06.geometry+transform - Clarkson Universitytransform… · Consider the point and the vector P = P...

CS452/552; EE465/505

Geometry Transformations

1-26-15

!Geometry: scalars, points & vectors ! Transformations

Read: ✦ Angel, Chapter 4 (study cube.html/cube.js example) ✦ Appendix B: Spaces (vector, affine, Euclidean) ✦ Appendix C: Matrices

Helpful links: ■ Linear Algebra: Khan Academy

Lab1 is posted on github, due: Friday, 1/30, midnight

Outline

https://www.khanacademy.org/math/linear-algebra

Jeff Chastine

The Local Coordinate System! Sometimes called “Object Space” (or Model Space”) ! It’s the coordinate system the model was made in

(0, 0, 0)

Jeff Chastine

The World SPACE! The coordinate system of the virtual environment

(619, 10, 628)

Jeff Chastine

(619, 10, 628)

Jeff Chastine

Camera Space (Eye Space)! It’s all relative to the camera… (or eye)

Jeff Chastine

Camera Space! It’s all relative to the camera…

(0, 0, -10)

Jeff Chastine

The Big Picture! How do we represent points, polygons, etc. ! How to we get from space to space? ! How do we move, scale and rotate objects?

? ?

! Introduce the elements of geometry ! Develop mathematical operations among them in a coordinate-free manner

! Define basic primitives ✦ Line segments ✦ Polygons

Geometry

Angel and Shreiner: Interactive Computer Graphics 7E © Addison-Wesley 2015

! Geometry is the study of the relationships among objects in an n-dimensional space ✦ In computer graphics, we are interested in objects that exist in three dimensions

!Want a minimum set of primitives from which we can build more sophisticated objects

!We will need three basic elements ✦Scalars ✦Vectors ✦ Points

Basic Elements


!When we learned simple geometry, most of us started with a Cartesian approach ✦ Points were at locations in space p=(x,y,z) ✦We derived results by algebraic manipulations involving these coordinates

! This approach was nonphysical ✦ Physically, points exist regardless of the location of an arbitrary coordinate system

✦Most geometric results are independent of the coordinate system

✦ Example Euclidean geometry: two triangles are identical if two corresponding sides and the angle between them are identical

Coordinate-Free Geometry


! Need three basic elements in geometry ✦Scalars, Vectors, Points

! Scalars can be defined as members of sets which can be combined by two operations (addition and multiplication) obeying some fundamental axioms (associativity, commutivity, inverses)

! Examples include the real and complex number systems under the ordinary rules with which we are familiar

! Scalars alone have no geometric properties

Scalars


! Physical definition: a vector is a quantity with two attributes ✦Direction ✦Magnitude

! Examples include ✦ Force ✦Velocity ✦Directed line segments ✤Most important example for graphics ✤Can map to other types

Vectors

v


! Every vector has an inverse ✦Same magnitude but points in opposite direction

! Every vector can be multiplied by a scalar ! There is a zero vector

✦ Zero magnitude, undefined orientation ! The sum of any two vectors is a vector

✦Use head-to-tail axiom

Vector Operations

v -v αvv

u

w


! Mathematical system for manipulating vectors ! Operations

✦Scalar-vector multiplication u=αv ✦Vector-vector addition: w=u+v

! Expressions such as v=u+2w-3r

Make sense in a vector space

Linear Vector Spaces

! These vectors are identical ✦Same length and magnitude

! Vectors spaces insufficient for geometry ✦Need points

Vectors Lack Position


! Location in space ! Operations allowed between points and vectors

✦ Point-point subtraction yields a vector ✦ Equivalent to point-vector addition

Points

P=v+Q

v=P-Q


! Point + a vector space ! Operations

✦Vector-vector addition ✦Scalar-vector multiplication ✦ Point-vector addition ✦Scalar-scalar operations

! For any point define ✦ 1 • P = P ✦ 0 • P = 0 (zero vector)

Affine Spaces


! Consider all points of the form ✦ P(α)=P0 + α d

✦Set of all points that pass through P0 in the direction of the vector d

Lines


! This form is known as the parametric form of the line ✦More robust and general than other forms ✦ Extends to curves and surfaces

! Two-dimensional forms ✦ Explicit: y = mx +h ✦ Implicit: ax + by +c =0 ✦ Parametric: x(α) = αx0 + (1-α)x1

y(α) = αy0 + (1-α)y1

Parametric Form


! If α >= 0, then P(α) is the ray leaving P0 in the direction d

If we use two points to define v, then P( α) = Q + α (R-Q)=Q+αv = αR + (1-α)Q

For 0<=α<=1 we get all the points on the line segment joining R and Q

Rays and Line Segments


! An object is convex iff for any two points in the object all points on the line segment between these points are also in the object

Convexity

P

Q

convex

Q

P

not convex


! Consider the “sum” P=α1P1+α2P2+…..+αnPn

Can show by induction that this sum makes sense iff α1+α2+…..αn=1

in which case we have the affine sum of the points P1,P2,…..Pn

! If, in addition, αi>=0, we have the convex hull of P1,P2,…..Pn

Affine Sums


! Smallest convex object containing P1,P2,…..Pn

! Formed by “shrink wrapping” points

Convex Hull


!Dot (inner) product of 2 vectors: u · v

!Cross (outer) product of 2 (non-parallel) vectors: u × v

25

Dot and Cross Product

! A plane can be defined by a point and two vectors or by three points

Planes

P(α,β)=R+αu+βv

u

v

R

Q

P(α,β)=R+α(Q-R)+β(P-Q)

P

R


Triangles

convex sum of S(α) and R

for 0<=α,β<=1, we get all points in triangle


convex sum of P and Q

Triangle is convex so any point inside can be represented as an affine sum

P(α1, α2, α3)=α1P+α2Q+α3R where

α1 +α2 +α3 = 1 αi>=0

The representation is called the barycentric coordinate representation of P

Barycentric Coordinates


! In three dimensional spaces, every plane has a vector n perpendicular or orthogonal to it called the normal vector

! From the two-point vector form P(α,β)=P+αu+βv, we know we can use the cross product to find n = u × v and the equivalent form

(P(α, β)-P) ⋅ n=0

Normals

u

v

P


! Curves are one parameter entities of the form P(α) where the function is nonlinear

! Surfaces are formed from two-parameter functions P(α, β) ✦ Linear functions give planes and polygons

Curves and Surfaces

P(α) P(α, β)


! Introduce concepts such as dimension and basis

! Introduce coordinate systems for representing vectors spaces and frames for representing affine spaces

! Discuss change of frames and bases ! Introduce homogeneous coordinates

Coordinate Systems & Frames


! A set of vectors v1, v2, …, vn is linearly independent if

α1v1+α2v2+.. αnvn=0 iff α1=α2=…=0 ! If a set of vectors is linearly independent, we cannot represent one in terms of the others

! If a set of vectors is linearly dependent, at least one can be written in terms of the others

Linear Independence


! In a vector space, the maximum number of linearly independent vectors is fixed and is called the dimension of the space

! In an n-dimensional space, any set of n linearly independent vectors form a basis for the space

! Given a basis v1, v2,…., vn, any vector v can be written as

v=α1v1+ α2v2 +….+αnvn

where the {αi} are unique

Dimension


! Until now we have been able to work with geometric entities without using any frame of reference, such as a coordinate system

! Need a frame of reference to relate points and objects to our physical world. ✦ For example, where is a point? Can’t answer without a reference system

✦World coordinates ✦Camera coordinates

Representation


! Consider a basis v1, v2,…., vn

! A vector is written v=α1v1+ α2v2 +….+αnvn

! The list of scalars {α1, α2, …. αn}is the representation of v with respect to the given basis

!We can write the representation as a row or column array of scalars

Coordinate Systems

a=[α1 α2 …. αn]T=

!!!!!

"

#

$$$$$

%

&

α

α

α

n

2

1

.


v=2v1+3v2-4v3

a=[2 3 –4]T

! Note that this representation is with respect to a particular basis

! For example, in WebGL we will start by representing vectors using the object basis but later the system needs a representation in terms of the camera or eye basis

Example


!Which is correct?

! Both are because vectors have no fixed location

Coordinate Systems

v

v


! A coordinate system is insufficient to represent points

! If we work in an affine space we can add a single point, the origin, to the basis vectors to form a frame

Frames

P0

v1

v2

v3


! Frame determined by (P0, v1, v2, v3) !Within this frame, every vector can be written as

v=α1v1+ α2v2 +….+αnvn

! Every point can be written as P = P0 + β1v1+ β2v2 +….+βnvn

Representation in a Frame


Consider the point and the vector P = P0 + β1v1+ β2v2 +….+βnvn v=α1v1+ α2v2 +….+αnvn

They appear to have the similar representations p=[β1 β2 β3] v=[α1 α2 α3] which confuses the point with the vector A vector has no position

Points differ from Vectors

v

pv

Vector can be placed anywhere

point: fixed


! Introduce homogeneous coordinates ! Introduce change of representation for both vectors and points

Homogeneous Coordinates


If we define 0•P = 0 and 1•P =P then we can write v=α1v1+ α2v2 +α3v3 = [α1 α2 α3 0 ] [v1 v2 v3 P0] T

P = P0 + β1v1+ β2v2 +β3v3= [β1 β2 β3 1 ] [v1 v2 v3 P0] T

Thus we obtain the four-dimensional homogeneous coordinate representation

v = [α1 α2 α3 0 ] T

p = [β1 β2 β3 1 ] T

A Single Representation


! Homogeneous coordinates are key to all computer graphics systems ✦All standard transformations (rotation, translation, scaling) can be implemented with matrix multiplications using 4 x 4 matrices

✦Hardware pipeline works with 4 dimensional representations

✦ For orthographic viewing, we can maintain w=0 for vectors and w=1 for points

✦ For perspective we need a perspective division

Homogeneous Coordinates and Computer Graphics


! Consider two representations of a the same vector with respect to two different bases. The representations are

Change of Coordinate Systems

v=α1v1+ α2v2 +α3v3 = [α1 α2 α3] [v1 v2 v3] T

=β1u1+ β2u2 +β3u3 = [β1 β2 β3] [u1 u2 u3] T

a=[α1 α2 α3 ]b=[β1 β2 β3]

where


Representing second basis in terms of first

u1 = γ11v1+γ12v2+γ13v3 u2 = γ21v1+γ22v2+γ23v3 u3 = γ31v1+γ32v2+γ33v3

v


! Each of the basis vectors, u1,u2, u3, are vectors that can be represented in terms of the first basis

The coefficients define a 3 x 3 matrix

and the bases can be related by

see text for numerical examples

Matrix Form

a=MTb

!!!

"

#

$$$

%

&

γγγ

γγγ

γγγ

3231

232221

131211

33

M =


!We can apply a similar process in homogeneous coordinates to the representations of both points and vectors

! Any point or vector can be represented in either frame ! We can represent Q0, u1, u2, u3 in terms of P0, v1, v2, v3

Change of Frames

Consider two frames: (P0, v1, v2, v3) (Q0, u1, u2, u3)

P0 v1

v2

v3

Q0

u1u2

u3


u1 = γ11v1+γ12v2+γ13v3 u2 = γ21v1+γ22v2+γ23v3 u3 = γ31v1+γ32v2+γ33v3 Q0 = γ41v1+γ42v2+γ43v3 +γ44P0

Representing One Frame in Terms of the Other

defining a 4 x 4 matrix

!!!!

"

#

$$$$

%

&

1γγγ

0γγγ

0γγγ

0γγγ

434241

333231

232221

131211

M =

! Extending what we did with change of bases


!Within the two frames any point or vector has a representation of the same form

a=[α1 α2 α3 α4 ] in the first frame b=[β1 β2 β3 β4 ] in the second frame

where α4 = β4 = 1 for points and α4 = β4 = 0 for vectors and

The matrix M is 4 x 4 and specifies an affine transformation in homogeneous coordinates

Working with Representations

a=MTb


! Every linear transformation is equivalent to a change in frames

! Every affine transformation preserves lines ! However, an affine transformation has only 12

degrees of freedom because 4 of the elements in the matrix are fixed and are a subset of all possible 4 x 4 linear transformations

Affine Transformations


Consider the pipeline

✦Model Coordinates ✦Object (or World) Coordinates ✦Eye (or Camera) Coordinates ✦Clip Coordinates ✦Normalized Device Coordinates ✦Window (or Screen) Coordinates

51

Frames in WebGL

!When we work with representations, we work with n-tuples or arrays of scalars

! Changes in frame are then defined by 4 x 4 matrices

! In OpenGL/WebGL, the base frame that we start with is the world frame

! Eventually we represent entities in the camera frame by changing the world representation using the model-view matrix

! Initially these frames are the same (M=I)

The World and Camera Frames


! If objects are on both sides of z=0, we must move camera frame

Moving the Camera

!!!!

"

#

$$$$

%

&

−

1000d10000100001

M =


! Introduce standard transformations ✦Rotation ✦Translation ✦Scaling ✦Shear

! Derive homogeneous coordinate transformation matrices

! Learn to build arbitrary transformation matrices from simple transformations

Transformations


General Transformations

Q=T(P)

v=T(u)


! A transformation maps points to other points and/or vectors to other vectors

! Line preserving ! Characteristic of many physically important transformations ✦Rigid body transformations: rotation, translation ✦Scaling, shear

! Importance in graphics is that we need only transform endpoints of line segments and let implementation draw line segment between the transformed endpoints

Affine Transformations


v

Pipeline Implementation

T(u)

T(v)

u

v

T(u)vertices vertices pixels

transformation rasterizer

u

T

T(u)

T(v)

T(v)

frame buffer

(from application program)


P,Q, R: points in an affine space u, v, w: vectors in an affine space α, β, γ: scalars p, q, r: representations of points

✦array of 4 scalars in homogeneous coordinates u, v, w: representations of vectors

✦array of 4 scalars in homogeneous coordinates

Notation!We will be working with both coordinate-free representations of transformations and representations within a particular frame


! Move (translate, displace) a point to a new location

! Displacement determined by a vector d ✦Three degrees of freedom ✦ P’=P+d

Translation

P

P’

d


How many ways?

object translation: every point displaced by same vector


! Although we can move a point to a new location in infinite ways, when we move many points there is usually only one way

Using the homogeneous coordinate representation in some frame

p=[ x y z 1]T

p’=[x’ y’ z’ 1]T

d=[dx dy dz 0]T

Hence p’ = p + d or

x’=x+dx

y’=y+dy

z’=z+dz

Translation Using Representations

note that this expression is in four dimensions and expresses

point = vector + point


We can also express translation using a 4 x 4 matrix T in homogeneous coordinates p’=Tp where

T = T(dx, dy, dz) =

This form is better for implementation because all affine transformations can be expressed this way and multiple transformations can be concatenated together

Translation Matrix

!!!!

"

#

$$$$

%

&

1000d100d010d001

z

y

x


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06.geometry+transform - Clarkson Universitytransform… · Consider the point and the vector P = P...

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