# Modeling dynamic target deformation in camera calibration

arXiv: https://arxiv.org/abs/2110.07322 · HTML: https://mlsanity.com/paper/2110.07322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty](https://mlsanity.com/paper/1811.03264/) (2018)
- [Efficient Pose Selection for Interactive Camera Calibration](https://mlsanity.com/paper/1907.04096/) (2019)
- [Inferring bias and uncertainty in camera calibration](https://mlsanity.com/paper/2107.13484/) (2021)

## Built on by (most cited first, 2 of 2)

- [L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras](https://mlsanity.com/paper/2207.01009/) (2022)
- [UniCal: Unified Neural Sensor Calibration](https://mlsanity.com/paper/2409.18953/) (2024)

## Abstract (arXiv)

Most approaches to camera calibration rely on calibration targets of well-known geometry. During data acquisition, calibration target and camera system are typically moved w.r.t. each other, to allow image coverage and perspective versatility. We show that moving the target can lead to small temporary deformations of the target, which can introduce significant errors into the calibration result. While static inaccuracies of calibration targets have been addressed in previous works, to our knowledge, none of the existing approaches can capture time-varying, dynamic deformations. To achieve high-accuracy calibrations despite moving the target, we propose a way to explicitly model dynamic target deformations in camera calibration. This is achieved by using a low-dimensional deformation model with only few parameters per image, which can be optimized jointly with target poses and intrinsics. We demonstrate the effectiveness of modeling dynamic deformations using different calibration targets and show its significance in a structure-from-motion application.