# E2EGS: Event-to-Edge Gaussian Splatting for Pose-Free 3D Reconstruction

arXiv: https://arxiv.org/abs/2603.14684 · HTML: https://mlsanity.com/paper/2603.14684/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 37 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 27 of 27)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://mlsanity.com/paper/2312.02145/) (2023)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (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)
- [EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images](https://mlsanity.com/paper/2405.20224/) (2024)
- [Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion](https://mlsanity.com/paper/2309.08596/) (2023)
- [TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset](https://mlsanity.com/paper/2108.07329/) (2021)
- [CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://mlsanity.com/paper/2403.08119/) (2024)
- [Deep Event Visual Odometry](https://mlsanity.com/paper/2312.09800/) (2023)
- [E2GS: Event Enhanced Gaussian Splatting](https://mlsanity.com/paper/2406.14978/) (2024)
- [Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion](https://mlsanity.com/paper/2406.02972/) (2024)
- [EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting](https://mlsanity.com/paper/2405.14959/) (2024)
- [IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera](https://mlsanity.com/paper/2410.08107/) (2024)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [Ev-GS: Event-based Gaussian splatting for Efficient and Accurate Radiance Field Rendering](https://mlsanity.com/paper/2407.11343/) (2024)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [Deep Visual Odometry with Events and Frames](https://mlsanity.com/paper/2309.09947/) (2023)
- [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)
- [E-3DGS: Event-Based Novel View Rendering of Large-Scale Scenes Using 3D Gaussian Splatting](https://mlsanity.com/paper/2502.10827/) (2025)
- [GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting](https://mlsanity.com/paper/2409.19228/) (2024)
- [EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization](https://mlsanity.com/paper/2408.01370/) (2024)
- [EGS-SLAM: RGB-D Gaussian Splatting SLAM with Events](https://mlsanity.com/paper/2508.07003/) (2025)

## Built on by (most cited first, 2 of 2)

- [EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams](https://mlsanity.com/paper/2608.08585/) (2026)
- [EdMCGS: Event-Driven Markov Chain Gaussian Splatting for Extreme-Low-Frame-Rate Dynamic Scene Reconstruction](https://mlsanity.com/paper/2609.08332/) (2026)

## Abstract (arXiv)

The emergence of neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS) has advanced novel view synthesis (NVS). These methods, however, require high-quality RGB inputs and accurate corresponding poses, limiting robustness under real-world conditions such as fast camera motion or adverse lighting. Event cameras, which capture brightness changes at each pixel with high temporal resolution and wide dynamic range, enable precise sensing of dynamic scenes and offer a promising solution. However, existing event-based NVS methods either assume known poses or rely on depth estimation models that are bounded by their initial observations, failing to generalize as the camera traverses previously unseen regions. We present E2EGS, a pose-free framework operating solely on event streams. Our key insight is that edge information provides rich structural cues essential for accurate trajectory estimation and high-quality NVS. To extract edges from noisy event streams, we exploit the distinct spatio-temporal characteristics of edges and non-edge regions. The event camera's movement induces consistent events along edges, while non-edge regions produce sparse noise. We leverage this through a patch-based temporal coherence analysis that measures local variance to extract edges while robustly suppressing noise. The extracted edges guide structure-aware Gaussian initialization and enable edge-weighted losses throughout initialization, tracking, and bundle adjustment. Extensive experiments on both synthetic and real datasets demonstrate that E2EGS achieves superior reconstruction quality and trajectory accuracy, establishing a fully pose-free paradigm for event-based 3D reconstruction.