# Relative Pose Estimation for Stereo Rolling Shutter Cameras

arXiv: https://arxiv.org/abs/2006.07807 · HTML: https://mlsanity.com/paper/2006.07807/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [SUNet: Symmetric Undistortion Network for Rolling Shutter Correction](https://mlsanity.com/paper/2108.04775/) (2021)
- [Context-Aware Video Reconstruction for Rolling Shutter Cameras](https://mlsanity.com/paper/2205.12912/) (2022)
- [Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video](https://mlsanity.com/paper/2210.03040/) (2022)
- [Single-Scanline Relative Pose Estimation for Rolling Shutter Cameras](https://mlsanity.com/paper/2506.22069/) (2025)
- [Minimal Solvers for Full DoF Motion Estimation from Asynchronous Tracks](https://mlsanity.com/paper/2508.17537/) (2025)
- [Single-View Rolling-Shutter SfM](https://mlsanity.com/paper/2603.11888/) (2026)

## Abstract (arXiv)

In this paper, we present a novel linear algorithm to estimate the 6 DoF relative pose from consecutive frames of stereo rolling shutter (RS) cameras. Our method is derived based on the assumption that stereo cameras undergo motion with constant velocity around the center of the baseline, which needs 9 pairs of correspondences on both left and right consecutive frames. The stereo RS images enable the recovery of depth maps from the semi-global matching (SGM) algorithm. With the estimated camera motion and depth map, we can correct the RS images to get the undistorted images without any scene structure assumption. Experiments on both simulated points and synthetic RS images demonstrate the effectiveness of our algorithm in relative pose estimation.