SlideShare uma empresa Scribd logo
1 de 15
C OMPUTER V ISION : P ROJECTIVE G EOMETRY 3D


                               IIT Kharagpur


                   Computer Science and Engineering,
                     Indian Institute of Technology
                              Kharagpur.




 (IIT Kharagpur)             Projective Geometry-3     Jan ’10   1 / 15
The projective geometry of 3D                                                   P3
A point X in 3-space is represented in homogeneous coordinates as:
                             
                          X1 
                             
                                                                  T
                             
                          X2 
                         
                       X=
                              
                          X3  =        X1      X2     X3   X4
                             
                              
                             
                         
                             
                              
                             
                           X4


    A projective transformation acting on P3 is a non-singular 4 × 4
    matrix.
                                 X = HX
    The matrix H has 15 degrees of freedom.
    The map is a collineation (lines are mapped to lines) which
    preserves incidence relations such as intersection point of a line
    with a plane, order of contact.

     (IIT Kharagpur)            Projective Geometry-3                 Jan ’10   2 / 15
Planes
 A plane in 3-space may be written as

                         π1 X + π2 Y + π3 Z + π4 = 0

 This equation is unaffected by scalar multiplication.
 The homogeneous representation of the plane is the 4-vector
 π = (π1 , π2 , π3 , π4 )T
 Homogenizing by replacements:
 X → X1 /X4 , Y → X2 /Y4 , Z → X3 /X4

              π1 X1 + π2 X2 + π3 X3 + π4 X4 = 0        πT X = 0

 The normal to the plane is given by: n = (π1 , π2 , π3 )T




  (IIT Kharagpur)            Projective Geometry-3            Jan ’10   3 / 15
Join and incidence relations
  A plane is defined uniquely by the join of 3 points, or the join of a
  line and a point, (in general position).
  Two distinct planes intersect in a unique line.
  Three distinct planes intersect in a unique point.




   (IIT Kharagpur)         Projective Geometry-3              Jan ’10   4 / 15
Three points define a plane
               A point Xi incident                     T    
               on a plane π would                      X1
                                                            
                                                             
               satisfy πT Xi = 0
                                                      
                                                            
                                                             
                                                      
                                                            
                                                             
                                                             π = 0
                                                       T
                                                       X    
                                                             
                                                      
                                                       2    
                                                      
                                                            
                                                             
                                                      
                                                            
                                                             
                                                      
                                                       T    
                                                             
                                                        X3
                                                            

                                          This is a 3 × 4 matrix with rank 3.

               The intersection
                                                       π1
                                                       T    
               point X of 3 planes                          
                                                             
               πi is obtained using:
                                                      
                                                            
                                                             
                                                      
                                                            
                                                             
                                                       π
                                                       T    
                                                      
                                                       2    X = 0
                                                             
                                                             
                                                      
                                                            
                                                             
                                                      
                                                            
                                                             
                                                            
                                                        π3
                                                       T
                                                            
                                                             



  (IIT Kharagpur)             Projective Geometry-3                   Jan ’10   5 / 15
Projective Transformation
Under the point transformation X = HX, a plane transforms as:

                             π = H−T π




     (IIT Kharagpur)       Projective Geometry-3           Jan ’10   6 / 15
Lines
  A line is defined by the join of two points or the intersection of two
  planes.
  A line has 4 degrees of freedom in 3-space. A line can be defined
  by its intersection with two orthogonal planes.




   (IIT Kharagpur)         Projective Geometry-3              Jan ’10   7 / 15
The hierarchy of transforms
                       A    t
   Projective:                  with 15 dof.
                       vT   v
                A      t
   Affine:                   with 12 dof.
                0T     1
                       sR t
   Similarity:                  with 7 dof.
                       0T 1
                       R    t
   Euclidean:                   with 6 dof.
                       0T   1




     (IIT Kharagpur)                Projective Geometry-3   Jan ’10   8 / 15
Invariants                                                    P3
  Projective:
       Intersection and tangency of surfaces in contact
  Affine:
       Parallelism of planes,
       volume ratios,
       centroids,
       The plane at infinity π∞
  Similarity:
       The absolute conic
  Euclidean:
       Volume




   (IIT Kharagpur)          Projective Geometry-3         Jan ’10   9 / 15
Comparison
In planar P2 projective           In 3-space P3 projective geometry
geometry
    Identifying the line at               Plane at infinity π∞
    infinity l∞ allowed affine
    properties of the plane
    to be measured.
    Identifying the circular
    points on l∞ allows the               Absolute conic Ω∞
    measurement of metric
    properties.




    (IIT Kharagpur)        Projective Geometry-3                Jan ’10   10 / 15
The plane at infinity
  π∞ has the canonical position π∞ = (0, 0, 0, 1)T in affine 3-space.
  Two planes are parallel, if and only if, their line of intersection is on
  π∞ .
  A line is parallel to another line, or to a plane, if the point of
  intersection is on π∞ .
  The plane π∞ is a geometric representation of the 3 degrees of
  freedom required to specify affine properties in a projective
  coordinate frame.
  The plane at infinity is a fixed plane under the projective
  transformation H if, and only if, H is an affinity.




   (IIT Kharagpur)           Projective Geometry-3               Jan ’10   11 / 15
Affine properties of a
reconstruction
  Identify π∞ in the projective coordinate frame.
  Move π∞ to its canonical position at π∞ = (0, 0, 0, 1)T .
  The scene and the reconstruction are now related by an affine
  transformation.
  Thus affine properties can now be measured directly from the
  coordinates of the entity.




   (IIT Kharagpur)         Projective Geometry-3              Jan ’10   12 / 15
The absolute conic                                                              Ω∞
  The absolute conic Ω∞ is a (point) conic on π∞ .
  In the metric frame π∞ = (0, 0, 0, 1)T and points on Ω∞ satisfy

       X2 + X2 + X2 
                     
        1    2     3 
                                      ( X 1 , X 2 , X 3 ) I ( X 1 , X 2 , X 3 )T = 0
                     
                     =0
                     
                     
                     
                 X   4

  Ω∞ corresponds to a conic C with matrix C = I.
  It is a conic of purely imaginary points on π∞ .
  The conic Ω∞ is a geometric representation of the 5 additional
  degrees of freedom that are required to specify metric properties
  in an affine coordinate frame.
  The absolute conic Ω∞ is a fixed conic under the projective
  transformation H if and only if H is a similarity transformation.


    (IIT Kharagpur)              Projective Geometry-3                    Jan ’10   13 / 15
The absolute conic                                                 Ω∞
  The absolute conic Ω∞ is only fixed as a set by a general
  similarity; it is not fixed pointwise. This means that under a
  similarity transformation, a point on Ω∞ may travel to another point
  on Ω∞ , but it is not mapped to a point off the conic.
  All circles intersect Ω∞ in two points. These points are the circular
  points of the support plane of the circle.
  All spheres intersect π∞ in Ω∞ .




   (IIT Kharagpur)         Projective Geometry-3             Jan ’10   14 / 15
Metric Properties
  Once Ω∞ and its support plane π∞ have been identified in
  projective 3-space then metric properties, such as angles and
  relative lengths, can be measured.
  Consider two lines with directions (3-vectors) d1 and d2 . The
  angle between these directions:
 In Euclidean frame                  In a projective frame
                        dT d2
                         1                                       dT Ω∞ d2
   cos θ =                                        cos θ =         1

                     (dT d1 )(dT d2 )
                       1       2                            (dT Ω∞ d1 )(dT Ω∞ d2 )
                                                              1          2
  These expressions are equivalent since in the Euclidean world
  frame Ω∞ = I




   (IIT Kharagpur)                 Projective Geometry-3                Jan ’10   15 / 15

Mais conteúdo relacionado

Mais procurados

5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
 
Advanced algebra (some terminologies)
Advanced algebra (some terminologies)Advanced algebra (some terminologies)
Advanced algebra (some terminologies)aufpaulalonzo
 
Inference for stochastic differential equations via approximate Bayesian comp...
Inference for stochastic differential equations via approximate Bayesian comp...Inference for stochastic differential equations via approximate Bayesian comp...
Inference for stochastic differential equations via approximate Bayesian comp...Umberto Picchini
 
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear TransformationsCeni Babaoglu, PhD
 
Beginning direct3d gameprogrammingmath04_calculus_20160324_jintaeks
Beginning direct3d gameprogrammingmath04_calculus_20160324_jintaeksBeginning direct3d gameprogrammingmath04_calculus_20160324_jintaeks
Beginning direct3d gameprogrammingmath04_calculus_20160324_jintaeksJinTaek Seo
 
2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and Dimension2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and DimensionCeni Babaoglu, PhD
 
CS229 Machine Learning Lecture Notes
CS229 Machine Learning Lecture NotesCS229 Machine Learning Lecture Notes
CS229 Machine Learning Lecture NotesEric Conner
 
1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear Systems1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear SystemsCeni Babaoglu, PhD
 
Beginning direct3d gameprogrammingmath03_vectors_20160328_jintaeks
Beginning direct3d gameprogrammingmath03_vectors_20160328_jintaeksBeginning direct3d gameprogrammingmath03_vectors_20160328_jintaeks
Beginning direct3d gameprogrammingmath03_vectors_20160328_jintaeksJinTaek Seo
 
Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721Glicerio Gavilan
 
ABC with data cloning for MLE in state space models
ABC with data cloning for MLE in state space modelsABC with data cloning for MLE in state space models
ABC with data cloning for MLE in state space modelsUmberto Picchini
 
An Algorithm to Find the Visible Region of a Polygon
An Algorithm to Find the Visible Region of a PolygonAn Algorithm to Find the Visible Region of a Polygon
An Algorithm to Find the Visible Region of a PolygonKasun Ranga Wijeweera
 
Math 1300: Section 4-5 Inverse of a Square Matrix
Math 1300: Section 4-5 Inverse of a Square MatrixMath 1300: Section 4-5 Inverse of a Square Matrix
Math 1300: Section 4-5 Inverse of a Square MatrixJason Aubrey
 
Numerical Methods
Numerical MethodsNumerical Methods
Numerical MethodsESUG
 
Ib mathematics hl
Ib mathematics hlIb mathematics hl
Ib mathematics hlRoss
 

Mais procurados (20)

Goldie chapter 4 function
Goldie chapter 4 functionGoldie chapter 4 function
Goldie chapter 4 function
 
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
 
Advanced algebra (some terminologies)
Advanced algebra (some terminologies)Advanced algebra (some terminologies)
Advanced algebra (some terminologies)
 
1533 game mathematics
1533 game mathematics1533 game mathematics
1533 game mathematics
 
Inference for stochastic differential equations via approximate Bayesian comp...
Inference for stochastic differential equations via approximate Bayesian comp...Inference for stochastic differential equations via approximate Bayesian comp...
Inference for stochastic differential equations via approximate Bayesian comp...
 
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
 
Acet syllabus 1 fac
Acet   syllabus 1 facAcet   syllabus 1 fac
Acet syllabus 1 fac
 
Numerical Methods 1
Numerical Methods 1Numerical Methods 1
Numerical Methods 1
 
Beginning direct3d gameprogrammingmath04_calculus_20160324_jintaeks
Beginning direct3d gameprogrammingmath04_calculus_20160324_jintaeksBeginning direct3d gameprogrammingmath04_calculus_20160324_jintaeks
Beginning direct3d gameprogrammingmath04_calculus_20160324_jintaeks
 
2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and Dimension2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and Dimension
 
CS229 Machine Learning Lecture Notes
CS229 Machine Learning Lecture NotesCS229 Machine Learning Lecture Notes
CS229 Machine Learning Lecture Notes
 
1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear Systems1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear Systems
 
Beginning direct3d gameprogrammingmath03_vectors_20160328_jintaeks
Beginning direct3d gameprogrammingmath03_vectors_20160328_jintaeksBeginning direct3d gameprogrammingmath03_vectors_20160328_jintaeks
Beginning direct3d gameprogrammingmath03_vectors_20160328_jintaeks
 
Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721
 
ABC with data cloning for MLE in state space models
ABC with data cloning for MLE in state space modelsABC with data cloning for MLE in state space models
ABC with data cloning for MLE in state space models
 
An Algorithm to Find the Visible Region of a Polygon
An Algorithm to Find the Visible Region of a PolygonAn Algorithm to Find the Visible Region of a Polygon
An Algorithm to Find the Visible Region of a Polygon
 
Math 1300: Section 4-5 Inverse of a Square Matrix
Math 1300: Section 4-5 Inverse of a Square MatrixMath 1300: Section 4-5 Inverse of a Square Matrix
Math 1300: Section 4-5 Inverse of a Square Matrix
 
Numerical Methods
Numerical MethodsNumerical Methods
Numerical Methods
 
Ib mathematics hl
Ib mathematics hlIb mathematics hl
Ib mathematics hl
 
Assignment 2 solution acs
Assignment 2 solution acsAssignment 2 solution acs
Assignment 2 solution acs
 

Semelhante a Lecture 3

11X1 T13 02 sketching polynomials
11X1 T13 02 sketching polynomials11X1 T13 02 sketching polynomials
11X1 T13 02 sketching polynomialsNigel Simmons
 
11X1 T15 02 sketching polynomials (2011)
11X1 T15 02 sketching polynomials (2011)11X1 T15 02 sketching polynomials (2011)
11X1 T15 02 sketching polynomials (2011)Nigel Simmons
 
11X1 T16 02 sketching polynomials
11X1 T16 02 sketching polynomials11X1 T16 02 sketching polynomials
11X1 T16 02 sketching polynomialsNigel Simmons
 
11 x1 t15 02 sketching polynomials (2012)
11 x1 t15 02 sketching polynomials (2012)11 x1 t15 02 sketching polynomials (2012)
11 x1 t15 02 sketching polynomials (2012)Nigel Simmons
 
11 x1 t03 02 sketching polynomials (2012)
11 x1 t03 02 sketching polynomials (2012)11 x1 t03 02 sketching polynomials (2012)
11 x1 t03 02 sketching polynomials (2012)Nigel Simmons
 
11X1 T03 02 sketching polynomials (2011)
11X1 T03 02 sketching polynomials (2011)11X1 T03 02 sketching polynomials (2011)
11X1 T03 02 sketching polynomials (2011)Nigel Simmons
 
11 X1 T03 02 sketching polynomials (2010)
11 X1 T03 02 sketching polynomials (2010)11 X1 T03 02 sketching polynomials (2010)
11 X1 T03 02 sketching polynomials (2010)Nigel Simmons
 
11 x1 t03 02 sketching polynomials (2013)
11 x1 t03 02 sketching polynomials (2013)11 x1 t03 02 sketching polynomials (2013)
11 x1 t03 02 sketching polynomials (2013)Nigel Simmons
 
Trig Review Sheet Answers
Trig Review Sheet AnswersTrig Review Sheet Answers
Trig Review Sheet Answersbwlomas
 
Lesson 24: Optimization II
Lesson 24: Optimization IILesson 24: Optimization II
Lesson 24: Optimization IIMatthew Leingang
 

Semelhante a Lecture 3 (14)

11X1 T13 02 sketching polynomials
11X1 T13 02 sketching polynomials11X1 T13 02 sketching polynomials
11X1 T13 02 sketching polynomials
 
11X1 T15 02 sketching polynomials (2011)
11X1 T15 02 sketching polynomials (2011)11X1 T15 02 sketching polynomials (2011)
11X1 T15 02 sketching polynomials (2011)
 
11X1 T16 02 sketching polynomials
11X1 T16 02 sketching polynomials11X1 T16 02 sketching polynomials
11X1 T16 02 sketching polynomials
 
11 x1 t15 02 sketching polynomials (2012)
11 x1 t15 02 sketching polynomials (2012)11 x1 t15 02 sketching polynomials (2012)
11 x1 t15 02 sketching polynomials (2012)
 
11 x1 t03 02 sketching polynomials (2012)
11 x1 t03 02 sketching polynomials (2012)11 x1 t03 02 sketching polynomials (2012)
11 x1 t03 02 sketching polynomials (2012)
 
11X1 T03 02 sketching polynomials (2011)
11X1 T03 02 sketching polynomials (2011)11X1 T03 02 sketching polynomials (2011)
11X1 T03 02 sketching polynomials (2011)
 
11 X1 T03 02 sketching polynomials (2010)
11 X1 T03 02 sketching polynomials (2010)11 X1 T03 02 sketching polynomials (2010)
11 X1 T03 02 sketching polynomials (2010)
 
11 x1 t03 02 sketching polynomials (2013)
11 x1 t03 02 sketching polynomials (2013)11 x1 t03 02 sketching polynomials (2013)
11 x1 t03 02 sketching polynomials (2013)
 
Lecture 4
Lecture 4Lecture 4
Lecture 4
 
Pisa math 2
Pisa math 2Pisa math 2
Pisa math 2
 
Pisa math 3
Pisa math 3Pisa math 3
Pisa math 3
 
Valencia 9 (poster)
Valencia 9 (poster)Valencia 9 (poster)
Valencia 9 (poster)
 
Trig Review Sheet Answers
Trig Review Sheet AnswersTrig Review Sheet Answers
Trig Review Sheet Answers
 
Lesson 24: Optimization II
Lesson 24: Optimization IILesson 24: Optimization II
Lesson 24: Optimization II
 

Mais de Krishna Karri (13)

11 mm91r05
11 mm91r0511 mm91r05
11 mm91r05
 
Translational health research
Translational health researchTranslational health research
Translational health research
 
Linear
LinearLinear
Linear
 
Lecture 7
Lecture 7Lecture 7
Lecture 7
 
Lecture 6
Lecture 6Lecture 6
Lecture 6
 
Lecture 13
Lecture 13Lecture 13
Lecture 13
 
Lecture 12
Lecture 12Lecture 12
Lecture 12
 
Lecture 11
Lecture 11Lecture 11
Lecture 11
 
Lecture 10h
Lecture 10hLecture 10h
Lecture 10h
 
Lecture 9h
Lecture 9hLecture 9h
Lecture 9h
 
Lecture9
Lecture9Lecture9
Lecture9
 
Segclus
SegclusSegclus
Segclus
 
Lecture 5
Lecture 5Lecture 5
Lecture 5
 

Lecture 3

  • 1. C OMPUTER V ISION : P ROJECTIVE G EOMETRY 3D IIT Kharagpur Computer Science and Engineering, Indian Institute of Technology Kharagpur. (IIT Kharagpur) Projective Geometry-3 Jan ’10 1 / 15
  • 2. The projective geometry of 3D P3 A point X in 3-space is represented in homogeneous coordinates as:    X1    T    X2   X=   X3  = X1 X2 X3 X4            X4 A projective transformation acting on P3 is a non-singular 4 × 4 matrix. X = HX The matrix H has 15 degrees of freedom. The map is a collineation (lines are mapped to lines) which preserves incidence relations such as intersection point of a line with a plane, order of contact. (IIT Kharagpur) Projective Geometry-3 Jan ’10 2 / 15
  • 3. Planes A plane in 3-space may be written as π1 X + π2 Y + π3 Z + π4 = 0 This equation is unaffected by scalar multiplication. The homogeneous representation of the plane is the 4-vector π = (π1 , π2 , π3 , π4 )T Homogenizing by replacements: X → X1 /X4 , Y → X2 /Y4 , Z → X3 /X4 π1 X1 + π2 X2 + π3 X3 + π4 X4 = 0 πT X = 0 The normal to the plane is given by: n = (π1 , π2 , π3 )T (IIT Kharagpur) Projective Geometry-3 Jan ’10 3 / 15
  • 4. Join and incidence relations A plane is defined uniquely by the join of 3 points, or the join of a line and a point, (in general position). Two distinct planes intersect in a unique line. Three distinct planes intersect in a unique point. (IIT Kharagpur) Projective Geometry-3 Jan ’10 4 / 15
  • 5. Three points define a plane A point Xi incident  T  on a plane π would  X1    satisfy πT Xi = 0         π = 0  T  X     2            T   X3   This is a 3 × 4 matrix with rank 3. The intersection  π1  T  point X of 3 planes    πi is obtained using:          π  T    2 X = 0             π3  T    (IIT Kharagpur) Projective Geometry-3 Jan ’10 5 / 15
  • 6. Projective Transformation Under the point transformation X = HX, a plane transforms as: π = H−T π (IIT Kharagpur) Projective Geometry-3 Jan ’10 6 / 15
  • 7. Lines A line is defined by the join of two points or the intersection of two planes. A line has 4 degrees of freedom in 3-space. A line can be defined by its intersection with two orthogonal planes. (IIT Kharagpur) Projective Geometry-3 Jan ’10 7 / 15
  • 8. The hierarchy of transforms A t Projective: with 15 dof. vT v A t Affine: with 12 dof. 0T 1 sR t Similarity: with 7 dof. 0T 1 R t Euclidean: with 6 dof. 0T 1 (IIT Kharagpur) Projective Geometry-3 Jan ’10 8 / 15
  • 9. Invariants P3 Projective: Intersection and tangency of surfaces in contact Affine: Parallelism of planes, volume ratios, centroids, The plane at infinity π∞ Similarity: The absolute conic Euclidean: Volume (IIT Kharagpur) Projective Geometry-3 Jan ’10 9 / 15
  • 10. Comparison In planar P2 projective In 3-space P3 projective geometry geometry Identifying the line at Plane at infinity π∞ infinity l∞ allowed affine properties of the plane to be measured. Identifying the circular points on l∞ allows the Absolute conic Ω∞ measurement of metric properties. (IIT Kharagpur) Projective Geometry-3 Jan ’10 10 / 15
  • 11. The plane at infinity π∞ has the canonical position π∞ = (0, 0, 0, 1)T in affine 3-space. Two planes are parallel, if and only if, their line of intersection is on π∞ . A line is parallel to another line, or to a plane, if the point of intersection is on π∞ . The plane π∞ is a geometric representation of the 3 degrees of freedom required to specify affine properties in a projective coordinate frame. The plane at infinity is a fixed plane under the projective transformation H if, and only if, H is an affinity. (IIT Kharagpur) Projective Geometry-3 Jan ’10 11 / 15
  • 12. Affine properties of a reconstruction Identify π∞ in the projective coordinate frame. Move π∞ to its canonical position at π∞ = (0, 0, 0, 1)T . The scene and the reconstruction are now related by an affine transformation. Thus affine properties can now be measured directly from the coordinates of the entity. (IIT Kharagpur) Projective Geometry-3 Jan ’10 12 / 15
  • 13. The absolute conic Ω∞ The absolute conic Ω∞ is a (point) conic on π∞ . In the metric frame π∞ = (0, 0, 0, 1)T and points on Ω∞ satisfy X2 + X2 + X2   1 2 3  ( X 1 , X 2 , X 3 ) I ( X 1 , X 2 , X 3 )T = 0  =0    X  4 Ω∞ corresponds to a conic C with matrix C = I. It is a conic of purely imaginary points on π∞ . The conic Ω∞ is a geometric representation of the 5 additional degrees of freedom that are required to specify metric properties in an affine coordinate frame. The absolute conic Ω∞ is a fixed conic under the projective transformation H if and only if H is a similarity transformation. (IIT Kharagpur) Projective Geometry-3 Jan ’10 13 / 15
  • 14. The absolute conic Ω∞ The absolute conic Ω∞ is only fixed as a set by a general similarity; it is not fixed pointwise. This means that under a similarity transformation, a point on Ω∞ may travel to another point on Ω∞ , but it is not mapped to a point off the conic. All circles intersect Ω∞ in two points. These points are the circular points of the support plane of the circle. All spheres intersect π∞ in Ω∞ . (IIT Kharagpur) Projective Geometry-3 Jan ’10 14 / 15
  • 15. Metric Properties Once Ω∞ and its support plane π∞ have been identified in projective 3-space then metric properties, such as angles and relative lengths, can be measured. Consider two lines with directions (3-vectors) d1 and d2 . The angle between these directions: In Euclidean frame In a projective frame dT d2 1 dT Ω∞ d2 cos θ = cos θ = 1 (dT d1 )(dT d2 ) 1 2 (dT Ω∞ d1 )(dT Ω∞ d2 ) 1 2 These expressions are equivalent since in the Euclidean world frame Ω∞ = I (IIT Kharagpur) Projective Geometry-3 Jan ’10 15 / 15