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

TL;DR

This paper advocates for using flexible, generic camera models with 10,000 parameters over traditional parametric models, demonstrating improved accuracy in calibration and downstream 3D vision tasks.

Contribution

It introduces an automated, easy-to-use calibration pipeline for generic camera models, promoting their adoption over parametric models for better accuracy.

Findings

Generic models outperform parametric models in calibration accuracy.

Calibration error significantly biases stereo and pose estimation.

Open-source pipeline facilitates widespread adoption.

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.