# MBA-VO: Motion Blur Aware Visual Odometry

arXiv: https://arxiv.org/abs/2103.13684 · HTML: https://mlsanity.com/paper/2103.13684/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [DeMoN: Depth and Motion Network for Learning Monocular Stereo](https://mlsanity.com/paper/1612.02401/) (2016)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM](https://mlsanity.com/paper/1804.00874/) (2018)
- [DeepTAM: Deep Tracking and Mapping](https://mlsanity.com/paper/1808.01900/) (2018)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations](https://mlsanity.com/paper/1903.06482/) (2019)
- [Rolling-Shutter Modelling for Direct Visual-Inertial Odometry](https://mlsanity.com/paper/1911.01015/) (2019)
- [Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect](https://mlsanity.com/paper/1705.04300/) (2017)
- [Self-Supervised Linear Motion Deblurring](https://mlsanity.com/paper/2002.04070/) (2020)

## Built on by (most cited first, 16 of 16)

- [BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields](https://mlsanity.com/paper/2211.12853/) (2022)
- [BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting](https://mlsanity.com/paper/2403.11831/) (2024)
- [BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting](https://mlsanity.com/paper/2503.15835/) (2025)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [RS-NeRF: Neural Radiance Fields from Rolling Shutter Images](https://mlsanity.com/paper/2407.10267/) (2024)
- [GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation](https://mlsanity.com/paper/2505.15287/) (2025)
- [Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image](https://mlsanity.com/paper/2503.17358/) (2025)
- [Dual-Camera Smooth Zoom on Mobile Phones](https://mlsanity.com/paper/2404.04908/) (2024)
- [DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring](https://mlsanity.com/paper/2409.13496/) (2024)
- [On Motion Blur and Deblurring in Visual Place Recognition](https://mlsanity.com/paper/2412.07751/) (2024)
- [Panoramas from Photons](https://mlsanity.com/paper/2309.03811/) (2023)
- [Deblur Gaussian Splatting SLAM](https://mlsanity.com/paper/2503.12572/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [MBA-SLAM: Motion Blur Aware Gaussian Splatting SLAM](https://mlsanity.com/paper/2411.08279/) (2024)
- [Unblur-SLAM: Dense Neural SLAM for Blurry Inputs](https://mlsanity.com/paper/2603.26810/) (2026)
- [SSMB: Self-Supervised Local Feature Detection under Motion Blur](https://mlsanity.com/paper/2608.27181/) (2026)

## Abstract (arXiv)

Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera's local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable accuracy as that for images without motion blur.