A theory of self-calibration of a moving camera
International Journal of Computer Vision
Canonical representations for the geometries of multiple projective views
Computer Vision and Image Understanding
International Journal of Computer Vision
Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Autocalibration and the absolute quadric
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
From Projective to Euclidean Space Under any Practical Situation, a Criticism of Self-Calibration
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Globally Convergent Autocalibration Using Interval Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum likelihood autocalibration
Image and Vision Computing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Stable two view reconstruction using the six-point algorithm
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Self-calibration of stationary non-rotating zooming cameras
Image and Vision Computing
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As it has been noted several times in literature, the difficult part of autocalibration efforts resides in the structural non-linearity of the search for the plane at infinity. In this paper we present a robust and versatile autocalibration method based on the enumeration of the inherently bounded space of the intrinsic parameters of two cameras in order to find the collineation of space that upgrades a given projective reconstruction to Euclidean. Each sample of the search space (which reduces to a finite subset of R2 under mild assumptions) defines a consistent plane at infinity. This in turn produces a tentative, approximate Euclidean upgrade of the whole reconstruction which is then scored according to the expected intrinsic parameters of a Euclidean camera. This approach has been compared with several other algorithms on both synthetic and concrete cases, obtaining favourable results.