Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A paraperspective factorization method for shape and motion recovery
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Sequential Updating of Projective and Affine Structure from Motion
International Journal of Computer Vision
Stratified Self-Calibration with the Modulus Constraint
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Factorization Methods for Projective Structure and Motion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A linear method for reconstruction from lines and points
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A column-space approach to projective reconstruction
Computer Vision and Image Understanding
Self-calibration from one circular motion sequence and two images
Pattern Recognition
Multi-stage 3D reconstruction under circular motion
Image and Vision Computing
Journal of Mathematical Imaging and Vision
A column-space approach to projective reconstruction
Computer Vision and Image Understanding
A subspace method for projective reconstruction from multiple images with missing data
Image and Vision Computing
Non-rigid metric reconstruction from perspective cameras
Image and Vision Computing
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In this paper, we consider the problem of projective reconstruction based on the factorization method. Unlike existing factorization based methods which minimize the SVD reprojection error, we propose to estimate the projective depths by minimizing the 2-D reprojection errors. An iterative algorithm is developed to minimize 2-D reprojection errors. This algorithm reconstructs the projective depths robustly and does not rely on any geometric knowledge, such as epipolar geometry. Simulation results using synthetic data are given to illustrate the performance of the algorithm.