Essential Matrix Estimation
This code uses a five point algorithm in a RANSAC framework to compute a robust initial estimate of the essential matrix.
That estimate is subsequently refined by parameterizing the essential matrix with six parameters (3 for the Rodrigues vector and 3 for the translation vector) and minimizing the cumulative symmetric distance from epipolar lines for RANSAC inliers with the Levenberg–Marquardt algorithm.
NOTE: The code requires several functions by others, see README.txt for further instructions.
See also https://en.wikipedia.org/wiki/Essential_matrix
Zitieren als
Manolis Lourakis (2026). Essential Matrix Estimation (https://de.mathworks.com/matlabcentral/fileexchange/67580-essential-matrix-estimation), MATLAB Central File Exchange. Abgerufen.
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- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation > Image Category Classification >
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E_estim
| Version | Veröffentlicht | Versionshinweise | |
|---|---|---|---|
| 1.1 | Minor changes to ransacfitessmatrix.m Included 3rd party scripts |
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| 1.0.0.0 | Updated description
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