# E2GS: Event Enhanced Gaussian Splatting

arXiv: https://arxiv.org/abs/2406.14978 · HTML: https://mlsanity.com/paper/2406.14978/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [E-NeRF: Neural Radiance Fields from a Moving Event Camera](https://mlsanity.com/paper/2208.11300/) (2022)
- [Deblurring 3D Gaussian Splatting](https://mlsanity.com/paper/2401.00834/) (2024)
- [Ev-NeRF: Event Based Neural Radiance Field](https://mlsanity.com/paper/2206.12455/) (2022)
- [Real-time high speed motion prediction using fast aperture-robust event-driven visual flow](https://mlsanity.com/paper/1811.11135/) (2018)

## Built on by (most cited first, 26 of 26)

- [IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera](https://mlsanity.com/paper/2410.08107/) (2024)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [EventVL: Understand Event Streams via Multimodal Large Language Model](https://mlsanity.com/paper/2501.13707/) (2025)
- [EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering](https://mlsanity.com/paper/2412.07293/) (2024)
- [Elite-EvGS: Learning Event-based 3D Gaussian Splatting by Distilling Event-to-Video Priors](https://mlsanity.com/paper/2409.13392/) (2024)
- [E3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2408.01840/) (2024)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark](https://mlsanity.com/paper/2507.11931/) (2025)
- [E-3DGS: Gaussian Splatting with Exposure and Motion Events](https://mlsanity.com/paper/2410.16995/) (2024)
- [Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2411.16180/) (2024)
- [Ev4DGS: Novel-view Rendering of Non-Rigid Objects from Monocular Event Streams](https://mlsanity.com/paper/2510.11717/) (2025)
- [A Survey on Event-driven 3D Reconstruction: Development under Different Categories](https://mlsanity.com/paper/2503.19753/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [E2EGS: Event-to-Edge Gaussian Splatting for Pose-Free 3D Reconstruction](https://mlsanity.com/paper/2603.14684/) (2026)
- [STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene](https://mlsanity.com/paper/2506.23157/) (2025)
- [DeblurSplat: SfM-free 3D Gaussian Splatting with Event Camera for Robust Deblurring](https://mlsanity.com/paper/2509.18898/) (2025)
- [Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones](https://mlsanity.com/paper/2602.21101/) (2026)
- [PhotonSplat: 3D Scene Reconstruction and Colorization from SPAD Sensors](https://mlsanity.com/paper/2506.21680/) (2025)
- [GeMS: Efficient Gaussian Splatting for Extreme Motion Blur](https://mlsanity.com/paper/2508.14682/) (2025)
- [Geometric-Photometric Event-based 3D Gaussian Ray Tracing](https://mlsanity.com/paper/2512.18640/) (2025)
- [Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events](https://mlsanity.com/paper/2601.15475/) (2026)
- [PRISM3D: Probabilistic Refinement and Robust Initialization for Physically Consistent Scene Modeling under Extreme Motion Blur](https://mlsanity.com/paper/2607.03855/) (2026)
- [JADE-GS: Joint Allocation of Deblurring Evidence for Event-Assisted 3D Gaussian Splatting](https://mlsanity.com/paper/2607.14990/) (2026)
- [ERF-GS: Reconstructing Fast Motion from Disjoint Event-RGB Viewpoints](https://mlsanity.com/paper/2608.08531/) (2026)
- [EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams](https://mlsanity.com/paper/2608.08585/) (2026)
- [EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset](https://mlsanity.com/paper/2609.17189/) (2026)

## Abstract (arXiv)

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.