Review and analysis of solutions of the three point perspective pose estimation problem
International Journal of Computer Vision
Polynomial-Time Geometric Matching for Object Recognition
International Journal of Computer Vision
Matching 3-D Models to 2-D Images
International Journal of Computer Vision
Solution of the simultaneous pose and correspondence problem using Gaussian error model
Computer Vision and Image Understanding
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Method for Geometric Feature Matching and Model Extraction
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Implementation techniques for geometric branch-and-bound matching methods
Computer Vision and Image Understanding
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Removing Outliers Using The L\infty Norm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Optimal Estimation of Perspective Camera Pose
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Optimal algorithms in multiview geometry
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Computers and Electrical Engineering
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
Robust Estimation for an Inverse Problem Arising in Multiview Geometry
Journal of Mathematical Imaging and Vision
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We study the problem of estimating the position and orientationof a calibrated camera from an image of a known scene. A commonproblem in camera pose estimation is the existence of falsecorrespondences between image features and modeled 3D points.Existing techniques such as RANSAC to handle outliers have noguarantee of optimality. In contrast, we work with a naturalextension of the L∞ norm to the outlier case. Usinga simple result from classical geometry, we derive necessaryconditions for L∞ optimality and show how to usethem in a branch and bound setting to find the optimum and todetect outliers. The algorithm has been evaluated on synthetic aswell as real data showing good empirical performance. In addition,for cases with no outliers, we demonstrate shorter execution timesthan existing optimal algorithms.