Curve matching and stereo calibration
Image and Vision Computing
Surface shape from the deformation of apparent contours
International Journal of Computer Vision
3D interpretation of conics and orthogonality
CVGIP: Image Understanding
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Epipolar Geometry from Profiles under Circular Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Single Axis Geometry by Fitting Conics
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Automatic 3D Model Construction for Turn-Table Sequences
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Motion from the frontier of curved surfaces
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Circular Motion Geometry by Minimal 2 Points in 4 Images
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Camera Calibration with One-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper addresses the problem of self-calibration and motion recovery for turntable sequences. Previous works exploited silhouette correspondences induced by epipolar tangencies to estimate the image invariants under turntable motion and recover the epipolar geometry. These approaches, however, require the camera intrinsics in order to obtain an Euclidean motion, and a dense sequence is required to provide a precise initialization of the image invariants. This paper proposes a novel approach to estimate the camera intrinsics, the image invariants and the rotation angles from a sparse turntable sequence. The silhouettes and a single point correspondence are extracted from the image sequence. The point traces out a conic in the sequence, from which the fixed entities (i.e., the image of the rotation axis, the horizon, the vanishing point of the coordinates, the circular points and a scalar) can be recovered given a simple initialization of the camera intrinsic matrix. The rotation angles are then recovered by estimating the epipoles that minimize the transfer errors of the outer epipolar tangents to the silhouettes for each pair of images. The camera intrinsics can be further refined by the above optimization. Based on a given range of the initial focal length, a robust method is proposed to give the best estimate of the camera intrinsics, the image invariants, the full camera positions and orientations, and hence a Euclidean reconstruction. Experimental results demonstrate the simplicity of this approach and the accuracy in the estimated motion and reconstruction.