Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve
Paper in proceeding, 2020

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.


Thomas Schops

Swiss Federal Institute of Technology in Zürich (ETH)

Viktor Larsson

Swiss Federal Institute of Technology in Zürich (ETH)

Marc Pollefeys

Microsoft Mixed Reality & AI Lab - Zürich

Swiss Federal Institute of Technology in Zürich (ETH)

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

10636919 (ISSN)

2532-2541 9156397

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
Online, USA,

Subject Categories

Probability Theory and Statistics

Control Engineering

Computer Vision and Robotics (Autonomous Systems)



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