# SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep

arXiv: https://arxiv.org/abs/2412.11579 · HTML: https://mlsanity.com/paper/2412.11579/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 16 Dec 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)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2112.00724/) (2021)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting](https://mlsanity.com/paper/2401.01339/) (2024)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)](https://mlsanity.com/paper/2210.00379/) (2022)
- [Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing](https://mlsanity.com/paper/2311.16043/) (2023)
- [Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction](https://mlsanity.com/paper/2403.11447/) (2024)
- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2407.17418/) (2024)
- [E-NeRF: Neural Radiance Fields from a Moving Event Camera](https://mlsanity.com/paper/2208.11300/) (2022)
- [GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh](https://mlsanity.com/paper/2404.07991/) (2024)
- [DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://mlsanity.com/paper/2202.02556/) (2022)
- [GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos](https://mlsanity.com/paper/2402.16607/) (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)
- [Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams](https://mlsanity.com/paper/2303.03856/) (2023)
- [SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data](https://mlsanity.com/paper/2209.15180/) (2022)
- [Neuromorphic Imaging with Joint Image Deblurring and Event Denoising](https://mlsanity.com/paper/2309.16106/) (2023)
- [Neuromorphic Imaging with Super-Resolution](https://mlsanity.com/paper/2407.05764/) (2024)

## Built on by (most cited first, 10 of 10)

- [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)
- [Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark](https://mlsanity.com/paper/2507.11931/) (2025)
- [Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views](https://mlsanity.com/paper/2512.10369/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [DeblurSplat: SfM-free 3D Gaussian Splatting with Event Camera for Robust Deblurring](https://mlsanity.com/paper/2509.18898/) (2025)
- [From Events to Enhancement: A Survey on Event-Based Imaging Technologies](https://mlsanity.com/paper/2505.05488/) (2025)
- [Computation-Aware Event-to-Frame Reconstruction via Selective Attention](https://mlsanity.com/paper/2606.06142/) (2026)
- [ERF-GS: Reconstructing Fast Motion from Disjoint Event-RGB Viewpoints](https://mlsanity.com/paper/2608.08531/) (2026)

## Abstract (arXiv)

Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view synthesis from continuously calibrated input views. However, conventional methods require high-frame-rate dense and high-quality sharp images, which are time-consuming and inefficient to capture, especially in dynamic environments. Event cameras, with their high temporal resolution and ability to capture asynchronous brightness changes, offer a promising alternative for more reliable scene reconstruction without motion blur. In this paper, we propose SweepEvGS, a novel hardware-integrated method that leverages event cameras for robust and accurate novel view synthesis across various imaging settings from a single sweep. SweepEvGS utilizes the initial static frame with dense event streams captured during a single camera sweep to effectively reconstruct detailed scene views. We also introduce different real-world hardware imaging systems for real-world data collection and evaluation for future research. We validate the robustness and efficiency of SweepEvGS through experiments in three different imaging settings: synthetic objects, real-world macro-level, and real-world micro-level view synthesis. Our results demonstrate that SweepEvGS surpasses existing methods in visual rendering quality, rendering speed, and computational efficiency, highlighting its potential for dynamic practical applications.