The Double Sphere Camera Model

TL;DR

This paper introduces the Double Sphere camera model, a computationally efficient and accurate model for large field-of-view lenses, validated through calibration and visual odometry metrics.

Contribution

The paper proposes the Double Sphere camera model, offering a closed-form inverse and improved fit for large FOV lenses, enhancing accuracy and efficiency in vision applications.

Findings

Double Sphere model fits large FOV lenses well

It has lower reprojection error compared to existing models

Offers fast projection and unprojection computations

Abstract

Vision-based motion estimation and 3D reconstruction, which have numerous applications (e.g., autonomous driving, navigation systems for airborne devices and augmented reality) are receiving significant research attention. To increase the accuracy and robustness, several researchers have recently demonstrated the benefit of using large field-of-view cameras for such applications. In this paper, we provide an extensive review of existing models for large field-of-view cameras. For each model we provide projection and unprojection functions and the subspace of points that result in valid projection. Then, we propose the Double Sphere camera model that well fits with large field-of-view lenses, is computationally inexpensive and has a closed-form inverse. We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i.e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians. We also provide qualitative results and discuss the performance of all models.