Shape from perspective: A rule-based approach
Computer Vision, Graphics, and Image Processing
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
New methods for matching 3-D objects with single perspective views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraints on length and angle
Computer Vision, Graphics, and Image Processing
Pose Determination of a Three-Dimensional Object Using Triangle Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analytic solution for the perspective 4-point problem
Computer Vision, Graphics, and Image Processing
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric computation for machine vision
Geometric computation for machine vision
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Model Based Pose Estimator Using Linear-Programming
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
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This paper investigates a fundamental problem of determining the position and orientation of a three-dimensional (3-D) object using a single perspective image view. The technique is focused on the interpretation of trihedral angle constraint information. A new closed form solution based on Kanatani's formulation is proposed. The main distinguishing feature of the authors' method over the original Kanatani formulation is that their approach gives an effective closed form solution for a general trihedral angle constraint. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using "angle to angle correspondence information." A detailed implementation of the authors' technique is presented. Different trihedral angle configurations were generated using synthetic data for testing the authors' approach of finding object orientation by angle to angle constraint. The authors performed simulation experiments by adding some noise to the synthetic data for evaluating the effectiveness of their method in a real situation. It has been found that the authors' method worked effectively in a noisy environment which confirms that the method is robust in practical application.