# Simultaneous Stereo Video Deblurring and Scene Flow Estimation

arXiv: https://arxiv.org/abs/1704.03273 · HTML: https://mlsanity.com/paper/1704.03273/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 8 of 8)

- [ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring](https://mlsanity.com/paper/2103.04260/) (2021)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Deep Dynamic Scene Deblurring from Optical Flow](https://mlsanity.com/paper/2301.07329/) (2023)
- [Video Demoireing using Focused-Defocused Dual-Camera System](https://mlsanity.com/paper/2508.03449/) (2025)
- [View Adaptive Light Field Deblurring Networks with Depth Perception](https://mlsanity.com/paper/2303.06860/) (2023)
- [Video Deblurring with Deconvolution and Aggregation Networks](https://mlsanity.com/paper/2506.04054/) (2025)
- [A Benchmark for Heterogeneous Stereo Deblurring with Physically- and Epipolar-constrained Cross Attention](https://mlsanity.com/paper/2606.25962/) (2026)
- [CasDeblurGS: Cascaded 2D-to-3D Multi-View Consistency for 3D Gaussian Splatting from Two Blurry Images](https://mlsanity.com/paper/2608.10345/) (2026)

## Abstract (arXiv)

Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow information to deblur the image. Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow and deblur the image, where the motion cues from scene flow estimation and blur information could reinforce each other, and produce superior results than the conventional scene flow estimation or stereo deblurring methods. We evaluate our method extensively on two available datasets and achieve significant improvement in flow estimation and removing the blur effect over the state-of-the-art methods.