# Efficient Pose Selection for Interactive Camera Calibration

arXiv: https://arxiv.org/abs/1907.04096 · HTML: https://mlsanity.com/paper/1907.04096/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

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

- [Camera Calibration with Pose Guidance](https://mlsanity.com/paper/2102.10202/) (2021)
- [Inferring bias and uncertainty in camera calibration](https://mlsanity.com/paper/2107.13484/) (2021)
- [Optimal Pose Guidance for Stereo Calibration in 3D Deformation Measurement](https://mlsanity.com/paper/2511.18317/) (2025)
- [Modeling dynamic target deformation in camera calibration](https://mlsanity.com/paper/2110.07322/) (2021)
- [YCB-Ev 1.1: Event-vision dataset for 6DoF object pose estimation](https://mlsanity.com/paper/2309.08482/) (2023)
- [Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline](https://mlsanity.com/paper/2508.07217/) (2025)

## Abstract (arXiv)

The choice of poses for camera calibration with planar patterns is only rarely considered - yet the calibration precision heavily depends on it. This work presents a pose selection method that finds a compact and robust set of calibration poses and is suitable for interactive calibration. Consequently, singular poses that would lead to an unreliable solution are avoided explicitly, while poses reducing the uncertainty of the calibration are favoured. For this, we use uncertainty propagation. Our method takes advantage of a self-identifying calibration pattern to track the camera pose in real-time. This allows to iteratively guide the user to the target poses, until the desired quality level is reached. Therefore, only a sparse set of key-frames is needed for calibration. The method is evaluated on separate training and testing sets, as well as on synthetic data. Our approach performs better than comparable solutions while requiring 30% less calibration frames.