Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Geometric Methods for Computer Vision: An Application toStructure and Motion Estimation
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting
International Journal of Computer Vision
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Structure from Motion Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Bayesian Modelling of Camera Calibration and Reconstruction
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
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3D reconstruction through point correspondences is a process that is sensitive to match errors and also to possible ambiguity in the solution space of shape and camera estimates - the existence of either or the combination of both propagates into sub-optimal estimates of the structure. To counteract this, most methods in the field jointly or sequentially estimate both the camera parameters and the 3D structure using methods such as Bundle Adjustment. However, joint estimation methods such as Bundle Adjustment find sub-optimal solutions of structure if the structure is not uniquely defined in the joint space. Using probabilistic models for reconstruction and marginalizing across camera parameter uncertainty we show how to compute the optimal 3D reconstruction. We use only uniform priors to make comparisons between Bundle Adjustment and our work. However, the method, by its construction, is set up to use prior information about either the camera parameters or the 3D structure, if it is available. Results show that this method produces better reconstruction estimates than joint estimation methods such as Bundle Adjustment especially in the face of increasing noise in the feature correspondences.