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REVIEW 4 major objections 4 minor 1 cited by

BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read BeSplat reconstructs a sharp, renderable 3D scene from a single motion-blurred image plus its event stream, jointly estimating the camera trajectory during exposure.

desk verdict An efficient 3DGS port of BeNeRF's event-plus-blur loss, with a real efficiency win, but the single-blurry-image claim is undercut by sharp-image COLMAP initialization. read the letter →

arxiv 2412.19370 v2 pith:Q2JWWXXU submitted 2024-12-26 cs.CV

Gopi Raju Matta , Reddypalli Trisha , Kaushik Mitra This is my paper · ORCID
classification cs.CV
keywords GaussianSplattingmotiondeblurringeventcameranovelviewsynthesistrajectoryestimationBéziercurveradiancefieldsingleimage
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 seeks to show that a sharp 3D scene representation can be recovered from inputs as limited as one motion-blurred photograph and the event stream recorded during that exposure. The method, BeSplat, represents the scene as 3D Gaussian splats rather than a neural radiance field, and it models the camera motion during the exposure as a differentiable Bézier curve in SE(3). By minimizing a photometric loss against the blurred image and an event loss against the accumulated events, the optimization recovers both the latent sharp scene and the camera trajectory. If the claim holds, it turns a single blurry frame plus events into enough information for real-time novel-view synthesis, deblurring, and motion estimation in one pass.

What carries the argument

The load-bearing mechanism is the differentiable combination of 3D Gaussian Splatting with a Bézier-curve camera trajectory in the special Euclidean group SE(3). The trajectory is defined by learnable control knots; at each sampled timestamp four control knots are combined through the De Boor–Cox basis matrix to yield an interpolated pose. Virtual sharp images rendered from the Gaussians at those poses are averaged to synthesize the blurred image, and the log-intensity difference between two trajectory endpoints synthesizes the accumulated event image. Both synthesized measurements are compared with the real measurements, giving gradients that flow back through the rasterizer to update both the Gaussian parameters and the control knots.

What would settle it

Run the identical optimization on the BeNeRF synthetic sequences with the Gaussian point cloud initialized from random depth in the camera frustum and the trajectory initialized to the identity pose; if the resulting PSNR falls below the learning-based baselines in Table 1, the claim that a single blurry image plus events alone recovers the sharp scene is not supported.

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Extended reading notes

Core claim

The authors claim to be the first to solve the single-blurry-image-plus-event-stream deblurring and novel-view-synthesis problem inside the 3D Gaussian Splatting framework. Their central discovery is that jointly optimizing Gaussian parameters and a 7th-order Bézier trajectory in SE(3), with 19 sampled poses during the exposure, lets the blurry-image formation model and a normalized accumulated-event loss pull out a sharp radiance field and the camera motion that produced the blur. On the BeNeRF synthetic dataset and the E2NeRF real dataset, the recovered splats render view-consistent sharp images along the trajectory, with training about four times faster and memory consumption about six times lower than the NeRF-based BeNeRF baseline.

Load-bearing premise

The pipeline starts from a COLMAP reconstruction computed on sharp images of the same scene, so the actual inputs are not limited to one blurry image and its events.

Editorial extensions

If this is right

  • The recovered scene is an explicit Gaussian point cloud, so sharp novel views can be rendered at real-time rates rather than through slow ray marching.
  • Training on the real dataset takes roughly one hour thirty minutes versus about six hours for BeNeRF, and GPU memory drops from 8.65 GB to 1.45 GB, making the method feasible on a single consumer GPU.
  • The same optimization outputs both a deblurred sequence of sharp images along the trajectory and an estimate of the camera motion that caused the blur, which could serve as a motion cue for downstream robotics or AR tasks.
  • The ablation shows a 7th-order Bézier trajectory reaches quality comparable to cubic B-splines while taking about one third of the training effort, so trajectory parameterization is a major efficiency lever.

Reading between the lines

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

  • Inference: Because the initialization relies on COLMAP run on sharp images, an immediate unproven extension is the fully constraint-satisfying setting where only a blurry frame and events are available; event-only or blur-only initialization is a natural follow-up test.
  • Inference: The event-loss formulation is modular and could be attached to other explicit scene representations, which would separate the contribution of trajectory recovery from the contribution of the Gaussian parameterization.
  • Inference: The recovered Bézier poses could be directly compared against inertial or visual-odometry ground truth in a real capture, offering a quantitative check on whether the trajectory recovered from blur-plus-events is genuinely the physical camera motion.
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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. The paper presents BeSplat, a 3D Gaussian Splatting method that aims to recover a sharp radiance field and the camera motion trajectory from a single motion-blurred image and its corresponding event stream. The method models the camera trajectory with a learnable SE(3) spline, renders virtual sharp views with 3DGS, averages them to synthesize the blurry image via a motion-blur formation model, and adds an event consistency loss. Experiments on synthetic BeNeRF and real E2NeRF datasets report competitive deblurring PSNR/SSIM/LPIPS compared with BeNeRF and learning-based deblurring baselines, with roughly 4x faster training and 6x lower GPU memory usage.

Significance. If fully supported, BeSplat would be a meaningful contribution as the first 3DGS-based method for single-image motion deblurring with event streams, and the efficiency gains over NeRF-based BeNeRF are substantial. The qualitative results on real data and the explicit event loss are strengths. However, the central claim is conditional on the use of COLMAP initialization from sharp images, which is outside the stated input protocol, and the trajectory recovery is never quantitatively validated. These issues must be resolved before the contribution can be fairly assessed.

major comments (4)
  1. [Section 5.1, Implementation Details] The implementation initializes the sparse point cloud and camera poses by running COLMAP on the corresponding sharp images. This supplies the 3D geometry and camera trajectory initialization that the method claims to recover, so the experimental protocol does not match the stated problem of a single blurry image plus event stream. No ablation is provided to show that the optimization converges to comparable quality without this initialization. This is a load-bearing discrepancy for the abstract's claim and must be addressed with either a revision of the problem statement or experiments that do not use sharp-image COLMAP.
  2. [Section 5.3, Tables 1-3] The abstract and introduction claim accurate recovery of the camera motion trajectory, yet the paper reports no trajectory error metrics (e.g., ATE, RPE, RMSE) anywhere. Tables 1-3 contain only image reconstruction metrics and efficiency numbers. Since the trajectory is a core output of the method, and a wrong trajectory could still fit the blurry input if the Gaussians overfit, the trajectory-recovery claim is currently unsupported.
  3. [Section 3.2, Eq. (7)] The text describes the trajectory as a 7th-order Bézier curve, but Eq. (7) gives the 4x4 basis matrix from the De Boor-Cox formula for a cubic B-spline, and Eq. (8) uses four consecutive control knots. This internal inconsistency means the implemented trajectory model is not the one described. The authors should clarify the actual spline order and update the text, or correct the equations; this matters for the trajectory ablation in Table 4.
  4. [Sections 5.2-5.3, Fig. 1] The paper claims novel view synthesis, but all quantitative evaluations are performed on sharp frames along the same trajectory that is fitted to the blurry image and events. No held-out camera poses are used for evaluation, so the reported PSNR/SSIM/LPIPS measure the model's fit to training views rather than its ability to synthesize novel views. The paper should either evaluate on held-out views or qualify the claim as deblurring along the estimated trajectory.
minor comments (4)
  1. [Table 1] Table 1 reports quantitative results for only two of the five synthetic sequences (Livingroom and Tanabata), while Fig. 3 shows qualitative results for all five; results for Outdoorpool, Pinkcastle, and Whiteroom are missing.
  2. [Tables 2 and 3] The memory and training time comparisons report deterministic values without standard deviations or multiple runs, and the efficiency comparison is only against BeNeRF, not against other 3DGS-based deblurring methods.
  3. [Section 5.1 and Fig. 5] The implementation says 19 sample points are used along the trajectory, while the ablation in Fig. 5 varies the virtual camera count n as 7, 11, and 15; the relationship between the sample count and the virtual camera count n in Eq. (9) is not defined.
  4. [Section 4.2] The event formation and loss in Eqs. (10)-(15) normalize both the captured and synthesized event images, which removes the absolute contrast scale; the paper does not discuss how this normalization interacts with the contrast threshold C in the event generation model.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: BeSplat's core is a direct inverse fit to the blurry image and event stream; the COLMAP sharp-image initialization is a validity concern, not a circular reduction.

full rationale

BeSplat is an inverse-rendering optimization. The claimed outputs (sharp Gaussian splats and the camera trajectory) are optimized directly against the two provided measurements through the combined loss in Eq. (13)-(15). There is no independent predicted quantity that secretly reduces to a fitted constant, no parameter fitted to a subset of the target data and then reported as a prediction, and no load-bearing self-citation chain. The event loss and Bezier-trajectory model are explicitly borrowed from BeNeRF [29] and Deblur-GS [8], but borrowing a loss or a trajectory parameterization is reuse, not circularity. The one notable concern is the Implementation Details statement: 'To obtain the initial sparse point cloud and camera pose for both real and synthetic datasets, structure-from-motion (COLMAP) is applied to the corresponding sharp images, following the approach in [8].' This supplies geometry and pose information that the abstract's stated input protocol ('a single motion-blurred image and its corresponding event stream') does not include, which could undermine the headline claim as an experimental-validity matter. However, it is not circular: the optimization still minimizes photometric and event discrepancies against the blurry image and events, and the final sharp renderings are not equal to the COLMAP initialization by construction. No step in the paper's derivation reduces to its own input in the sense required for a circularity finding, so the circularity score is 0.

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

The central claim rests on standard forward models for blur and events, on the assumption that exposure-time camera motion lives in a low-dimensional SE(3) spline, and on an ad hoc initialization that uses sharp ground-truth images. The sharp-image initialization is the most consequential item because it is outside the stated input setting.

free parameters (3)
  • Loss weights alpha and beta = alpha=0.1, beta=1.0 (real); alpha=1.0, beta=2.0 (synthetic)
    Chosen per dataset to balance RGB and event terms; affects all reported results.
  • Virtual camera count n = 19 sample points (main); ablation at 7, 11, 15
    Number of sharp frames averaged in the blur formation model; hand-set.
  • Trajectory spline order and knot spacing = Claimed order 7, four control knots per segment, uniform delta t
    Defines the capacity and smoothness of the SE(3) motion model; chosen by hand.
assumptions (4)
  • domain assumption Averaging n sharp renders approximates the motion-blurred image (Eq. 9).
    Ignores sensor noise, nonlinearities, and rolling shutter; standard forward model.
  • domain assumption Accumulated event image equals log intensity difference between start and end poses (Eq. 12).
    Neglects contrast threshold, noise, and event quantization; exact only for ideal logarithmic brightness changes.
  • domain assumption The camera trajectory during exposure is representable by a cubic B-spline or Bezier curve in SE(3).
    Assumes smooth 6-DoF motion; may fail for abrupt or high-frequency motion.
  • ad hoc to paper COLMAP on corresponding sharp images provides reliable initialization.
    This input is not part of the stated single-blurry-image setting; it supplies privileged scene geometry and poses.

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Cite this review

Pith. "Pith review of BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream." pith.science (2026). https://pith.science/paper/Q2JWWXXU

@misc{pith2026241219370,
  author       = {Pith},
  title        = {Pith review of: BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q2JWWXXU}},
  note         = {Machine review of arXiv:2412.19370}
}
read the original abstract

Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-quality reconstructions. In this work (BeSplat), we demonstrate the recovery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream. Our method jointly learns the scene representation via Gaussian Splatting and recovers the camera motion through Bezier SE(3) formulation effectively, minimizing discrepancies between synthesized and real-world measurements of both blurry image and corresponding event stream. We evaluate our approach on both synthetic and real datasets, showcasing its ability to render view-consistent, sharp images from the learned radiance field and the estimated camera trajectory. To the best of our knowledge, ours is the first work to address this highly challenging ill-posed problem in a Gaussian Splatting framework with the effective incorporation of temporal information captured using the event stream.

Figures

Figures reproduced from arXiv: 2412.19370 by the authors.

Figure 1
Figure 1. Given a single blurry image and its corresponding event stream, BeSplat synthesizes high-quality, novel sharp images along the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Our Method: The BeSplat framework reconstructs sharp radiance fields (Gaussian splats) while accurately estimating the camera motion trajectory, modeled with a Bezier curve, from a single blurry image and its corresponding event stream. The framework ´ jointly optimizes the Gaussian splats and camera motion trajectory by integrating RGB loss to align the synthesized and captured blurry images, as well as event loss … view at source ↗
Figure 3
Figure 3. Results on the Synthetic Dataset: Our method consistently achieves high performance, closely rivaling BeNeRF in image reconstruction quality. Insets highlight specific regions of the images, demonstrating that our method achieves comparable visual fidelity to BeNeRF while offering significant reductions in training and rendering times, as well as lower GPU memory usage. ble reconstruction quality to Bezier curves, c… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Results on the Real Dataset: Our method effectively handles varying levels of blur, consistently producing sharp, high-quality images. Insets illustrate that while our approach delivers results comparable to state-of-the-art methods on synthetic datasets, it performs n…
Figure 5
Figure 5. Figure 5: Ablation Studies on Virtual Camera Count: Our method demonstrates robust deblurring performance, maintaining high-quality results even when using a limited number of virtual cameras along the camera motion trajectory. age and its associated event stream. Through extens…

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.