Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
An analytic solution for the perspective 4-point problem
Computer Vision, Graphics, and Image Processing
Determination of Camera Location from 2-D to 3-D Line and Point Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating 3-D location parameters using dual number quaternions
CVGIP: Image Understanding
Visual tracking of known three-dimensional objects
International Journal of Computer Vision
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Object pose from 2-D to 3-D point and line correspondences
International Journal of Computer Vision
Iterative pose estimation using coplanar feature points
Computer Vision and Image Understanding
Object Pose: The Link between Weak Perspective,Paraperspective, and Full Perspective
International Journal of Computer Vision
Model-Based Localisation and Recognition of Road Vehicles
International Journal of Computer Vision
Automatic Model Construction and Pose Estimation From Photographs Using Triangular Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fully projective formulation to improve the accuracy of Lowe's pose-estimation algorithm
Computer Vision and Image Understanding
Position Estimation from Outdoor Visual Landmarks for Teleoperation of Lunar Rovers
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Pseudo-linearizing collinearity constraint for accurate pose estimation from a single image
Pattern Recognition Letters
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We propose a new algorithm for model-based extrinsic cameracalibration that allows one to separate the recovery of the relativeorientation of the camera from the recovery of its relative position,given a set of at least three correspondences between model and imagepoints. The key idea is to replace each (real) modelpoint whose correspondence is known by two (virtual) model edges, andthen to use the fact that these edges have pairwise intersections in 3Dspace to derive a set of alignment constraints. We provide aproof that the resulting technique is essentially more powerful thanany of the traditional methods for decoupled orientation and positionrecovery based uniquely on line correspondences. We also present adetailed example of a real-life application that benefits from ourwork, namely autonomous navigation using distant visual landmarks. Weuse simulation to show that, for this specific application, ouralgorithm, when compared to similar techniques, is either significantlymore accurate at the same computational cost, or significantly fasterwith roughly the same average-case accuracy.