Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve (1912.02908v3)

Abstract: Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone.

• The paper demonstrates that generic camera models with 10,000 parameters significantly enhance calibration accuracy by reducing reprojection errors compared to conventional parametric models.
• It introduces an automated calibration pipeline using advanced star-based patterns and precise subpixel feature detection to optimize model performance.
• The study validates the approach with reduced bias and up to 0.6 cm depth error reduction, challenging traditional camera calibration methodologies.

Analysis of "Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve"

This essay provides an in-depth analysis of the paper titled "Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve" authored by Thomas Schöps, Viktor Larsson, Marc Pollefeys, and Torsten Sattler. The paper presents a comprehensive argument for the adoption of generic camera models over conventional parametric models in camera calibration, particularly emphasizing the accuracy and flexibility brought by the former.

Core Thesis

The paper posits that generic camera models, due to their greater flexibility and higher degrees of freedom, offer significantly improved calibration accuracy compared to traditional parametric models. This argument is supported by the introduction of an automated calibration pipeline designed for generic models, which can serve as a direct replacement for parametric methods. The pipeline is made available as open-source, fostering broader adoption.

Motivation and Background

Camera calibration is a foundational component of 3D Computer Vision systems, requiring accurate modeling of lens distortions. Parametric models, while widely used, are limited in their ability to capture the complexities of real-world optics due to their constrained parameter space. This inadequacy becomes apparent in high-distortion scenarios or unconventional camera setups, such as those common in autonomous vehicle systems. The authors argue that generic models should be the new standard, given their superior capability in minimizing calibration errors that propagate through subsequent computational steps in vision applications.

Methodology

The paper introduces key contributions including:

1. Calibration Pipeline Improvements: The authors enhance the calibration pattern and feature detection methods to achieve higher accuracy. Their focus is on developing sophisticated star-based calibration patterns that enable precise subpixel feature detection, surpassing traditional checkerboard or deltille patterns.
2. Generic Model Calibration: The paper discusses two types of generic models—central and non-central. The central model uses a unit-length direction for each grid point, while the non-central model stores both direction and a point on the observation line. These models are refined through bundle adjustment, optimizing the reprojection error using a robust Huber loss.
3. Software Release: An easy-to-use calibration pipeline and generic models have been released on GitHub, enabling wider adoption and experimentation by the research community.

Results and Validation

The paper presents a detailed evaluation of the proposed models across several cameras. When compared to parametric models such as OpenCV’s distortion model and the Thin-Prism Fisheye model, the generic models achieve lower median reprojection errors and demonstrate reduced bias in error distributions. Notably, the non-central generic model consistently outperforms others, indicating the limitation of assuming centrality even for standard cameras.

Practical and Theoretical Implications

The strong numerical results highlight not only the immediate practical benefits in applications like stereo depth estimation and camera pose estimation but also challenge long-standing assumptions in camera calibration. The demonstration of up to 0.6 cm depth error reduction in stereo estimation exemplifies the practical value of precise calibration. Furthermore, the release of the pipeline invites further exploration into applications beyond those tested, such as dynamic environments or varied imaging conditions.

Speculation on Future Developments

The move towards high-degree-of-freedom models suggests a potential shift in how camera-based systems are developed, potentially leading to more sophisticated machine vision applications that can better handle imperfect imaging scenarios and reduce accumulated computational bias.

Conclusion

This paper provides a compelling argument, backed by solid empirical data, for the adoption of generic camera models in computer vision. By highlighting the limitations of widely used parametric models and offering an accessible solution, the authors set the stage for a paradigm shift in camera calibration methodology. As AI and machine vision continue to evolve, such advancements underscore the importance of precision in foundational processes like camera calibration.