Difference between revisions of "Tutorial Geometric Vision"
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A major component of 3D computer vision is the study of [http://en.wikipedia.org/wiki/Epipolar_geometry epipolar geometry], the geometry of two views. BoofCV provides many standard algorithms for mathematically describing features across two views and more. Below is a list of these algorithms. The API is still being worked on and a more detailed tutorial will be written later on. | A major component of 3D computer vision is the study of [http://en.wikipedia.org/wiki/Epipolar_geometry epipolar geometry], the geometry of two views. BoofCV provides many standard algorithms for mathematically describing features across two views and more. Below is a list of these algorithms. The API is still being worked on and a more detailed tutorial will be written later on. | ||
'''AS A WHOLE THIS CODE SHOULD BE CONSIDERED EXPERIMENTAL''' | |||
Recommend Reading: | Recommend Reading: | ||
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Algorithm List | Algorithm List | ||
* Fundamental Matrix 8 Points | * Fundamental/Essential Matrix 8+ Points | ||
* Fundamental Matrix 7 Points | * Fundamental/Essential Matrix 7 Points | ||
* Fundamental Optimization | * Essential Matrix 5 Points | ||
* Fundamental Matrix Non-linear Optimization | |||
** Sampson Error | ** Sampson Error | ||
** Epipolar Error | ** Epipolar Error | ||
Revision as of 16:06, 18 September 2012
Geometric Computer Vision Tutorial
A major component of 3D computer vision is the study of epipolar geometry, the geometry of two views. BoofCV provides many standard algorithms for mathematically describing features across two views and more. Below is a list of these algorithms. The API is still being worked on and a more detailed tutorial will be written later on.
AS A WHOLE THIS CODE SHOULD BE CONSIDERED EXPERIMENTAL
Recommend Reading:
- Yi Ma, et. al., "An Invitation to 3-D Vision"
- Algebraic approach to 3D vision.
- Easy to use as a reference.
- R. Hartley, and A. Zisserman, "Multiple View Geometry in Computer Vision"
- Geometric approach and the most popular book on this subject.
- More content and good algorithms.
- Mediocre index and tendency to not define terms in some chapters.
Algorithm List
- Fundamental/Essential Matrix 8+ Points
- Fundamental/Essential Matrix 7 Points
- Essential Matrix 5 Points
- Fundamental Matrix Non-linear Optimization
- Sampson Error
- Epipolar Error
- Homography 4 Points (Linear)
- Homography Optimization
- Sampson Error
- Transfer Error
- Efficient PnP 4-Point
- Linear 6 Point Pose
- Linear Pixel Depth
- Triangulation Geometric
- Triangulation Linear
- Triangulation Optimization
- Sampson Error
- Euclidean Error
- Decompose Essential
- Decompose Homography
- Bundle Adjustment
- Dense (inefficient/slow)
- Stereo Rectification
- Calibrated
- Uncalibrated
Examples
Related
- Zhang 99 Camera Calibration
- Various different feature trackers and detectors
