Relative Pose Estimation for Stereo Rolling Shutter Cameras
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
6 papers.
- 2021SUNet: Symmetric Undistortion Network for Rolling Shutter Correctioncited by 20
- 2022Context-Aware Video Reconstruction for Rolling Shutter Camerascited by 15
- 2022Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Videocited by 6
- 2025Single-Scanline Relative Pose Estimation for Rolling Shutter Camerascited by 3
- 2025Minimal Solvers for Full DoF Motion Estimation from Asynchronous Trackscited by 2
- 2026Single-View Rolling-Shutter SfMcited by 0
- 2026Single-View Rolling-Shutter SfM12 Mar 2026
- 2025Minimal Solvers for Full DoF Motion Estimation from Asynchronous Tracks24 Aug 2025
- 2025Single-Scanline Relative Pose Estimation for Rolling Shutter Cameras27 Jun 2025
- 2022Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video6 Oct 2022
- 2022Context-Aware Video Reconstruction for Rolling Shutter Cameras25 May 2022
- 2021SUNet: Symmetric Undistortion Network for Rolling Shutter Correction10 Aug 2021