# Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion

arXiv: https://arxiv.org/abs/2403.13327 · HTML: https://mlsanity.com/paper/2403.13327/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 23 of 23)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [ReconFusion: 3D Reconstruction with Diffusion Priors](https://mlsanity.com/paper/2312.02981/) (2023)
- [GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2311.11700/) (2023)
- [COLMAP-Free 3D Gaussian Splatting](https://mlsanity.com/paper/2312.07504/) (2023)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur](https://mlsanity.com/paper/1612.02583/) (2016)
- [BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields](https://mlsanity.com/paper/2211.12853/) (2022)
- [A Neural Approach to Blind Motion Deblurring](https://mlsanity.com/paper/1603.04771/) (2016)
- [Deblurring 3D Gaussian Splatting](https://mlsanity.com/paper/2401.00834/) (2024)
- [NeRFiller: Completing Scenes via Generative 3D Inpainting](https://mlsanity.com/paper/2312.04560/) (2023)
- [CamP: Camera Preconditioning for Neural Radiance Fields](https://mlsanity.com/paper/2308.10902/) (2023)
- [Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur](https://mlsanity.com/paper/2304.12652/) (2023)
- [Context-Aware Video Reconstruction for Rolling Shutter Cameras](https://mlsanity.com/paper/2205.12912/) (2022)
- [Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution](https://mlsanity.com/paper/2209.08503/) (2022)
- [USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields](https://mlsanity.com/paper/2310.02687/) (2023)
- [HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry](https://mlsanity.com/paper/2106.11857/) (2021)

## Built on by (most cited first, 29 of 29)

- [3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes](https://mlsanity.com/paper/2407.07090/) (2024)
- [3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting](https://mlsanity.com/paper/2412.12507/) (2024)
- [3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2407.17418/) (2024)
- [SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2411.16816/) (2024)
- [Deformable Beta Splatting](https://mlsanity.com/paper/2501.18630/) (2025)
- [Dynamic 3D Gaussian Fields for Urban Areas](https://mlsanity.com/paper/2406.03175/) (2024)
- [Radiant Foam: Real-Time Differentiable Ray Tracing](https://mlsanity.com/paper/2502.01157/) (2025)
- [Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion](https://mlsanity.com/paper/2406.02972/) (2024)
- [Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels](https://mlsanity.com/paper/2411.12440/) (2024)
- [BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting](https://mlsanity.com/paper/2503.15835/) (2025)
- [Sources of Uncertainty in 3D Scene Reconstruction](https://mlsanity.com/paper/2409.06407/) (2024)
- [COS3D: Collaborative Open-Vocabulary 3D Segmentation](https://mlsanity.com/paper/2510.20238/) (2025)
- [Digital Twin Generation from Visual Data: A Survey](https://mlsanity.com/paper/2504.13159/) (2025)
- [Plenodium: UnderWater 3D Scene Reconstruction with Plenoptic Medium Representation](https://mlsanity.com/paper/2505.21258/) (2025)
- [SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera Motion](https://mlsanity.com/paper/2407.10062/) (2024)
- [Deblur Gaussian Splatting SLAM](https://mlsanity.com/paper/2503.12572/) (2025)
- [Photoreal Scene Reconstruction from an Egocentric Device](https://mlsanity.com/paper/2506.04444/) (2025)
- [NimbusGS: Unified 3D Scene Reconstruction under Hybrid Weather](https://mlsanity.com/paper/2603.27228/) (2026)
- [DBMovi-GS: Dynamic View Synthesis from Blurry Monocular Video via Sparse-Controlled Gaussian Splatting](https://mlsanity.com/paper/2506.20998/) (2025)
- [Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones](https://mlsanity.com/paper/2602.21101/) (2026)
- [Spectral GS-SLAM: Observability-Aware, Degeneracy-Robust Tracking for Real-Time 3D Gaussian Splatting SLAM](https://mlsanity.com/paper/2606.21258/) (2026)
- [Dense Scene Reconstruction from Light-Field Images Affected by Rolling Shutter](https://mlsanity.com/paper/2412.03518/) (2024)
- [NeeCo: Image Synthesis of Novel Instrument States Based on Dynamic and Deformable 3D Gaussian Reconstruction](https://mlsanity.com/paper/2508.07897/) (2025)
- [Every Camera Effect, Every Time, All at Once: 4D Gaussian Ray Tracing for Physics-based Camera Effect Data Generation](https://mlsanity.com/paper/2509.10759/) (2025)
- [Single-View Rolling-Shutter SfM](https://mlsanity.com/paper/2603.11888/) (2026)
- [SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting](https://mlsanity.com/paper/2607.05598/) (2026)
- [Splat-based 3D Scene Reconstruction with Extreme Motion-blur](https://mlsanity.com/paper/2607.16926/) (2026)
- [Scanline-Aware Animatable Gaussian Avatars from Rolling-Shutter Videos](https://mlsanity.com/paper/2608.17314/) (2026)
- [ClearGS: Reliability-Aware Gaussian Splatting from Handheld Videos](https://mlsanity.com/paper/2609.31509/) (2026)

## Abstract (arXiv)

High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras. We present a method that adapts to camera motion and allows high-quality scene reconstruction with handheld video data suffering from motion blur and rolling shutter distortion. Our approach is based on detailed modelling of the physical image formation process and utilizes velocities estimated using visual-inertial odometry (VIO). Camera poses are considered non-static during the exposure time of a single image frame and camera poses are further optimized in the reconstruction process. We formulate a differentiable rendering pipeline that leverages screen space approximation to efficiently incorporate rolling-shutter and motion blur effects into the 3DGS framework. Our results with both synthetic and real data demonstrate superior performance in mitigating camera motion over existing methods, thereby advancing 3DGS in naturalistic settings.