International Journal of Computer Vision manuscript No. (will be inserted by the editor) Radial Multi-focal Tensors Applications to Omnidirectional Camera Calibration (original) (raw)
Related papers
Radial Multi-focal Tensors Applications to Omnidirectional Camera Calibration
2011
The 1D radial camera maps all points on a plane, containing the principal axis, onto the radial line which is the intersection of that plane and the image plane. It is a sufficiently general model to express both central and non-central cameras, since the only assumption it makes is of known center of distortion. In this paper, we study the multi-focal tensors arising out of 1D radial cameras. There exist no two-view constraints (like the fundamental matrix) for 1D radial cameras. However, the 3-view and 4-view cases are interesting. For the 4-view case we have the radial quadrifocal tensor, which has 15 d.o.f and 2 internal constraints. For the 3-view case, we have the radial trifocal tensor, which has 7 d.o.f and no internal constraints. Under the assumption of a purely rotating central camera, this can be used to do a non-parametric estimation of the radial distortion of a 1D camera. Even in the case of a non-rotating camera it can be used to do parametric estimation, assuming a ...
Applications to Omnidirectional Camera Calibration
The 1D radial camera maps all points on a plane, containing the principal axis, onto the radial line which is the intersection of that plane and the image plane. It is a suciently general model to express both central and non-central cameras, since the only assumption it makes is of known center of distortion. In this paper, we study the multi-focal tensors arising out of 1D radial cameras. There exist no two-view constraints (like the fundamental matrix) for 1D radial cameras. However, the 3-view and 4-view cases are interesting. For the 4-view case we have the radial quadrifocal tensor, which has 15 d.o.f and 2 internal constraints. For the 3-view case, we have the radial trifocal tensor, which has 7 d.o.f and no internal constraints. Under the assumption of a purely rotating central camera, this can be used to do a non-parametric estimation of the radial distortion of a 1D camera. Even in the case of a non-rotating camera it can be used to do parametric estimation, assuming a pla...
2012
Abstract The 1D radial camera maps all points on a plane, containing the principal axis, onto the radial line which is the intersection of that plane and the image plane. It is a sufficiently general model to express both central and non-central cameras, since the only assumption it makes is of known center of distortion. In this paper, we study the multi-focal tensors arising out of 1D radial cameras. There exist no two-view constraints (like the fundamental matrix) for 1D radial cameras. However, the 3-view and 4-view cases are interesting.
Trifocal tensor for heterogeneous cameras
2005
Abstract We study the multi-view geometry arising out of a setup consisting of a pin-hole camera and two 1D radial cameras. A broad class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1D radial cameras under the assumption of known center of radial distortion. For cameras in general configuration, we introduce the mixed trifocal tensor which can be computed from 10 or more features seen in these three heterogeneous views.