Why 10,000 Camera Model Parameters Outperform Twelve

Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve

This article discusses the advantages of using generic camera models with a large number of parameters over traditional parametric models in 3D Computer Vision systems. It highlights that while parametric models are commonly used due to their simplicity, they often fail to accurately represent complex real-world lens distortions. Generic models, on the other hand, offer greater flexibility and can achieve higher calibration accuracy.

The Limitations of Parametric Camera Models

Parametric camera models, such as the pinhole model with a few distortion coefficients, are widely adopted in computer vision. These models are computationally efficient and easier to implement. However, their limited degrees of freedom restrict their ability to accurately model the intricate distortions introduced by real camera lenses. This can lead to significant errors in applications that rely on precise geometric measurements.

The Power of Generic Camera Models

Generic camera models, often referred to as non-parametric or view-dependent models, can represent camera distortions with a much higher degree of freedom. These models can capture complex, non-linear distortions that parametric models cannot. While historically less utilized due to perceived complexity, advancements in calibration techniques and computational power have made them more practical.

A Practical Calibration Pipeline for Generic Models

The authors propose a fully automated and user-friendly calibration pipeline specifically designed for generic camera models. This pipeline aims to be a drop-in replacement for existing parametric calibration methods, emphasizing accuracy and ease of use. The process involves:

Automated Feature Detection: Identifying calibration patterns (e.g., checkerboards) in images.
Parameter Estimation: Utilizing robust algorithms to estimate the numerous parameters of the generic model.
Distortion Correction: Applying the learned model to correct lens distortions in images.

Impact on Downstream Tasks

The paper demonstrates the practical benefits of using generic models by evaluating their impact on key computer vision tasks:

Stereo Depth Estimation: Calibrations with generic models lead to more accurate depth maps by reducing the bias introduced by uncorrected distortions.
Camera Pose Estimation: Precise camera pose estimation, crucial for applications like SLAM (Simultaneous Localization and Mapping) and augmented reality, is significantly improved with accurate generic calibrations.

The study shows that the calibration error directly translates to a bias in the results of these tasks. Therefore, adopting generic models can lead to substantial improvements in the overall performance and reliability of 3D vision systems.

Open-Source Contribution

To facilitate the adoption of generic camera models, the authors have released their calibration pipeline on GitHub. This open-source contribution makes accurate and easy-to-use camera calibration accessible to the broader research and development community.

Conclusion

The article strongly advocates for the preference of generic camera models over parametric ones whenever possible. By leveraging their flexibility and the availability of robust calibration pipelines, researchers and developers can achieve higher accuracy and reduce systematic errors in a wide range of 3D computer vision applications. The availability of the open-source tool further lowers the barrier to entry for utilizing these advanced calibration techniques.

Research Areas:

Computer Vision

Research Labs:

Spatial AI Lab – Zurich

Follow us:

Share this page:

What's new:

Surface Pro
Surface Laptop
Surface Laptop Studio 2
Surface Laptop Go 3
Microsoft Copilot
AI in Windows
Explore Microsoft products
Windows 11 apps

Microsoft Store:

Account profile
Download Center
Microsoft Store support
Returns
Order tracking
Certified Refurbished
Microsoft Store Promise
Flexible Payments

Education:

Microsoft in education
Devices for education
Microsoft Teams for Education
Microsoft 365 Education
How to buy for your school
Educator training and development
Deals for students and parents
AI for education

Business:

Microsoft Cloud
Microsoft Security
Dynamics 365
Microsoft 365
Microsoft Power Platform
Microsoft Teams
Microsoft 365 Copilot
Small Business

Developer & IT:

Azure
Microsoft Developer
Microsoft Learn
Support for AI marketplace apps
Microsoft Tech Community
Azure Marketplace
AppSource
Visual Studio

Company:

Careers
About Microsoft
Company news
Privacy at Microsoft
Investors
Diversity and inclusion
Accessibility
Sustainability

Consumer Health Privacy:

Sitemap
Contact Microsoft
Privacy
Manage cookies
Terms of use
Trademarks
Safety & eco
Recycling
About our ads