Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Review and analysis of solutions of the three point perspective pose estimation problem
International Journal of Computer Vision
Sequential Updating of Projective and Affine Structure from Motion
International Journal of Computer Vision
Computer Vision and Image Understanding
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
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Exact Two-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear N = 4-Point Pose Determination
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Unified Factorization Algorithm for Points, Line Segments and Planes with Uncertainty Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Convex Optimization
Epipolar Constraints for Vision-Aided Inertial Navigation
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Feature Uncertainty Arising from Covariant Image Noise
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
How Hard is 3-View Triangulation Really?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Quasiconvex Optimization for Robust Geometric Reconstruction
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple View Geometry and the L_"-norm
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image-Based Interactive Exploration of Real-World Environments
IEEE Computer Graphics and Applications
Global Optimization through Rotation Space Search
International Journal of Computer Vision
Omnidirectional Image Stabilization by Computing Camera Trajectory
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Removing outliers by minimizing the sum of infeasibilities
Image and Vision Computing
A branch-and-bound algorithm for globally optimal camera pose and focal length
Image and Vision Computing
Generalized Convexity in Multiple View Geometry
Journal of Mathematical Imaging and Vision
Efficient Suboptimal Solutions to the Optimal Triangulation
International Journal of Computer Vision
Omnidirectional Image Stabilization for Visual Object Recognition
International Journal of Computer Vision
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Global optimization for estimating a multiple-lobe analytical BRDF
Computer Vision and Image Understanding
Global Optimization for One-Dimensional Structure and Motion Problems
SIAM Journal on Imaging Sciences
Robust fitting for multiple view geometry
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Verifying Global Minima for L2 Minimization Problems in Multiple View Geometry
International Journal of Computer Vision
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Geometric reconstruction problems in computer vision are often solved by minimizing a cost function that combines the reprojection errors in the 2D images. In this paper, we show that, for various geometric reconstruction problems, their reprojection error functions share a common and quasiconvex formulation. Based on the quasiconvexity, we present a novel quasiconvex optimization framework in which the geometric reconstruction problems are formulated as a small number of small-scale convex programs that are ready to solve. Our final reconstruction algorithm is simple and has intuitive geometric interpretation. In contrast to existing local minimization approaches, our algorithm is deterministic and guarantees a predefined accuracy of the minimization result.The quasiconvexity also provides an intuitive method to handle directional uncertainties and outliers in measurements. When there are outliers in the measurements, our method provides a mechanism to locate the global minimum of a robust error function. For large scale problems and when computational resources are constrained, we provide an efficient approximation that gives a good upper bound (but not global minimum) on the reconstruction error. We demonstrate the effectiveness of our algorithm by experiments on both synthetic and real data.