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REVIEW 4 major objections 4 minor 2 cited by

USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Joint spike, pose, and 3D Gaussians beat three-stage pipelines.

desk verdict A credible one-stage integration of spike-to-image reconstruction, pose correction, and 3DGS that beats cascaded baselines on synthetic data, but the real-world pose-correction claim is under-supported. read the letter →

arxiv 2411.10504 v2 pith:5YRHUEYN submitted 2024-11-15 cs.CV cs.AI

Kang Chen , Jiyuan Zhang , Zecheng Hao , Yajing Zheng , Tiejun Huang , Zhaofei Yu This is my paper · ORCID
classification cs.CVcs.AI
keywords spikecamera3DGaussiansplattingjointoptimizationposecorrectionnovelviewsynthesisneuromorphicvisionself-supervisedimagereconstructionmotiondeblurring
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

Spike cameras record 40-kHz binary streams, and previous methods turn those streams into images first, then estimate poses, then build 3D scenes. That cascade lets image-reconstruction errors corrupt pose estimation and ultimately the 3D model. This paper claims that optimizing the spike-to-image network, the camera poses, and a 3D Gaussian Splatting scene in a single stage removes that error amplification. It reports higher PSNR/SSIM/LPIPS than cascaded baselines on synthetic scenes and stable quality under pose perturbation, while also correcting noisy initial poses.

What carries the argument

The load-bearing mechanism is the joint loss $L_{\text{joint}}$ that aligns the $M$ reconstructed frames $I_{\text{rec}}(t_m)$ from Recon-Net with the $M$ frames $I_{\text{gs}}(t_m)$ rendered by 3DGS at the same interpolated poses, minimized with a flip-and-minimum operation to handle reversed temporal order. Around it, the multi-reblur loss $L_{\text{rec}}$ (and its 3DGS counterpart $L_{\text{gs}}$) compare the average of $M$ sharp frames against the long-exposure image $E(T) = C \cdot N / T$ computed from spike counts, which prevents the identity-mapping failure mode and lets both branches be trained without pre-reconstructed clean images. The camera-trajectory model in Eq. (10), linear interpolation in the Lie algebra of SE(3) between an optimized $T_{\text{start}}$ and $T_{\text{end}}$, supplies the poses at each sampled time and is what pose correction actually adjusts.

What would settle it

Run USP-Gaussian on a sequence captured with a motion-capture-verified trajectory that includes a sharp acceleration or a pause within a spike interval, and compare optimized poses to ground truth; if translation or rotation errors stay near the initial values or diverge from the ground-truth trajectory, the linear-SE(3) motion model is the failing load-bearing assumption.

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Extended reading notes

Core claim

USP-Gaussian proposes a one-stage framework that takes spike streams from multiple viewpoints with imprecise initial poses and jointly optimizes three things: a convolution-based Recon-Net that maps a long-plus-short spike window to a sharp image at time t; the start and end SE(3) poses of each spike interval, with intermediate poses by linear Lie-algebra interpolation; and a set of 3D Gaussian primitives. The training signal is a sum of three losses: a multi-reblur loss for Recon-Net, a reblur loss for 3DGS, both comparing averaged sharp frames against the long-exposure image computed from spike counts, and a joint loss that aligns the Recon-Net image sequence with the 3DGS-rendered sequence, with a flip-and-minimum operation to fix temporal ordering. The central claim is that this joint optimization makes the two branches mutually corrective, so that multi-view consistency from 3DGS regularizes reconstruction and spike-reconstruction details sharpen the 3D scene, avoiding the cumulative errors of cascaded pipelines.

Load-bearing premise

The paper assumes each spike interval is captured during constant-velocity motion, because the pose at any time is obtained by linear interpolation in SE(3) between a start and an end pose; real hand-shaken trajectories with sudden accelerations break this assumption, and then the pose-correction gains in the real-world experiment may not generalize.

Editorial extensions

If this is right

  • End-to-end joint training should replace the image-reconstruction-then-3DGS recipe for spike cameras, since it removes the dominant source of cumulative error.
  • Pose optimization makes the pipeline usable when COLMAP-style initialization is poor, such as fast hand-shaken capture, without a separate bundle-adjustment stage.
  • The mutual supervision between Recon-Net and 3DGS yields better spike-to-image reconstruction than training Recon-Net alone, as the ablation ID-V versus ID-III shows.
  • Multi-reblur and long-short spike input are necessary to avoid a trivial identity mapping and to suppress salt-and-pepper noise, respectively.
  • Real-world gains transfer only if the linear SE(3) trajectory model fits the actual motion during each spike interval.

Reading between the lines

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

  • Replacing the linear SE(3) interpolation with a spline or per-timestamp learnable poses would test whether the constant-velocity model is the bottleneck for arbitrary hand motion.
  • The L2 alignment between 3DGS and Recon-Net outputs could be reweighted per scene or learned, since relative scale between the two branches may affect optimization stability.
  • The same unifying recipe could transfer to event cameras and RGB blur cameras, where reconstruction, pose, and radiance-field optimization are also cascaded.
  • The flip-and-minimum temporal-alignment trick is a cheap regularizer that other multi-frame joint-optimization pipelines could adopt to avoid trajectory reversals.
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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 / 4 minor

Summary. This paper proposes USP-Gaussian, an end-to-end framework that jointly optimizes a spike-to-image reconstruction network (Recon-Net), camera poses, and 3D Gaussian Splatting for 3D reconstruction from spike-camera data. The authors argue that the standard three-stage cascade (reconstructing images from spikes, estimating poses, then fitting a 3D representation) suffers from error accumulation, and they replace it with a unified loss combining a multi-reblur reconstruction loss, a 3DGS reblur loss supervised by the physically derived long-exposure signal E(T), and a joint alignment loss between the rendered and reconstructed image sequences. Experiments on a synthetic dataset report improved PSNR/SSIM/LPIPS over cascaded baselines and over SpikeGS, and a synthetic pose-perturbation experiment shows reduced translation and rotation errors. Real-world captures are compared only qualitatively.

Significance. If the experimental claims hold up, the paper makes a useful contribution: it shows that spike-to-image reconstruction and 3D scene representation can be trained jointly without ground-truth sharp images, and that the multi-view consistency of 3DGS can regularize a self-supervised deblurring network. The physically derived E(T) anchors the loss externally and is not circular, and the ablation study is informative. However, the significance is currently bounded by an asymmetric comparison with SpikeGS and by the lack of quantitative real-world evidence for the pose-correction claim; these issues directly affect the strength of the central conclusion.

major comments (4)
  1. [Sec. 4.3, Eq. (10)] The camera trajectory model represents the pose at time tm by linear interpolation between start and end poses in the SE(3) Lie algebra, which corresponds to constant-velocity geodesic motion. The real-world capture in Sec. 5.1 is described as a fast hand-shaken sequence; for such motion, non-constant velocity or non-geodesic rotations will make the rendered Igs(tm) in Eq. (11) correspond to incorrect camera poses. The flip-and-min operation in Eq. (12) only resolves the forward/reverse ambiguity and does not correct for a nonlinear velocity profile. Table 3 reports pose errors only for synthetic perturbed poses, and Sec. 6 lists only training time and memory as limitations. Please either justify the constant-velocity assumption for the target capture motions (e.g., with a velocity-profile analysis of the real spike data) or adopt a more expressive trajectory model, and provide quantitative pose or trajectory error evaluation on the real-world data.
  2. [Sec. 5.2, Tables 1 and 2] The SpikeGS baseline is run without its exposure loss, because that loss requires several poses per spike stream, while USP-Gaussian retains long-exposure supervision through Lgs and Lrec. Removing a central component of SpikeGS likely weakens the baseline, so the comparison is not apples-to-apples and the reported gaps (e.g., about 0.7 dB PSNR over SpikeGS in Table 1) may overstate the improvement. Please run SpikeGS with its exposure loss (using COLMAP poses or the optimized poses where needed), or quantify how much of the gap comes from the removed loss by also ablating the corresponding supervision from USP-Gaussian.
  3. [Tables 1-4 and Table 3] All quantitative results are reported as single-run metrics without standard deviations or multiple seeds. Because 3DGS optimization involves stochastic initialization and adaptive density control, and the pose optimization is coupled to both branches, it is unclear whether the improvements over baselines are statistically significant. At minimum, the main comparisons in Tables 1 and 2 and the pose-error results in Table 3 should include mean and standard deviation over several runs, or an error-bar analysis.
  4. [Sec. 5.1 and Sec. 5.2, real-world dataset] The real-world evaluation is only qualitative (Fig. 5); there are no quantitative reconstruction metrics, no pose or trajectory errors, and no comparison with methods on the real captures. Given that the paper's contribution is specifically motivated by inaccurate real-world poses, this is a major evidence gap. Please add quantitative real-world evaluation (e.g., reconstruction metrics against a reference, or relative pose accuracy if ground truth is unavailable), or explicitly restrict the pose-correction claim to the synthetic setting.
minor comments (4)
  1. [Abstract and Sec. 1] The abstract contains the typo 'casecaded' and the introduction uses 'casecading'; both should be 'cascaded' and 'cascading'.
  2. [Sec. 5.3, paragraph II] The text says 'The multi-reblur loss Lrec in Eq. (7) is designed to substitute the single-reblur loss Lrec in Eq. (8)', but Eq. (7) defines the single-reblur loss and Eq. (8) defines the multi-reblur loss; the equation references are reversed.
  3. [Sec. 4.1 and Fig. 3] The notation Lrec is used for both the single-reblur loss and the multi-reblur loss; please use distinct symbols (e.g., Lrec^single and Lrec^multi) to avoid the confusion visible in Table 4 and the Sec. 5.3 discussion.
  4. [Sec. 5.1] For reproducibility, please report the number of viewpoints, the spike-window length, and the temporal sampling details for both the synthetic and real-world datasets; the current text only states that 97 spike frames are extracted per view.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: both branches are supervised by the physically derived E(T), and the joint loss is a consistency regularizer rather than a self-defined target.

full rationale

The claimed derivation chain is anchored to Eq. (6), where the long-exposure image E(T) is computed from the spike stream via the physical firing-count relation C*N/T; this target is external to the network outputs. Both branches are supervised against this target: Recon-Net through the multi-reblur loss Lrec (Eqs. 7-8) and 3DGS through Lgs (Eq. 9). The joint loss Ljoint (Eq. 11) is a consistency term between the reconstructed and rendered frame sequences; it is not the sole supervisor of either branch and does not define the target E(T). Therefore, neither branch's output is equivalent by construction to its input. The camera trajectory model (Eq. 10) is adopted from BAD-NeRF/BAD-Gaussian [30,42], external prior work, not from the authors' own derived result, and the flip-and-min operation (Eq. 12) only resolves the forward/reverse ambiguity inherent in the deblurring loss. Self-citations to SpikeReveal [3] supply a deblurring-loss component and the caveat about identity mapping, but the paper independently ablates the multi-reblur loss (Fig. 7, Tab. 4), making those self-citations non-load-bearing. The linear SE(3) interpolation assumption is a correctness risk for real hand-shaken trajectories, not a circularity. No equation was found that reduces to its own input by construction, and no fitted parameter is relabeled as a prediction.

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

No new physical entities, forces, or conserved quantities are introduced. Recon-Net is a standard CNN, and the flip-and-minimum operation is an algorithmic choice, not an invented entity.

free parameters (3)
  • Temporal sample count M per interval = not reported
    Controls the number of reconstructed and rendered frames averaged in Lrec, Lgs, and Ljoint (Eqs. 7-11); chosen by hand and affects sharpness and pose sampling.
  • Sub-interval count N in multi-reblur loss = not reported
    Number of sub-windows in Eq. (8); a manual hyperparameter that balances regularization strength against computation.
  • Loss weight lambda in L1 + D-SSIM combination = not reported
    Used in all pixel-level losses, inherited from 3DGS [13]; the paper does not state its value or sensitivity.
assumptions (4)
  • domain assumption Spike generation follows the integrate-and-fire model of Eq. (1) with constant threshold C.
    Used to derive the long-exposure estimate E(T) = C*N/T in Eq. (6), which supervises both Recon-Net and 3DGS.
  • domain assumption The temporal average of a sharp image sequence equals the observed long-exposure image (motion-blur model).
    Basis of Lrec (Eqs. 7-8) and Lgs (Eq. 9); if sensor response is nonlinear or blur is not a simple average, the supervision signal is biased.
  • domain assumption Camera motion during each spike interval is a geodesic linear interpolation between start and end poses in SE(3).
    Eq. (10) defines all intermediate poses; real trajectories with acceleration or jitter violate this assumption.
  • domain assumption 3DGS multi-view consistency provides coherent supervision for the reconstruction network.
    The joint loss Ljoint (Eqs. 11-13) assumes the rendered and reconstructed sequences represent the same scene at the same timestamps; this holds only if pose and scene geometry are accurate enough.

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Cite this review

Pith. "Pith review of USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting." pith.science (2026). https://pith.science/paper/5YRHUEYN

@misc{pith2026241110504,
  author       = {Pith},
  title        = {Pith review of: USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5YRHUEYN}},
  note         = {Machine review of arXiv:2411.10504}
}
read the original abstract

Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task via Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS). Previous spike-based 3D reconstruction approaches often employ a casecased pipeline: starting with high-quality image reconstruction from spike streams based on established spike-to-image reconstruction algorithms, then progressing to camera pose estimation and 3D reconstruction. However, this cascaded approach suffers from substantial cumulative errors, where quality limitations of initial image reconstructions negatively impact pose estimation, ultimately degrading the fidelity of the 3D reconstruction. To address these issues, we propose a synergistic optimization framework, \textbf{USP-Gaussian}, that unifies spike-based image reconstruction, pose correction, and Gaussian splatting into an end-to-end framework. Leveraging the multi-view consistency afforded by 3DGS and the motion capture capability of the spike camera, our framework enables a joint iterative optimization that seamlessly integrates information between the spike-to-image network and 3DGS. Experiments on synthetic datasets with accurate poses demonstrate that our method surpasses previous approaches by effectively eliminating cascading errors. Moreover, we integrate pose optimization to achieve robust 3D reconstruction in real-world scenarios with inaccurate initial poses, outperforming alternative methods by effectively reducing noise and preserving fine texture details. Our code, data and trained models will be available at https://github.com/chenkang455/USP-Gaussian.

Figures

Figures reproduced from arXiv: 2411.10504 by the authors.

Figure 1
Figure 1. Left. Illustration of our USP-Gaussian framework, where the spike-based image Reconstruction Network (Recon-Net), camera poses, and 3DGS are collaboratively optimized signified by . Mid. Visual ablation showcasing the performance of Recon-Net and 3DGS with and without (w/ & w/o) the joint optimization strategy, with the ablation table depicted in Tab. 4 and the input formulated in Eq. (6). Right. Training curve comp… view at source ↗
Figure 2
Figure 2. (a) The spike camera captures a railway moving 350 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The working pipeline of our USP-Gaussian. For each camera viewpoint, we initially derive a pose sequence at equal time intervals based on the camera model described in Sec. 4.3. Leveraging the 3DGS rendering pipeline detailed in Sec. 4.2, we subsequently generate a corresponding sharp sequence for this viewpoint. Concurrently, Recon-Net is employed to reconstruct the same sequence from the captured spike stream as o… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: 3D reconstruction visual comparison of our USP-Gaussian compared with previous methods on the synthetic dataset, where the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: 3D reconstruction visual comparison of our USP-Gaussian compared with previous methods on the real-world dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Visual comparison of initial and optimized poses. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 8
Figure 8. Figure 8: Visual ablation on the effectiveness of long spike input. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 7
Figure 7. Figure 7: Visual ablation on the effectiveness of multi-reblur loss, [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Forward citations

Cited by 2 Pith papers

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

  1. E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras

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    E-4DGS is a deformable 3D Gaussian Splatting method that reconstructs dynamic scenes directly from multi-view event camera streams, outperforming event-to-image baseline approaches.

  2. GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A pipeline that turns sparse multi-view RGB images into a claimed 1,150-scene synthetic event dataset using 3D Gaussian Splatting rendering plus a stochastic event simulator.

Reference graph

Works this paper leans on

50 extracted references · 40 canonical work pages · cited by 2 Pith papers

  1. [1]

    Evdnerf: Reconstructing event data with dynamic neural ra- diance fields

    Anish Bhattacharya, Ratnesh Madaan, Fernando Cladera, Sai Vemprala, Rogerio Bonatti, Kostas Daniilidis, Ashish Kapoor, Vijay Kumar, Nikolai Matni, and Jayesh K Gupta. Evdnerf: Reconstructing event data with dynamic neural ra- diance fields. In WACV, pages 5846–5855, 2024. 3

    work page 2024

  2. [2]

    Motion deblur by learning residual from events

    Kang Chen and Lei Yu. Motion deblur by learning residual from events. IEEE TMM, 2024. 4

    work page 2024

  3. [3]

    Spikereveal: Unlocking temporal sequences from real blurry inputs with spike streams

    Kang Chen, Shiyan Chen, Jiyuan Zhang, Baoyue Zhang, Ya- jing Zheng, Tiejun Huang, and Zhaofei Yu. Spikereveal: Unlocking temporal sequences from real blurry inputs with spike streams. Advances in Neural Information Processing Systems, 37:62673–62696, 2025. 2, 5, 6, 8

    work page 2025

  4. [4]

    Spike-Zoo: A toolbox for spike-to-image recon- struction

    Kang Chen, Zhiyuan Ye, Tiejun Huang, and Zhaofei Yu. Spike-Zoo: A toolbox for spike-to-image recon- struction. https://github.com/chenkang455/ Spike-Zoo, 2025. 7

    work page 2025

  5. [5]

    Rethinking High-speed Image Reconstruction Framework with Spike Camera

    Kang Chen, Yajing Zheng, Tiejun Huang, and Zhaofei Yu. Rethinking high-speed image reconstruction framework with spike camera. arXiv preprint arXiv:2501.04477, 2025. 2

    work page Pith review arXiv 2025

  6. [6]

    Self-supervised mutual learning for dy- namic scene reconstruction of spiking camera

    Shiyan Chen, Chaoteng Duan, Zhaofei Yu, Ruiqin Xiong, and Tiejun Huang. Self-supervised mutual learning for dy- namic scene reconstruction of spiking camera. In IJCAI, pages 2859–2866, 2022. 2, 4, 8

    work page 2022

  7. [7]

    Self- supervised joint dynamic scene reconstruction and optical flow estimation for spiking camera

    Shiyan Chen, Zhaofei Yu, and Tiejun Huang. Self- supervised joint dynamic scene reconstruction and optical flow estimation for spiking camera. In AAAI, pages 350– 358, 2023. 2

    work page 2023

  8. [8]

    Enhancing motion deblurring in high-speed scenes with spike streams

    Shiyan Chen, Jiyuan Zhang, Yajing Zheng, Tiejun Huang, and Zhaofei Yu. Enhancing motion deblurring in high-speed scenes with spike streams. In NeurIPS, 2023. 2

    work page 2023

Show all 50 references
  1. [9]

    Spikenvs: Enhancing novel view synthesis from blurry im- ages via spike camera

    Gaole Dai, Zhenyu Wang, Qinwen Xu, Wen Cheng, Ming Lu, Boxing Shi, Shanghang Zhang, and Tiejun Huang. Spikenvs: Enhancing novel view synthesis from blurry im- ages via spike camera. arXiv preprint arXiv:2404.06710 ,

    arXiv

  2. [10]

    Event-based vision: A survey

    Guillermo Gallego, Tobi Delbr ¨uck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, J ¨org Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. IEEE TPAMI, 44(1):154–180,

  3. [11]

    Spikegs: Reconstruct 3d scene via fast-moving bio-inspired sensors

    Yijia Guo, Liwen Hu, Lei Ma, and Tiejun Huang. Spikegs: Reconstruct 3d scene via fast-moving bio-inspired sensors. arXiv preprint arXiv:2407.03771, 2024. 2, 3

    2024 arXiv

  4. [12]

    1000 × faster camera and machine vision with ordinary devices

    Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, et al. 1000 × faster camera and machine vision with ordinary devices. Engineering, 25:110–119, 2023. 1, 3

    2023

  5. [13]

    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 Trans. Graph., 42(4):139–1,

  6. [14]

    E-nerf: Neural radiance fields from a mov- ing event camera

    Simon Klenk, Lukas Koestler, Davide Scaramuzza, and Daniel Cremers. E-nerf: Neural radiance fields from a mov- ing event camera. IEEE Robotics and Automation Letters, 8 (3):1587–1594, 2023. 3

    2023

  7. [15]

    High-quality self-supervised deep image denoising

    Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. NeurIPS, 32, 2019. 2, 7

    2019

  8. [16]

    Benerf: Neural radiance fields from a single blurry image and event stream

    Wenpu Li, Pian Wan, Peng Wang, Jinghang Li, Yi Zhou, and Peidong Liu. Benerf: Neural radiance fields from a single blurry image and event stream. In European Conference on Computer Vision (ECCV), 2024. 3

    2024

  9. [17]

    Ef-3dgs: Event-aided free-trajectory 3d gaussian splatting

    Bohao Liao, Wei Zhai, Zengyu Wan, Tianzhu Zhang, Yang Cao, and Zheng-Jun Zha. Ef-3dgs: Event-aided free-trajectory 3d gaussian splatting. arXiv preprint arXiv:2410.15392, 2024. 3

    2024

  10. [18]

    Optical flow-guided 6dof object pose tracking with an event camera

    Zibin Liu, Banglei Guan, Yang Shang, Shunkun Liang, Zhenbao Yu, and Qifeng Yu. Optical flow-guided 6dof object pose tracking with an event camera. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 6501–6509, 2024. 3

    2024

  11. [19]

    Deblur-nerf: Neural radiance fields from blurry images

    Li Ma, Xiaoyu Li, Jing Liao, Qi Zhang, Xuan Wang, Jue Wang, and Pedro V Sander. Deblur-nerf: Neural radiance fields from blurry images. In CVPR, pages 12861–12870,

  12. [20]

    Deformable neural radiance fields using rgb and event cameras

    Qi Ma, Danda Pani Paudel, Ajad Chhatkuli, and Luc Van Gool. Deformable neural radiance fields using rgb and event cameras. In ICCV, pages 3590–3600, 2023. 3

    2023

  13. [21]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. In ECCV, 2020. 2

    2020

  14. [22]

    Understanding exposure: how to shoot great photographs with any camera

    Bryan Peterson. Understanding exposure: how to shoot great photographs with any camera. AmPhoto books, 2016. 1

    2016

  15. [23]

    Eventnerf: Neural radiance fields from a single colour event camera

    Viktor Rudnev, Mohamed Elgharib, Christian Theobalt, and Vladislav Golyanik. Eventnerf: Neural radiance fields from a single colour event camera. In CVPR, pages 4992–5002,

  16. [24]

    Structure- from-motion revisited

    Johannes L Schonberger and Jan-Michael Frahm. Structure- from-motion revisited. In CVPR, pages 4104–4113, 2016. 6

    2016

  17. [25]

    Xvfi: ex- treme video frame interpolation

    Hyeonjun Sim, Jihyong Oh, and Munchurl Kim. Xvfi: ex- treme video frame interpolation. In ICCV, pages 14489– 14498, 2021. 6

    2021

  18. [26]

    E-cir: Event-enhanced continuous intensity recovery

    Chen Song, Qixing Huang, and Chandrajit Bajaj. E-cir: Event-enhanced continuous intensity recovery. In CVPR, pages 7803–7812, 2022. 4

    2022

  19. [27]

    Simultaneous enhancement and noise suppression un- der complex illumination conditions

    Jing Tao, You Li, Banglei Guan, Yang Shang, and Qifeng Yu. Simultaneous enhancement and noise suppression un- der complex illumination conditions. IEEE Transactions on Instrumentation and Measurement, 2024. 3

    2024

  20. [28]

    Event enhanced high-quality image recovery

    Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In ECCV, pages 155–171. Springer, 2020. 4

    2020

  21. [29]

    Evggs: A collaborative learning framework for event-based generalizable gaussian splatting

    Jiaxu Wang, Junhao He, Ziyi Zhang, Mingyuan Sun, Jingkai Sun, and Renjing Xu. Evggs: A collaborative learning framework for event-based generalizable gaussian splatting. arXiv preprint arXiv:2405.14959, 2024. 3

    2024 arXiv

  22. [30]

    Bad-nerf: Bundle adjusted deblur neural radiance fields

    Peng Wang, Lingzhe Zhao, Ruijie Ma, and Peidong Liu. Bad-nerf: Bundle adjusted deblur neural radiance fields. In CVPR, pages 4170–4179, 2023. 5

    2023

  23. [31]

    Event3dgs: Event-based 3d gaussian splatting for high-speed robot egomotion

    Tianyi Xiong, Jiayi Wu, Botao He, Cornelia Fermuller, Yiannis Aloimonos, Heng Huang, and Christopher Metzler. Event3dgs: Event-based 3d gaussian splatting for high-speed robot egomotion. In 8th Annual Conference on Robot Learn- ing, 2024. 3

    2024

  24. [32]

    Motion deblur- ring with real events

    Fang Xu, Lei Yu, Bishan Wang, Wen Yang, Gui-Song Xia, Xu Jia, Zhendong Qiao, and Jianzhuang Liu. Motion deblur- ring with real events. In ICCV, pages 2583–2592, 2021. 5

    2021

  25. [33]

    Spikemm: Flexi- magnification of high-speed micro-motions

    Baoyue Zhang, Yajing Zheng, Shiyan Chen, Jiyuan Zhang, Kang Chen, Zhaofei Yu, and Tiejun Huang. Spikemm: Flexi- magnification of high-speed micro-motions. arXiv preprint arXiv:2406.00383, 2024. 2

    2024 arXiv

  26. [34]

    Spike transformer: Monocular depth estimation for spiking camera

    Jiyuan Zhang, Lulu Tang, Zhaofei Yu, Jiwen Lu, and Tiejun Huang. Spike transformer: Monocular depth estimation for spiking camera. In ECCV, pages 34–52. Springer, 2022. 2

    2022

  27. [35]

    Unveiling the potential of spike streams for foreground occlusion removal from densely continuous views

    Jiyuan Zhang, Shiyan Chen, Yajing Zheng, Zhaofei Yu, and Tiejun Huang. Unveiling the potential of spike streams for foreground occlusion removal from densely continuous views. arXiv preprint arXiv:2307.00821, 2023. 2

    2023 arXiv

  28. [36]

    Learning temporal-ordered representation for spike streams based on discrete wavelet transforms

    Jiyuan Zhang, Shanshan Jia, Zhaofei Yu, and Tiejun Huang. Learning temporal-ordered representation for spike streams based on discrete wavelet transforms. In AAAI, pages 137– 147, 2023. 2, 4, 8

    2023

  29. [37]

    Spikegs: 3d gaussian splat- ting from spike streams with high-speed camera motion

    Jiyuan Zhang, Kang Chen, Shiyan Chen, Yajing Zheng, Tiejun Huang, and Zhaofei Yu. Spikegs: 3d gaussian splat- ting from spike streams with high-speed camera motion. arXiv preprint arXiv:2407.10062, 2024. 2, 3, 6

    2024 arXiv

  30. [38]

    Spike-guided motion deblurring with un- known modal spatiotemporal alignment

    Jiyuan Zhang, Shiyan Chen, Yajing Zheng, Zhaofei Yu, and Tiejun Huang. Spike-guided motion deblurring with un- known modal spatiotemporal alignment. In CVPR, pages 25047–25057, 2024. 2

    2024

  31. [39]

    Unifying motion deblurring and frame interpolation with events

    Xiang Zhang and Lei Yu. Unifying motion deblurring and frame interpolation with events. In CVPR, pages 17765– 17774, 2022. 5

    2022

  32. [40]

    Generalizing event-based motion deblurring in real-world scenarios

    Xiang Zhang, Lei Yu, Wen Yang, Jianzhuang Liu, and Gui- Song Xia. Generalizing event-based motion deblurring in real-world scenarios. In ICCV, pages 10734–10744, 2023. 5

    2023

  33. [41]

    Spk2imgnet: Learning to reconstruct dy- namic scene from continuous spike stream

    Jing Zhao, Ruiqin Xiong, Hangfan Liu, Jian Zhang, and Tiejun Huang. Spk2imgnet: Learning to reconstruct dy- namic scene from continuous spike stream. In CVPR, pages 11996–12005, 2021. 2, 4, 6, 7, 8

    2021

  34. [42]

    Bad-gaussians: Bundle adjusted deblur gaussian splatting

    Lingzhe Zhao, Peng Wang, and Peidong Liu. Bad-gaussians: Bundle adjusted deblur gaussian splatting. arXiv preprint arXiv:2403.11831, 2024. 5

    2024 arXiv

  35. [43]

    Learning optical flow from continu- ous spike streams

    Rui Zhao, Ruiqin Xiong, Jing Zhao, Zhaofei Yu, Xiaopeng Fan, and Tiejun Huang. Learning optical flow from continu- ous spike streams. NeurIPS, 35:7905–7920, 2022. 2

    2022

  36. [44]

    Boosting spike cam- era image reconstruction from a perspective of dealing with spike fluctuations

    Rui Zhao, Ruiqin Xiong, Jing Zhao, Jian Zhang, Xiaopeng Fan, Zhaofei Yu, and Tiejun Huang. Boosting spike cam- era image reconstruction from a perspective of dealing with spike fluctuations. In CVPR, pages 24955–24965, 2024. 2

    2024

  37. [45]

    High-speed image re- construction through short-term plasticity for spiking cam- eras

    Yajing Zheng, Lingxiao Zheng, Zhaofei Yu, Boxin Shi, Yonghong Tian, and Tiejun Huang. High-speed image re- construction through short-term plasticity for spiking cam- eras. In CVPR, pages 6358–6367, 2021. 2

    2021

  38. [46]

    Spike-based motion estimation for object tracking through bio-inspired unsupervised learning

    Yajing Zheng, Zhaofei Yu, Song Wang, and Tiejun Huang. Spike-based motion estimation for object tracking through bio-inspired unsupervised learning. IEEE TIP, 32:335–349,

  39. [47]

    Event-based depth estimation with dense occlusion

    Kangrui Zhou, Taihang Lei, Banglei Guan, and Qifeng Yu. Event-based depth estimation with dense occlusion. Optics Letters, 49(12):3376–3379, 2024. 3

    2024

  40. [48]

    A retina-inspired sampling method for visual texture recon- struction

    Lin Zhu, Siwei Dong, Tiejun Huang, and Yonghong Tian. A retina-inspired sampling method for visual texture recon- struction. In ICME, pages 1432–1437. IEEE, 2019. 2, 4, 6, 7

    2019

  41. [49]

    Retina-like visual image reconstruction via spiking neural model

    Lin Zhu, Siwei Dong, Jianing Li, Tiejun Huang, and Yonghong Tian. Retina-like visual image reconstruction via spiking neural model. In CVPR, pages 1438–1446, 2020. 1, 2

    2020

  42. [50]

    Spikenerf: Learning neural radi- ance fields from continuous spike stream

    Lin Zhu, Kangmin Jia, Yifan Zhao, Yunshan Qi, Lizhi Wang, and Hua Huang. Spikenerf: Learning neural radi- ance fields from continuous spike stream. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6285–6295, 2024. 2, 3, 6

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.