CS 4204 Computer Graphics

Transcription

1 CS 4204 Computer Graphics Vector and Matrix Yong Cao Virginia Tech

2 Vectors N-tuple:

3 Vectors N-tuple tuple: Magnitude: Unit vectors Normalizing a vector

4 Operations with vectors Addition Multiplication with scalar (scaling) Properties

5 Visualization for 2D and 3D vectors Addition Scaling

6 Subtraction Adding the negatively scaled vector

7 Linear combination of vectors Definition A linear combination of the m vectors v 1,,v is a m vector of the form: w = a 1 v 1 + a m v m, a 1,,a m in R

8 Special cases Linear combination w = a 1 v 1 + a m v m, a 1,,a m in R Affine combination: A linear combination for which a 1 + +a m = 1 Convex combination An affine combination for which a i 0 for i = 1,,m,m

9 Linear Independence For vectors v 1,, v m If a 1 v 1 + a m v m = 0 iff a 1 =a 2 = =a=a m =0 then the vectors are linearly independent.

10 Generators and Base vectors How many vectors are needed to generate a vector space? Any set of vectors that generate a vector space is called a generator set. Given a vector space R n we can prove that we need minimum n vectors to generate all vectors v in R n. A generator set with minimum size is called a base for the given vector space.

11 Standard unit vectors

12 Standard unit vectors For any vector space R n : The elements of a vector v in R n are the scalar coefficients of the linear combination of the base vectors.

13 Standard unit vectors in 3D i = (1,0,0) j = (0,1,0) k = (0,0,1) Right handed Left handed

14 Representation of vectors through basis vectors Given a vector space R n, a set of basis vectors B {b{ i in R n, i=1, n} and a vector v in R n we can always find scalar coefficients such that: v = a 1 b 1 + +a n b n So, v with respect to B is: v B = (a( 1,,a n )

15 Dot Product Definition: Properties

16 Dot product and perpendicularity From Property 5: b c > 0 b c = 0 b c < 0

17 Perpendicular vectors Definition Vectors b and c are perpendicular iff b c = 0 Also called normal or orthogonal It is easy to see that the standard unit vectors form an orthogonal basis: i j = 0, j k = 0, i k = 0

18 Cross product Defined only for 3D Vectors and with respect to the standard unit vectors Definition

19 Properties of the cross product

20 Geometric interpretation of the cross product

21 Recap Vector spaces Operations with vectors Representing vectors through a basis v = a 1 b 1 + a n b n, v B = (a 1,,a,a n ) Standard unit vectors Dot product Perpendicularity Cross product Normal to both vectors

22 Points vs Vectors What is the difference?

23 Points vs Vectors What is the difference? Points have location but no size or direction. Vectors have size and direction but no location. Problem: we represent both as triplets!

24 Relationship between points and vectors A difference between two points is a vector: Q P = v Q P v We can consider a point as a point plus an offset Q = P + v

25 Coordinate systems Defined by: (a,b,c, )

26 The homogeneous representation of points and vectors

27 Switching coordinates Normal to homegeneous: Vector: append as fourth coordinate 0 Point: append as fourth coordinate 1

28 Switching coordinates Homegeneous to normal: Vector: remove fourth coordinate (0) Point: remove fourth coordinate (1)

29 Does the homogeneous representation support operations? Operations : v + w = (v 1,v 2,v 3,0) +(w 1,w 2,w 3,0)= (v 1 +w 1, v 2 +w 2, v 3 +w 3, 0) Vector! av = a(v 1,v 2,v 3,0) = (av 1, av 2, av 3, 0), Vector! av + bw = a(v 1,v 2,v 3,0) +b(w 1,w 2,w 3,0)= (av 1 +bw 1, av 2 +bw 2, av 3 +bw 3, 0) Vector! P+v = (p 1,p 2,p 3,1) +(v 1,v 2,v 3,0)= = (p 1 +v 1, p 2 +v 2, p 3 +v 3, 1) Point!

30 Linear combination of points Points P, R scalars f,g: fp+gr = f(p 1,p 2,p 3,1) +g(r 1,r 2,r 3,1) = (fp 1 +gr 1, fp 2 +gr 2, fp 3 +gr 3, f+g) What is this?

31 Linear combination of points Points P, R scalars f,g: fp+gr = f(p 1,p 2,p 3,1) +g(r 1,r 2,r 3,1) = (fp 1 +gr 1, fp 2 +gr 2, fp 3 +gr 3, f+g) What is this? If (f+g( f+g) ) = 0 then vector! If (f+g( f+g) ) = 1 then point!

32 Affine combinations of points Definition: Points P i : i = 1,,n,n Scalars f i : i = 1,,n,n f 1 P f n P n iff f 1 + +f n = 1 Example: 0.5P P 2

33 Geometric explanation

34 Recap Vector spaces Dot product Cross product Coordinate systems Homogeneous representations of points and vectors

35 Matrices Rectangular arrangement of elements:

36 Special square matrices Symmetric: (A( ij ) n x n = (A( ji ) n x n Zero: A ij = 0, for all i,j Identity: I n = I ii = 1, for all i I ij = 0 for i j

37 Operations with matrices Addition: Properties:

38 Multiplication Definition: Properties:

39 Inverse of a square matrix Definition MM -1 = M -1 M = I Important property (AB) -1 = B -1-1 A -1

40 Convention Vectors and points are represented as column matrices.

41 Dot product as a matrix multiplication A vector is a column matrix

42 Lines and Planes

43 Lines Line (in 2D) Explicit Implicit Parametric (extends to 3D)

44 Planes Plane equations Implicit F(x, y,z) = Ax + By + Cz + D = N P + D Points on Plane F(x, y,z) = 0 Parametric Plane(s,t) = P 0 + s(p 1 P 0 ) + t(p 2 P 0 ) P 0,P 1,P 2 not colinear or Plane(s,t) = (1 s t)p 0 + sp 1 + tp 2 Plane(s,t) = P 0 + sv 1 + tv 2 where V 1,V 2 basis vectors Explicit z = - (A/C)x - (B/C)y - D/C, C 0