Difference between revisions of "Tutorial Geometric Vision"
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= Geometric Computer Vision Tutorial = | = Geometric Computer Vision Tutorial = | ||
The low level mathematics used to estimate the scene's structure and camera ego motion are contained in the Geometric Vision package in BoofCV. Most of the standard algorithms in this field are provided with numerious options for comptuing and refining constructs such as the Fundamental/Essential matrix, Trifocal Tensor, camera pose, and points/lines. | |||
These algorithms are typically used in structure from motiom (SFM) and their correct usage is not trivial, see below for a list of books on the subject. The API is still being refined to help make this process easier. As is typical with BoofCV, most of the documentation on usage is provided in the form of examples and JavaDoc comments. | |||
When reviewing the JavaDoc pay close attention to the type of inputs it takes (e.g. pixel or normalized image coordinates) and the direction of the reference frame transform. Pixels refers to coordinates in the image while normalized image coordinates are in Euclidean space and found by multiplying pixels by the inverse of the intrinsic camera calibration matirx. | |||
== Examples == | |||
* [[Example_Fundamental_Matrix| Compute Fundamental Matrix]] | |||
== Algorithm List == | |||
* Fundamental/Essential Matrix 8+ Points | * Fundamental/Essential Matrix 8+ Points | ||
* Fundamental/Essential Matrix 7 Points | * Fundamental/Essential Matrix 7 Points | ||
| Line 27: | Line 24: | ||
** Sampson Error | ** Sampson Error | ||
** Transfer Error | ** Transfer Error | ||
* Linear 6 Point Pose | * Linear 6 Point Pose | ||
* Linear Pixel Depth | * Linear Pixel Depth | ||
* Perspective-N-Point (PnP) | |||
** Efficient PnP 4-Point (EPnP) | |||
** P3P Grunert | |||
** P3P Finsterwalder | |||
** Non-linear optimization | |||
* Triangulation Geometric | * Triangulation Geometric | ||
* Triangulation Linear | * Triangulation Linear | ||
| Line 35: | Line 36: | ||
** Sampson Error | ** Sampson Error | ||
** Euclidean Error | ** Euclidean Error | ||
* Trifocal Tensor | |||
** Linear 7 point | |||
* Decompose Essential | * Decompose Essential | ||
* Decompose Homography | * Decompose Homography | ||
| Line 43: | Line 46: | ||
** Uncalibrated | ** Uncalibrated | ||
== Camera Model == | |||
ADD TO THIS | |||
= Recommend Reading = | |||
* [http://www.amazon.com/gp/product/0387008934/ref=as_li_ss_tl?ie=UTF8&tag=boofcv-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=0387008934 Yi Ma, et. al., "An Invitation to 3-D Vision"] | |||
* | ** Algebraic approach to 3D vision. | ||
* | ** Easy to use as a reference. | ||
* [http://www.amazon.com/gp/product/0521540518/ref=as_li_ss_tl?ie=UTF8&tag=boofcv-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=0521540518 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. | |||
Revision as of 05:43, 6 November 2012
Geometric Computer Vision Tutorial
The low level mathematics used to estimate the scene's structure and camera ego motion are contained in the Geometric Vision package in BoofCV. Most of the standard algorithms in this field are provided with numerious options for comptuing and refining constructs such as the Fundamental/Essential matrix, Trifocal Tensor, camera pose, and points/lines.
These algorithms are typically used in structure from motiom (SFM) and their correct usage is not trivial, see below for a list of books on the subject. The API is still being refined to help make this process easier. As is typical with BoofCV, most of the documentation on usage is provided in the form of examples and JavaDoc comments.
When reviewing the JavaDoc pay close attention to the type of inputs it takes (e.g. pixel or normalized image coordinates) and the direction of the reference frame transform. Pixels refers to coordinates in the image while normalized image coordinates are in Euclidean space and found by multiplying pixels by the inverse of the intrinsic camera calibration matirx.
Examples
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
- Linear 6 Point Pose
- Linear Pixel Depth
- Perspective-N-Point (PnP)
- Efficient PnP 4-Point (EPnP)
- P3P Grunert
- P3P Finsterwalder
- Non-linear optimization
- Triangulation Geometric
- Triangulation Linear
- Triangulation Optimization
- Sampson Error
- Euclidean Error
- Trifocal Tensor
- Linear 7 point
- Decompose Essential
- Decompose Homography
- Bundle Adjustment
- Dense (inefficient/slow)
- Stereo Rectification
- Calibrated
- Uncalibrated
Camera Model
ADD TO THIS
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.
