Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correspondenceless Stereo and Motion: Planar Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
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
Reliable Determination of Object Pose from Line Features by Hypothesis Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D to 2-D Pose Determination with Regions
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Efficient Linear Solution of Exterior Orientation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
Single view based pose estimation from circle or parallel lines
Pattern Recognition Letters
Robust Optimal Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pose estimation for augmented reality applications using genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Classification of 3-D objects and faces employing view-based clusters
Computers and Electrical Engineering
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Problem of relative pose estimation between a camera and rigid object, given an object model with feature points and image(s) with respective image points (hence known correspondence) has been extensively studied in the literature. We propose a ''correspondenceless'' method called gravitational pose estimation (GPE), which is inspired by classical mechanics. GPE can handle occlusion and uses only one image (i.e., perspective projection of the object). GPE creates a simulated gravitational field from the image and lets the object model move and rotate in that force field, starting from an initial pose. Experiments were carried out with both real and synthetic images. Results show that GPE is robust, consistent, and fast (runs in less than a minute). On the average (including up to 30% occlusion cases) it finds the orientation within 6^o and the position within 17% of the object's diameter. SoftPOSIT was so far the best correspondenceless method in the literature that works with a single image and point-based object model like GPE. However, SoftPOSIT's convergence to a result is sensitive to the choice of initial pose. Even ''random start SoftPOSIT,'' which performs multiple runs of SoftPOSIT with different initial poses, can often fail. However, SoftPOSIT finds the pose with great precision when it is able to converge. We have also integrated GPE and SoftPOSIT into a single method called GPEsoftPOSIT, which finds the orientation within 3^o and the position within 10% of the object's diameter even under occlusion. In GPEsoftPOSIT, GPE finds a pose that is very close to the true pose, and then SoftPOSIT is used to enhance accuracy of the result. Unlike SoftPOSIT, GPE also has the ability to work with three points as well as planar object models.