Algebraic and Geometric Tools to Compute Projective and Permutation Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stratified Self-Calibration with the Modulus Constraint
IEEE Transactions on Pattern Analysis and Machine Intelligence
General Object Reconstruction Based on Simplex Meshes
International Journal of Computer Vision
Stereo Calibration from Rigid Motions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Multiple-Hypothesis Disparity Maps and Image Velocity for 3-D Motion Estimation
International Journal of Computer Vision
Stratified Self Calibration from Screw-Transform Manifolds
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Binocular Self-Alignment and Calibration from Planar Scenes
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Stereo Autocalibration from One Plane
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Projective Translations and Affine Stereo Calibration
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The 3D Line Motion Matrix and Alignment of Line Reconstructions
International Journal of Computer Vision
Affine Reconstruction from Translational Motion under Various Autocalibration Constraints
Journal of Mathematical Imaging and Vision
Self-calibration of a stereo rig using monocular epipolar geometries
Pattern Recognition
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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To make a Euclidean reconstruction of the world seen through a stereo rig, we can either use a calibration grid, and the results will rely on the precision Of the grid and the extracted points of interest, or use self-calibration. Past work on self-calibration is focussed on the use of only one camera, and gives sometimes very unstable results. In this paper, we use a stereo rig which is supposed to be weakly calibrated using a method such as the one described in Deriche et al. (1994). Then, by matching two sets of points of the same scene reconstructed from different points of view, we try to find both the homography that maps the projective reconstruction to the Euclidean space and the displacement from the first set of points to the second set of points. We present results of the Euclidean reconstruction of a whole object from uncalibrated cameras using the method proposed here.