Neuralpci: Spatio-temporal neural field for 3d point cloud multi-frame non-linear interpolation,

    Z. Zheng, D. Wu, R. Lu, F. Lu, G. Chen, and C. Jiang, “Neuralpci: Spatio-temporal neural field for 3d point cloud multi-frame non-linear interpolation,” inProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, 2023, pp. 909–918

    2023

  20. [20]

    Fastpci: Motion-structure guided fast point cloud frame interpolation,

    T. Zhang, G. Qian, J. Xie, and J. Yang, “Fastpci: Motion-structure guided fast point cloud frame interpolation,” inEuropean Conference on Computer Vision. Springer, 2024, pp. 251–267

    2024

  21. [21]

    Event-based vision: A survey,

    G. Gallegoet al., “Event-based vision: A survey,”IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 1, pp. 154–180, 2020

    2020

  22. [22]

    Time lens++: Event-based frame interpolation with parametric non-linear flow and multi-scale fusion,

    S. Tulyakov, A. Bochicchio, D. Gehrig, S. Georgoulis, Y . Li, and D. Scaramuzza, “Time lens++: Event-based frame interpolation with parametric non-linear flow and multi-scale fusion,”IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022

    2022

  23. [23]

    Time lens: Event-based video frame interpolation,

    S. Tulyakov, D. Gehrig, S. Georgoulis, J. Erbach, M. Gehrig, Y . Li, and D. Scaramuzza, “Time lens: Event-based video frame interpolation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 16 155–16 164

    2021

  24. [24]

    Timelens-xl: Real-time event-based video frame interpolation with large motion,

    Y . Ma, S. Guo, Y . Chen, T. Xue, and J. Gu, “Timelens-xl: Real-time event-based video frame interpolation with large motion,” inEuropean Conference on Computer Vision. Springer, 2024, pp. 178–194

    2024

  25. [25]

    Timetracker: Event-based continuous point tracking for video frame interpolation with non-linear motion,

    H. Liu, J. Xu, Y . Chang, H. Zhou, H. Zhao, L. Wang, and L. Yan, “Timetracker: Event-based continuous point tracking for video frame interpolation with non-linear motion,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 17 649–17 659

    2025

  26. [26]

    Event-based video frame interpolation with cross-modal asymmetric bidirectional motion fields,

    T. Kim, Y . Chae, H.-K. Jang, and K.-J. Yoon, “Event-based video frame interpolation with cross-modal asymmetric bidirectional motion fields,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 18 032–18 042

    2023

  27. [27]

    Perception- oriented video frame interpolation via asymmetric blending,

    G. Wu, X. Tao, C. Li, W. Wang, X. Liu, and Q. Zheng, “Perception- oriented video frame interpolation via asymmetric blending,” inPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 2753–2762

    2024

  28. [28]

    Depth- aware video frame interpolation,

    W. Bao, W.-S. Lai, C. Ma, X. Zhang, Z. Gao, and M.-H. Yang, “Depth- aware video frame interpolation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 3703– 3712

    2019

  29. [29]

    End- to-end learning of representations for asynchronous event-based data,

    D. Gehrig, A. Loquercio, K. G. Derpanis, and D. Scaramuzza, “End- to-end learning of representations for asynchronous event-based data,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 5633–5643

    2019

  30. [30]

    Feature pyramid networks for object detection,

    T.-Y . Lin, P. Doll´ar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117–2125

    2017

  31. [31]

    Coordinate attention for efficient mobile network design,

    Q. Hou, D. Zhou, and J. Feng, “Coordinate attention for efficient mobile network design,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 13 713–13 722

    2021

  32. [32]

    Cross-modal fusion and progressive decoding network for rgb-d salient object detection,

    X. Hu, F. Sun, J. Sun, F. Wang, and H. Li, “Cross-modal fusion and progressive decoding network for rgb-d salient object detection,” International Journal of Computer Vision, vol. 132, no. 8, pp. 3067– 3085, 2024

    2024

  33. [33]

    Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers,

    J. Zhang, H. Liu, K. Yang, X. Hu, R. Liu, and R. Stiefelhagen, “Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers,” IEEE Transactions on intelligent transportation systems, vol. 24, no. 12, pp. 14 679–14 694, 2023

    2023

  34. [34]

    Raft: Recurrent all-pairs field transforms for optical flow,

    Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” inEuropean conference on computer vision. Springer, 2020, pp. 402–419

    2020

  35. [35]

    E-raft: Dense optical flow from event cameras,

    M. Gehrig, M. Millh ¨ausler, D. Gehrig, and D. Scaramuzza, “E-raft: Dense optical flow from event cameras,” in2021 International Con- ference on 3D Vision (3DV). IEEE, 2021, pp. 197–206

    2021

  36. [36]

    Hartley and A

    R. Hartley and A. Zisserman,Multiple view geometry in computer vision. Cambridge university press, 2003

    2003

  37. [37]

    The interpretation of a moving retinal image,

    H. C. Longuet-Higgins and K. Prazdny, “The interpretation of a moving retinal image,”Proceedings of the Royal Society of London. Series B. Biological Sciences, vol. 208, no. 1173, pp. 385–397, 1980

    1980

  38. [38]

    Passive navigation,

    A. R. Bruss and B. K. Horn, “Passive navigation,”Computer Vision, Graphics, and Image Processing, vol. 21, no. 1, pp. 3–20, 1983

    1983

  39. [39]

    Subspace methods for recovering rigid motion i: Algorithm and implementation,

    D. J. Heeger and A. D. Jepson, “Subspace methods for recovering rigid motion i: Algorithm and implementation,”International Journal of Computer Vision, vol. 7, no. 2, pp. 95–117, 1992

    1992

  40. [40]

    Flowseek: Optical flow made easier with depth foundation models and motion bases,

    M. Poggi and F. Tosi, “Flowseek: Optical flow made easier with depth foundation models and motion bases,”arXiv preprint arXiv:2509.05297, 2025

    work page arXiv 2025

  41. [41]

    Three- dimensional scene flow,

    S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three- dimensional scene flow,” inProceedings of the Seventh IEEE Interna- tional Conference on Computer Vision, vol. 2. IEEE, 1999, pp. 722– 729

    1999

  42. [42]

    Y . Ma, S. Soatto, J. Ko ˇseck´a, and S. Sastry,An invitation to 3-d vision: from images to geometric models. Springer, 2004, vol. 26

    2004

  43. [43]

    A quantitative analysis of current practices in optical flow estimation and the principles behind them,

    D. Sun, S. Roth, and M. J. Black, “A quantitative analysis of current practices in optical flow estimation and the principles behind them,” International Journal of Computer Vision, vol. 106, no. 2, pp. 115–137, 2014

    2014

  44. [44]

    A general and adaptive robust loss function,

    J. T. Barron, “A general and adaptive robust loss function,” inPro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4331–4339

    2019

  45. [45]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595

    2018

  46. [46]

    Adabins: Depth estimation using adaptive bins,

    S. F. Bhat, I. Alhashim, and P. Wonka, “Adabins: Depth estimation using adaptive bins,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 4009–4018

    2021

  47. [47]

    Unsupervised monocular depth estimation with left-right consistency,

    C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 270– 279

    2017

  48. [48]

    Digging into self-supervised monocular depth estimation,

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

    2019

  49. [49]

    Vector: A versatile event-centric benchmark for multi-sensor slam,

    L. Gao, Y . Liang, J. Yang, S. Wu, C. Wang, J. Chen, and L. Kneip, “Vector: A versatile event-centric benchmark for multi-sensor slam,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8217–8224, 2022

    2022

  50. [50]

    Loftr: Detector- free local feature matching with transformers,

    J. Sun, Z. Shen, Y . Wang, H. Bao, and X. Zhou, “Loftr: Detector- free local feature matching with transformers,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 8922–8931

    2021

  51. [51]

    Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,

    M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,”Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981

    1981

  52. [52]

    Intel® realsense™ sdk,

    “Intel® realsense™ sdk,” https://github.com/IntelRealSense/librealsense, accessed 10-11-2025

    2025

  53. [53]

    Dvsensedriver,

    “Dvsensedriver,” https://sdk.dvsense.com/zh/html/index.html, accessed 10-11-2025

    2025

  54. [54]

    Rgb-de: Event camera calibration for fast 6-dof object tracking,

    E. Dubeau, M. Garon, B. Debaque, R. de Charette, and J.-F. Lalonde, “Rgb-de: Event camera calibration for fast 6-dof object tracking,” in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2020, pp. 127–135

    2020

  55. [55]

    Ep n p: An accurate o (n) solution to the p n p problem,

    V . Lepetit, F. Moreno-Noguer, and P. Fua, “Ep n p: An accurate o (n) solution to the p n p problem,”International journal of computer vision, vol. 81, no. 2, pp. 155–166, 2009

    2009

  56. [56]

    Adam: A Method for Stochastic Optimization

    D. P. Kingma, “Adam: A method for stochastic optimization,”arXiv preprint arXiv:1412.6980, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

This paper was first reviewed by grok-4.3 on June 26, 2026.