Determination of Camera Location from 2-D to 3-D Line and Point Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Linear Solution of Exterior Orientation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cryptanalysis of the HFE Public Key Cryptosystem by Relinearization
CRYPTO '99 Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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A novel linear algorithm to estimate the camera pose from known correspondences of 3D points and their 2D image points is proposed based on the angle constraints from arbitrary three points in 3D point set. Compared with Ansar's N Point Linear method which is based on the distance constraints between 3D points, due to more strict geometric constraints, this approach is more accurate. Simultaneously some strategies of choosing constraint equations are introduced so that this algorithm's computational complexity is reduced. In order to obtain more accurate estimated pose, we propose the singular value decomposition method to derive the parameters from their quadratic terms more exactly. Finally, the experiments show our approach's effectiveness and accuracy compared with the other two algorithms using synthetic data and real images.