Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Pose Recovery for Generalized Visual Sensors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Estimation for Multiple Camera Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Calibration of a multi-camera rig from non-overlapping views
Proceedings of the 29th DAGM conference on Pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment
International Journal of Robotics Research
| Hi-index | 0.00 |
Estimating motions of a multi-camera system which may not have overlapping fields of view is generally complex and computationally expensive because of the non-zero offset between each camera's center. It is conceivable that if we can assume that multiple cameras share a single optical center, and thus can be modeled as a spherical imaging system, motion estimation and calibration of this system would become simpler and more efficient. In this paper, we analytically and empirically derive the conditions under which a multi-camera system can be modeled as a single spherical camera. Various analyses and experiments using simulated and real images show that spherical approximation is applicable to a surprisingly larger extent than currently expected. Moreover, we show that, when applicable, this approximation even results in improvements in accuracy and stability of estimated motion over the exact algorithm.