# Efficient Pose Selection for Interactive Camera Calibration

## TL;DR

This paper introduces an interactive pose selection method for camera calibration that improves accuracy and efficiency by guiding users to optimal poses, reducing calibration frames by 30%.

## Contribution

A novel pose selection approach that explicitly avoids singular poses and leverages real-time tracking for efficient, robust camera calibration.

## Key findings

- Performs better than comparable solutions.
- Requires 30% fewer calibration frames.
- Validated on synthetic and real data.

## Abstract

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.