BabelCalib: A Universal Approach to Calibrating Central Cameras (2109.09704v3)

Abstract: Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parameters of the used camera model. This might occur because a simpler projection model is assumed in an initial step, or a poor initial guess for the internal parameters is pre-defined. A lot of the difficulties of general camera calibration lie in the use of a forward projection model. We side-step these challenges by first proposing a solver to calibrate the parameters in terms of a back-projection model and then regress the parameters for a target forward model. These steps are incorporated in a robust estimation framework to cope with outlying detections. Extensive experiments demonstrate that our approach is very reliable and returns the most accurate calibration parameters as measured on the downstream task of absolute pose estimation on test sets. The code is released at https://github.com/ylochman/babelcalib.

• The paper presents a two-step calibration method that uses back-projection for robust parameter initialization followed by regression for model conversion.
• It achieves significant reductions in reprojection error and increased inlier ratios across diverse camera setups and lens distortions.
• Its approach effectively handles non-linearity and displaced projection centers, outperforming traditional methods like OpenCV, Kalibr, and OCamCalib.

Insightful Overview of "BabelCalib: A Universal Approach to Calibrating Central Cameras"

The paper "BabelCalib: A Universal Approach to Calibrating Central Cameras" presents a novel framework for calibrating cameras with a central projection model, addressing the shortcomings of existing calibration approaches in handling large field-of-view cameras such as fisheye lenses and catadioptric rigs. The authors introduce a comprehensive strategy that encompasses a robust initialization method, leveraging a back-projection model to achieve accurate camera geometry estimation, followed by a regression step to align with the target projection model's parameters.

Technical Contributions and Methodology

The primary challenge in calibrating cameras with extensive fields of view lies in the non-linearity of the problem and the difficulty in obtaining accurate initial parameters for the camera model. The proposed BabelCalib method accomplishes this by sidestepping these challenges through a two-step process:

1. Back-Projection Model Initialization: BabelCalib utilizes a back-projection model to recover camera parameters. This method involves solving the division model to obtain an initial approximation of the camera geometry, which includes the center of projection and pixel aspect ratio. The approach uses a robust radial fundamental matrix estimation, facilitating corner correction and enhancing the initialization accuracy by minimizing the reprojection errors.
2. Model-to-Model Regression: After obtaining the initial parameters, BabelCalib performs a regression to translate the back-projection parameters into the desired forward model parameters. This step ensures that the final calibration supports various radially-symmetric projection models such as Brown-Conrady (BC), Kannala-Brandt (KB), Unified Camera Model (UCM), and others outlined in the paper.

The framework is validated using extensive experimental data, showcasing strong results across a diverse range of camera models, from narrow to omni-directional fields of view. The robustness of the method is demonstrated through RANSAC-based outlier rejection, making the calibration resilient to bad initial detections.

Evaluation and Results

The empirical results highlight BabelCalib’s capability in reducing reprojection error and improving inlier ratios when compared to traditional methods, including those implemented in state-of-the-art frameworks like OpenCV, Kalibr, and OCamCalib. The authors tested the framework across multiple datasets encompassing different cameras and lens types, ensuring comprehensive evaluation.

Key findings from these experiments are:

• BabelCalib provides a significant reduction in RMS reprojection error across all tested camera models.
• The method maintains robustness even with displaced projection centers and non-square pixels, typical challenges in practical calibrations.
• BabelCalib's methodology in handling complex lens distortions and its resilience to minimal failures make it favorable over existing frameworks, establishing its reliability in diverse settings.

Theoretical and Practical Implications

The BabelCalib framework not only advances practical calibration processes by enabling more reliable and precise parameter estimation but also contributes theoretically to the modeling of camera systems with non-linear characteristics. The decoupling of calibration into back-projection followed by model regression can potentially simplify integrative camera model formulations in other computer vision applications.

The paper suggests avenues for future work, such as exploring additional back-projection variations and integrating minimal solvers for aspect ratio estimation. These potential enhancements could further bolster BabelCalib's utility and performance, paving the way for more versatile and adaptive calibration solutions in the computer vision and robotics domains.