# Rolling Shutter Camera Self-Calibration

arXiv: https://arxiv.org/abs/2608.01509 · HTML: https://mlsanity.com/paper/2608.01509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [Visual Geometry Grounded Deep Structure From Motion](https://mlsanity.com/paper/2312.04563/) (2023)
- [Self-Calibrating Neural Radiance Fields](https://mlsanity.com/paper/2108.13826/) (2021)
- [BA-Net: Dense Bundle Adjustment Network](https://mlsanity.com/paper/1806.04807/) (2018)
- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://mlsanity.com/paper/1904.04998/) (2019)
- [Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera](https://mlsanity.com/paper/1907.05820/) (2019)
- [FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent](https://mlsanity.com/paper/2404.15259/) (2024)
- [CTRL-C: Camera calibration TRansformer with Line-Classification](https://mlsanity.com/paper/2109.02259/) (2021)
- [FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification](https://mlsanity.com/paper/1804.04784/) (2018)
- [Rolling-Shutter-Aware Differential SfM and Image Rectification](https://mlsanity.com/paper/1903.03943/) (2019)
- [Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry](https://mlsanity.com/paper/1605.00475/) (2016)
- [Learning to Calibrate Straight Lines for Fisheye Image Rectification](https://mlsanity.com/paper/1904.09856/) (2019)
- [From two rolling shutters to one global shutter](https://mlsanity.com/paper/2006.01964/) (2020)
- [Self-Supervised Camera Self-Calibration from Video](https://mlsanity.com/paper/2112.03325/) (2021)
- [Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution](https://mlsanity.com/paper/2209.08503/) (2022)
- [Direct Sparse Odometry with Rolling Shutter](https://mlsanity.com/paper/1808.00558/) (2018)
- [Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM](https://mlsanity.com/paper/1907.13185/) (2019)
- [Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction](https://mlsanity.com/paper/2303.18125/) (2023)
- [Self-calibration-based Approach to Critical Motion Sequences of Rolling-shutter Structure from Motion](https://mlsanity.com/paper/1611.05476/) (2016)
- [Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System](https://mlsanity.com/paper/2108.07200/) (2021)

## Abstract (arXiv)

Rolling shutter (RS) cameras are widely used in consumer devices, but their row-wise exposure causes distortions under motion, making geometric 3D vision problems dependent on both camera intrinsics and readout time ratio. Existing RS calibration methods rely on calibration targets or specialised hardware, limiting their use in unconstrained settings. We present the first self-calibration method for RS cameras that directly estimates camera intrinsics and the readout time ratio from image sequences, without requiring calibration targets. The method is implemented as a self-calibrating bundle adjustment (BA), which critically depends on the RS imaging model. We combine two known complementary models. The first formulates RS imaging as continuous-time trajectory estimation under a row-wise pose representation. The second interprets RS images as temporally distorted global shutter (GS) images and requires to estimate correction fields. The combination is non-trivial and results in a unified dual-projection model, in which each 3D point is simultaneously constrained at both row-dependent and reference timestamps along a shared continuous trajectory, enforcing stronger geometric and temporal consistency. Extensive simulations analyse the applicability of several implementations under varying conditions, and real data experiments demonstrate the accuracy, robustness, and practical effectiveness of the proposed approach.