# E-3DGS: Gaussian Splatting with Exposure and Motion Events

arXiv: https://arxiv.org/abs/2410.16995 · HTML: https://mlsanity.com/paper/2410.16995/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [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)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [INeRF: Inverting Neural Radiance Fields for Pose Estimation](https://mlsanity.com/paper/2012.05877/) (2020)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields](https://mlsanity.com/paper/2210.13641/) (2022)
- [EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://mlsanity.com/paper/2312.00863/) (2023)
- [OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding](https://mlsanity.com/paper/2305.10764/) (2023)
- [Weakly Supervised 3D Open-vocabulary Segmentation](https://mlsanity.com/paper/2305.14093/) (2023)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [VR-NeRF: High-Fidelity Virtualized Walkable Spaces](https://mlsanity.com/paper/2311.02542/) (2023)
- [E-NeRF: Neural Radiance Fields from a Moving Event Camera](https://mlsanity.com/paper/2208.11300/) (2022)
- [Ev-NeRF: Event Based Neural Radiance Field](https://mlsanity.com/paper/2206.12455/) (2022)
- [EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images](https://mlsanity.com/paper/2405.20224/) (2024)
- [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)
- [Ev-GS: Event-based Gaussian splatting for Efficient and Accurate Radiance Field Rendering](https://mlsanity.com/paper/2407.11343/) (2024)
- [EaDeblur-GS: Event assisted 3D Deblur Reconstruction with Gaussian Splatting](https://mlsanity.com/paper/2407.13520/) (2024)
- [Temporal-Mapping Photography for Event Cameras](https://mlsanity.com/paper/2403.06443/) (2024)

## Built on by (most cited first, 3 of 3)

- [E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras](https://mlsanity.com/paper/2508.09912/) (2025)
- [GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation](https://mlsanity.com/paper/2505.15287/) (2025)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)

## Abstract (arXiv)

Achieving 3D reconstruction from images captured under optimal conditions has been extensively studied in the vision and imaging fields. However, in real-world scenarios, challenges such as motion blur and insufficient illumination often limit the performance of standard frame-based cameras in delivering high-quality images. To address these limitations, we incorporate a transmittance adjustment device at the hardware level, enabling event cameras to capture both motion and exposure events for diverse 3D reconstruction scenarios. Motion events (triggered by camera or object movement) are collected in fast-motion scenarios when the device is inactive, while exposure events (generated through controlled camera exposure) are captured during slower motion to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian Splatting (3DGS). Our framework supports three modes: High-Quality Reconstruction using exposure events, Fast Reconstruction relying on motion events, and Balanced Hybrid optimizing with initial exposure events followed by high-speed motion events. On the EventNeRF dataset, we demonstrate that exposure events significantly improve fine detail reconstruction compared to motion events and outperform frame-based cameras under challenging conditions such as low illumination and overexposure. Furthermore, we introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method achieves faster and higher-quality reconstruction than event-based NeRF and is more cost-effective than methods combining event and RGB data. E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset will be available at https://github.com/MasterHow/E-3DGS.