# ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images

arXiv: https://arxiv.org/abs/2309.08957 · HTML: https://mlsanity.com/paper/2309.08957/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 64 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 30 of 47)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [Mixture of Volumetric Primitives for Efficient Neural Rendering](https://mlsanity.com/paper/2103.01954/) (2021)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)

## Built on by (most cited first, 30 of 38)

- [BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling](https://mlsanity.com/paper/2403.04926/) (2024)
- [Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video](https://mlsanity.com/paper/2412.06424/) (2024)
- [BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting](https://mlsanity.com/paper/2503.15835/) (2025)
- [RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction](https://mlsanity.com/paper/2512.23437/) (2025)
- [DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video](https://mlsanity.com/paper/2403.10103/) (2024)
- [LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes](https://mlsanity.com/paper/2411.06757/) (2024)
- [BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream](https://mlsanity.com/paper/2407.02174/) (2024)
- [DeblurGS: Gaussian Splatting for Camera Motion Blur](https://mlsanity.com/paper/2404.11358/) (2024)
- [Neural Radiance Fields for the Real World: A Survey](https://mlsanity.com/paper/2501.13104/) (2025)
- [MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video](https://mlsanity.com/paper/2504.15122/) (2025)
- [Deblur e-NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions](https://mlsanity.com/paper/2409.17988/) (2024)
- [MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video](https://mlsanity.com/paper/2312.13528/) (2023)
- [Exploiting Deblurring Networks for Radiance Fields](https://mlsanity.com/paper/2502.14454/) (2025)
- [CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images](https://mlsanity.com/paper/2503.05332/) (2025)
- [SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields](https://mlsanity.com/paper/2403.07547/) (2024)
- [Spike-NeRF: Neural Radiance Field Based On Spike Camera](https://mlsanity.com/paper/2403.16410/) (2024)
- [GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring](https://mlsanity.com/paper/2410.23658/) (2024)
- [RaFE: Generative Radiance Fields Restoration](https://mlsanity.com/paper/2404.03654/) (2024)
- [Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View](https://mlsanity.com/paper/2407.06613/) (2024)
- [LSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo](https://mlsanity.com/paper/2409.06104/) (2024)
- [Radiance Fields from Photons](https://mlsanity.com/paper/2407.09386/) (2024)
- [Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views](https://mlsanity.com/paper/2512.10369/) (2025)
- [Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos](https://mlsanity.com/paper/2501.13335/) (2025)
- [Drantal-NeRF: Diffusion-Based Restoration for Anti-aliasing Neural Radiance Field](https://mlsanity.com/paper/2407.07461/) (2024)
- [ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events](https://mlsanity.com/paper/2409.14103/) (2024)
- [MBA-SLAM: Motion Blur Aware Gaussian Splatting SLAM](https://mlsanity.com/paper/2411.08279/) (2024)
- [Casual3DHDR: Deblurring High Dynamic Range 3D Gaussian Splatting from Casually Captured Videos](https://mlsanity.com/paper/2504.17728/) (2025)
- [DBMovi-GS: Dynamic View Synthesis from Blurry Monocular Video via Sparse-Controlled Gaussian Splatting](https://mlsanity.com/paper/2506.20998/) (2025)
- [Dynamic Gaussian Splatting from Defocused and Motion-blurred Monocular Videos](https://mlsanity.com/paper/2510.10691/) (2025)
- [BSGS: Bi-stage 3D Gaussian Splatting for Camera Motion Deblurring](https://mlsanity.com/paper/2510.12493/) (2025)

## Abstract (arXiv)

We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. From extremely blurred images, we optimize the sharp radiance fields by jointly estimating the camera trajectories that generate the blurry images. In training, multiple rays along the camera trajectory are accumulated to reconstruct single blurry color, which is equivalent to the physical motion blur operation. We minimize the photo-consistency loss on blurred image space and obtain the sharp radiance fields with camera trajectories that explain the blur of all images. The joint optimization on the blurred image space demands painfully increasing computation and resources proportional to the blur size. Our method solves this problem by replacing the MLP-based framework to low-dimensional 6-DOF camera poses and voxel-based radiance fields. Compared with the existing works, our approach restores much sharper 3D scenes from challenging motion blurred views with the order of 10 times less training time and GPU memory consumption.