Efficient Pose Selection for Interactive Camera Calibration (1907.04096v1)

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.

• The paper introduces an interactive pose selection method that optimizes camera calibration accuracy by guiding users to choose poses that reduce parameter variance.
• Through evaluation, the proposed technique demonstrated superior performance, requiring 30% fewer calibration frames than existing methods like AprilCal for similar accuracy.
• This method improves calibration efficiency and user experience, making precise camera setup more accessible for applications in robotics, AR, and medical imaging.

Efficient Pose Selection for Interactive Camera Calibration

The paper "Efficient Pose Selection for Interactive Camera Calibration" by Pavel Rojtberg and Arjan Kuijper introduces an advanced pose selection methodology for camera calibration utilizing planar patterns, emphasizing precision in the intrinsic and extrinsic parameter determination. The novelty of the work lies in its approach to iteratively guiding users in selecting optimal camera poses during interactive calibration. This approach seeks to minimize the calibration frames needed while maintaining accuracy, thereby making the calibration process more accessible and efficient.

Key Contributions

1. Pose Selection Strategy: The authors present a technique explicitly designed to identify a compact set of reliable calibration poses. This method avoids singular and degenerate poses that could lead to inaccuracies in calibration, instead favoring poses that enhance calibration certainty through uncertainty propagation.
2. Self-Identifying Calibration Patterns: The paper details leveraging self-identifying calibration patterns, which enable real-time tracking of camera pose. These patterns streamline the calibration process by dynamically guiding the user to adopt optimal poses.
3. Performance Evaluation: Through extensive evaluation using both synthetic and real datasets, the authors demonstrate that their method outperforms existing calibration approaches like AprilCal, requiring 30% fewer calibration frames to achieve similar accuracy levels.

Methodology and Implementation

The proposed method is built upon the pinhole camera model and incorporates a novel integration strategy for intrinsic and distortion parameters. It utilizes backward covariance transport for assessing parameter uncertainties, allowing the method to iteratively focus on poses that reduce parameter variance. The calibration framework is structured to minimize variance in estimated parameters progressively until desired confidence levels are achieved.

The calibration pipeline follows a procedure comprising:

• Initial pose setup utilizing known configuration constraints.
• Iterative pose adjustments guided by uncertainty metrics.
• Real-time user assistance through visual overlay guidance that helps align the calibration pattern to the designated poses.

Strong Numerical Results and User Experience

The evaluation section demonstrates significant improvements in calibration error standards, with the proposed method reducing estimation errors compared to OpenCV and AprilCal solutions. Additionally, an informal user survey underscored the method's practicality, highlighting its time efficiency without compromising precision.

Practical and Theoretical Implications

Practical implications of this research are particularly relevant in fields requiring efficient and precise camera calibrations, such as robotics, augmented reality, and medical imaging. The method's reduced frame requirements underscore its suitability for scenarios demanding rapid recalibration, potentially improving workflow efficiency and reducing the barriers for non-expert users.

Theoretically, this work advances the understanding of pose selection influence on calibration effectiveness. It paves the way for further exploration in leveraging dynamic parameter tracking to enhance calibration accuracy and efficiency.

Speculation on Future Developments

Future developments could include extending the method's robustness in non-standard calibration scenarios like large distance calibration or microscopy, where physical constraints on pose adjustment are more pronounced. Introducing more comprehensive distortion models could further enhance the method's applicability across diverse imaging systems.

In conclusion, the paper presents a substantial contribution to interactive camera calibration, offering an efficient, user-friendly, and accurate methodology that holds significant promise for both practical and academic advancements in the field of computer vision.