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arxiv: 2406.14978 · v2 · pith:SQDZNBCLnew · submitted 2024-06-21 · 💻 cs.CV

E2GS: Event Enhanced Gaussian Splatting

Hiroyuki Deguchi , Mana Masuda , Takuya Nakabayashi , Hideo Saito This is my paper

Pith reviewed 2026-05-24 00:26 UTC · model grok-4.3

classification 💻 cs.CV
keywords event cameragaussian splattingnovel view synthesisdeblurring3d reconstructioncomputer vision
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The pith

Fusing event camera data into Gaussian Splatting corrects blur and enables high-quality novel view synthesis.

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

The paper introduces Event Enhanced Gaussian Splatting (E2GS) as a way to bring event camera information into the Gaussian Splatting framework for novel view synthesis. It demonstrates that this combination allows the system to handle blurry input images effectively by leveraging the motion information from events. This leads to better deblurred results and photorealistic renderings while keeping the fast training and rendering times associated with Gaussian Splatting. A reader would care because event cameras are good at capturing fast motion without blur, and combining them with efficient 3D reconstruction methods could make such techniques more usable in dynamic real-world settings.

Core claim

E2GS incorporates event data into Gaussian Splatting to effectively utilize both blurry images and event data, significantly improving image deblurring and producing high-quality novel view synthesis with faster training and rendering speed of 140 FPS, as shown on synthetic and real-world datasets.

What carries the argument

E2GS, the integration of event data directly into the Gaussian Splatting optimization pipeline for blur correction.

If this is right

  • Blurry images can be used for high-quality 3D reconstruction when paired with event data.
  • Novel view synthesis achieves better visual quality than using images alone.
  • Training and rendering speeds stay high at around 140 FPS.
  • The method applies to both synthetic and real datasets with visually appealing results.

Where Pith is reading between the lines

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

  • Extending the fusion to other event-based signals could further enhance reconstruction in low-light or high-speed scenarios.
  • Real-time robotics applications might adopt this for on-the-fly 3D mapping from moving cameras.
  • Investigating the exact mechanism of how events correct Gaussian parameters could lead to even simpler integration methods.

Load-bearing premise

Event data from the camera can be directly and effectively incorporated into the Gaussian Splatting process without needing extensive new models to bridge the different data types.

What would settle it

Experiments showing that E2GS produces equivalent or worse deblurring and view synthesis quality compared to standard Gaussian Splatting on the same blurry inputs would falsify the central claim.

read the original abstract

Event cameras, known for their high dynamic range, absence of motion blur, and low energy usage, have recently found a wide range of applications thanks to these attributes. In the past few years, the field of event-based 3D reconstruction saw remarkable progress, with the Neural Radiance Field (NeRF) based approach demonstrating photorealistic view synthesis results. However, the volume rendering paradigm of NeRF necessitates extensive training and rendering times. In this paper, we introduce Event Enhanced Gaussian Splatting (E2GS), a novel method that incorporates event data into Gaussian Splatting, which has recently made significant advances in the field of novel view synthesis. Our E2GS effectively utilizes both blurry images and event data, significantly improving image deblurring and producing high-quality novel view synthesis. Our comprehensive experiments on both synthetic and real-world datasets demonstrate our E2GS can generate visually appealing renderings while offering faster training and rendering speed (140 FPS). Our code is available at https://github.com/deguchihiroyuki/E2GS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Event Enhanced Gaussian Splatting (E2GS), which augments 3D Gaussian Splatting with event camera data to jointly perform image deblurring and novel view synthesis from blurry inputs. It claims effective fusion of event streams and blurry images yields visually appealing renderings at 140 FPS, with supporting experiments on synthetic and real-world datasets and public code release.

Significance. If the fusion approach proves robust, the work would offer a practical, high-speed alternative to slower NeRF-based event reconstruction pipelines by exploiting Gaussian Splatting's efficiency while leveraging event cameras' high dynamic range and motion-blur immunity. The code availability supports reproducibility.

major comments (2)
  1. [§3] §3 (Method): the event-image fusion step into the Gaussian optimization pipeline does not appear to include an explicit event rendering model, noise model, or calibration term to reconcile the integration mismatch between blurry-image intensity accumulation and asynchronous log-intensity event changes; without this, the auxiliary event loss risks producing inconsistent gradients, undermining the central claim of effective direct utilization.
  2. [§4] §4 (Experiments): quantitative tables comparing deblurring PSNR/SSIM and NVS metrics against baselines are needed to establish that reported gains are statistically meaningful and not dataset-specific; if the event contribution is marginal once domain mismatch is controlled, the general applicability of the pipeline is weakened.
minor comments (2)
  1. The abstract states 'significantly improving image deblurring' but the precise weighting between image and event losses should be stated explicitly in the method to allow reproduction.
  2. Figure captions could more clearly annotate the contribution of the event branch versus the baseline Gaussian Splatting run on blurry images alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive comments on our paper. We address each major comment below and outline the revisions we plan to make.

read point-by-point responses

  1. Referee: [§3] §3 (Method): the event-image fusion step into the Gaussian optimization pipeline does not appear to include an explicit event rendering model, noise model, or calibration term to reconcile the integration mismatch between blurry-image intensity accumulation and asynchronous log-intensity event changes; without this, the auxiliary event loss risks producing inconsistent gradients, undermining the central claim of effective direct utilization.

    Authors: We appreciate this observation. Our current formulation uses an auxiliary event loss to guide the optimization, but we acknowledge the lack of an explicit rendering model and calibration term in the description. To address this, we will revise §3 to include a detailed derivation of the event loss, incorporating an event rendering approximation based on the log-intensity changes and a simple calibration to handle domain differences. This will clarify the gradient flow and support the claim of effective fusion. We believe this addition will resolve the concern without requiring changes to the implementation. revision: yes

  2. Referee: [§4] §4 (Experiments): quantitative tables comparing deblurring PSNR/SSIM and NVS metrics against baselines are needed to establish that reported gains are statistically meaningful and not dataset-specific; if the event contribution is marginal once domain mismatch is controlled, the general applicability of the pipeline is weakened.

    Authors: We agree that quantitative metrics are crucial for validating the improvements. Although the manuscript emphasizes visual quality and speed, we will add quantitative tables in the revised version, reporting PSNR and SSIM for both deblurring and novel view synthesis tasks. These will include comparisons with baselines on the synthetic and real-world datasets used in our experiments. This will allow readers to assess the statistical significance and general applicability. revision: yes

Circularity Check

0 steps flagged

No circularity detected; method description contains no derivation chain or self-referential fitting

full rationale

The abstract and available text describe E2GS as a fusion of event data into an existing Gaussian Splatting pipeline for deblurring and view synthesis, with claims of effective utilization and faster performance. No equations, optimization procedures, parameter fitting steps, or self-citations are presented that would allow any claimed prediction or result to reduce by construction to its own inputs. The central claim rests on empirical demonstration rather than a closed mathematical derivation, making the work self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no equations, loss terms, or implementation details are available to enumerate free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5716 in / 984 out tokens · 21211 ms · 2026-05-24T00:26:16.407212+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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

  1. AsyncEvGS: Asynchronous Event-Assisted Gaussian Splatting for Handheld Motion-Blurred Scenes

    cs.CV 2026-05 unverdicted novelty 6.0

    AsyncEvGS reconstructs high-fidelity 3D scenes from motion-blurred images by first deblurring via event data then using VGGT-based pose estimation and structure-driven losses inside Gaussian Splatting.

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    INTRODUCTION In the task of 3D scene reconstruction and novel view syn- thesis, we witnessed tremendous progress over the past few years. Especially, after the NeRF (Neural Radiance Field) [1] marked a significant milestone, leading to the active develop- ment of various neural rendering techniques for 3D scene re- construction [2, 3]. Among these, 3D Gau...

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    RELA TED WORKS 2.1. 3D Scene Reconstruction 3D scene reconstruction is one of the fundamental functional- ity of computer vision. Recent advancements in 3D scene re- construction have gained more attention after the emergence of NeRF [1]. While several methods emerged to strengthen the NeRF-based approach [2, 3], there is one research direc- tion to accel...

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    METHOD The overview of our method is illustrated in Fig. 2. The input of our method is a set of blurry images and event stream of a static scene. In our E2GS framework, we first perform pre- processing using the correspondence between event data and blurred images. Then, we use two types of loss functions to train the Gaussian Splatting considering the bl...

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    EXPERIMENTS 4.1. Experimental Setup We evaluated our E2GS on two different tasks: Image deblur- ring and novel view synthesis. For the image deblurring task, we evaluate the rendering results from the perspective of the blurry image set. for the novel view synthesis task, we evalu- ate the rendering results from the perspective not used in the blurry imag...

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