The Double Sphere Camera Model
This addresses the need for accurate and efficient camera models in domains such as robotics and augmented reality, but it is incremental as it builds on existing models.
The paper tackles the problem of modeling large field-of-view cameras for vision-based applications like autonomous driving, proposing the Double Sphere camera model that fits well, is computationally inexpensive, and has a closed-form inverse, with evaluation showing competitive performance in reprojection error and computation time.
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.