CSE 167: Introduction to Computer Graphics Lecture 12: Bézier Curves. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2013

Transcription

1 CSE 167: Introduction to Computer Graphics Lecture 12: Bézier Curves Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2013

2 Announcements Homework assignment 5 due tomorrow, Nov 8 at 1:30pm Late submissions for assignment 4 will be accepted 2

3 CSE 169: Computer Animation Most recent course web site is from 2009: PixelActive s CityScape: 3

4 CSE 190: Shader Programming Instructor: Wolfgang Engel, CEO and Co-Founder of Confetti Interactive Lecture topics: Introduction to DirectX 11.1 Compute Simple Compute Case Studies DirectCompute performance optimization Direct3D 11.1 Graphics Pipeline Physically Based Lighting Deferred Lighting, AA Shadows Order-Independent Transparency Global Illumination Algorithms in Games 4

5 Lecture Overview Polynomial Curves Introduction Polynomial functions Bézier Curves Introduction Drawing Bézier curves Piecewise Bézier curves 5

6 Modeling Creating 3D objects How to construct complex surfaces? Goal Specify objects with control points Objects should be visually pleasing (smooth) Start with curves, then generalize to surfaces Next: What can curves be used for? 6

7 Curves Surface of revolution 7

8 Curves Extruded/swept surfaces 8

9 Curves Animation Provide a track for objects Use as camera path 9

10 Video Bezier Curves 10

11 Curves Can be generalized to surface patches 11

12 Curve Representation Specify many points along a curve, connect with lines? Difficult to get precise, smooth results across magnification levels Large storage and CPU requirements How many points are enough? Specify a curve using a small number of control points Known as a spline curve or just spline Control point 12

13 Spline: Definition Wikipedia: Term comes from flexible spline devices used by shipbuilders and draftsmen to draw smooth shapes. Spline consists of a long strip fixed in position at a number of points that relaxes to form a smooth curve passing through those points. 13

14 Interpolating Control Points Interpolating means that curve goes through all control points Seems most intuitive Surprisingly, not usually the best choice Hard to predict behavior Hard to get aesthetically pleasing curves 14

15 Approximating Control Points Curve is influenced by control points Various types Most common: polynomial functions Bézier spline (our focus) B-spline (generalization of Bézier spline) NURBS (Non Uniform Rational Basis Spline): used in CAD tools 15

16 Mathematical Definition A vector valued function of one variable x(t) Given t, compute a 3D point x=(x,y,z) Could be interpreted as three functions: x(t), y(t), z(t) Parameter t moves a point along the curve x(t) z y x(0.0) x(0.5) x(1.0) x 16

17 Tangent Vector Derivative Vector x points in direction of movement Length corresponds to speed x(t) z y x (0.0) x (0.5) x (1.0) 17 x

18 Lecture Overview Polynomial Curves Introduction Polynomial functions Bézier Curves Introduction Drawing Bézier curves Piecewise Bézier curves 18

19 Polynomial Functions Linear: (1 st order) Quadratic: (2 nd order) Cubic: (3 rd order) 19

20 Polynomial Curves Linear Evaluated as: 20

21 Polynomial Curves Quadratic: (2 nd order) Cubic: (3 rd order) We usually define the curve for 0 t 1 21

22 Control Points Polynomial coefficients a, b, c, d can be interpreted as control points Remember: a, b, c, d have x,y,z components each Unfortunately, they do not intuitively describe the shape of the curve Goal: intuitive control points 22

23 Control Points How many control points? Two points define a line (1 st order) Three points define a quadratic curve (2 nd order) Four points define a cubic curve (3 rd order) k+1 points define a k-order curve Let s start with a line 23

24 First Order Curve Based on linear interpolation (LERP) Weighted average between two values Value could be a number, vector, color, Interpolate between points p 0 and p 1 with parameter t Defines a curve that is straight (first-order spline) t=0 corresponds to p 0 t=1 corresponds to p 1 t=0.5 corresponds to midpoint 24 p 0. 0<t<1 t=0. p 1 t=1 x(t) = Lerp( t, p 0, p 1 )= ( 1 t)p 0 + t p 1

25 Linear Interpolation Three equivalent ways to write it Expose different properties 1. Regroup for points p 2. Regroup for t 3. Matrix form 25

26 Weighted Average x(t) = (1 t)p 0 + (t)p 1 = B 0 (t) p 0 + B 1 (t)p 1, where B 0 (t) = 1 t and B 1 (t) = t Weights are a function of t Sum is always 1, for any value of t Also known as blending functions 26

27 Linear Polynomial x(t) = (p p 0 ) t + p vector point Curve is based at point p 0 Add the vector, scaled by t p 0..5(p 1 -p 0 ). p 1 -p 0. 27

28 Matrix Form Geometry matrix Geometric basis Polynomial basis In components 28

29 Tangent For a straight line, the tangent is constant Weighted average Polynomial Matrix form 29

30 Lecture Overview Polynomial Curves Introduction Polynomial functions Bézier Curves Introduction Drawing Bézier curves Piecewise Bézier curves 30

31 Bézier Curves Are a higher order extension of linear interpolation p 1 p 1 p 1 p 2 p 3 p 0 p 0 p 0 p 2 Linear Quadratic Cubic 31

32 Bézier Curves Give intuitive control over curve with control points Endpoints are interpolated, intermediate points are approximated Convex Hull property Many demo applets online, for example: Demo: ezier/bezier.html 32

33 Cubic Bézier Curve Most commonly used case Defined by four control points: Two interpolated endpoints (points are on the curve) Two points control the tangents at the endpoints Points x on curve defined as function of parameter t p 1 p 0 x(t) p 2 33 p 3

34 Algorithmic Construction Algorithmic construction De Casteljau algorithm, developed at Citroen in 1959, named after its inventor Paul de Casteljau (pronounced Cast-all- Joe ) Developed independently from Bézier s work: Bézier created the formulation using blending functions, Casteljau devised the recursive interpolation algorithm 34

35 De Casteljau Algorithm A recursive series of linear interpolations Works for any order Bezier function, not only cubic Not very efficient to evaluate Other forms more commonly used But: Gives intuition about the geometry Useful for subdivision 35

36 De Casteljau Algorithm Given: Four control points A value of t (here t 0.25) p 1 p 0 p 2 p 3 36

37 De Casteljau Algorithm p 1 q 1 q 0 (t) = Lerp( t,p 0,p ) 1 p 0 q 0 q 1 (t) = Lerp( t,p 1,p ) 2 p 2 q 2 (t) = Lerp( t,p 2,p ) 3 q 2 p 3 37

38 De Casteljau Algorithm q 0 r 0 q 1 r 0 (t) = Lerp( t,q 0 (t),q 1 (t)) r 1 (t) = Lerp( t,q 1 (t),q 2 (t)) r 1 q 2 38

39 De Casteljau Algorithm r 0 x r 1 x(t) = Lerp( t,r 0 (t),r 1 (t)) 39

40 De Casteljau Algorithm p 1 p 0 x p 2 Applets Demo: p 3

41 Recursive Linear Interpolation x = Lerp t,r 0,r 1 ( ) r = Lerp ( t,q,q ) ( ) r 1 = Lerp t,q 1,q 2 q 0 = Lerp( t,p 0,p ) 1 q 1 = Lerp( t,p 1,p ) 2 q 2 = Lerp( t,p 2,p ) 3 p 0 p 1 p 2 p 3 p 1 q 0 r 0 p 2 x q 1 r 1 p 3 q 2 p 4 41

42 Expand the LERPs q 0 (t) = Lerp( t,p 0,p 1 )= ( 1 t)p 0 + tp 1 q 1 (t) = Lerp( t,p 1,p 2 )= ( 1 t)p 1 + tp 2 q 2 (t) = Lerp( t,p 2,p 3 )= ( 1 t)p 2 + tp 3 r 0 (t) = Lerp( t,q 0 (t),q 1 (t))= 1 t r 1 (t) = Lerp( t,q 1 (t),q 2 (t))= 1 t ( ) ( )p 0 + tp 1 )+ t ( 1 t)p 1 + tp 2 ( )p 1 + tp 2 )+ t ( 1 t)p 2 + tp 3 ( ) 1 t ( ) 1 t x(t) = Lerp t,r 0 (t),r 1 (t) = ( 1 t) ( 1 t) ( 1 t)p 0 + tp 1 )+ t ( 1 t)p 1 + tp 2 +t ( 1 t) ( 1 t)p 1 + tp 2 )+ t ( 1 t)p 2 + tp 3 42 ( ) ( ) ( ) ( ) ( ) ( )

43 Weighted Average of Control Points Regroup for p: x(t) = 1 t ( )p 0 + tp 1 )+ t ( 1 t)p 1 + tp 2 ( )p 1 + tp 2 )+ t( 1 t)p 2 + tp 3 ) ( ) ( 1 t) 1 t +t 1 t ( ) ( ) ( ) 1 t ( ) x(t) = ( 1 t) 3 p 0 + 3( 1 t) 2 tp 1 + 3( 1 t)t 2 p 2 + t 3 p 3 B 0 (t ) B (t ) x(t) = t 3 + 3t 2 3t + 1 p 0 + 3t 3 6t 2 + 3t ( ) ( ) + 3t 3 + 3t p + t 3 2 { p 3 B 2 (t ) ( ) B 3 (t ) ( ) p 1 43

44 Cubic Bernstein Polynomials x(t) = B 0 ( t)p 0 + B 1 ( t)p 1 + B 2 ( t)p 2 + B 3 ( t)p 3 The cubic Bernstein polynomials : B 0 ( t)= t 3 + 3t 2 3t + 1 B 1 ( t)= 3t 3 6t 2 + 3t B 2 ( t)= 3t 3 + 3t 2 ( t)= t 3 B 3 B i (t) = 1 Weights B i (t) add up to 1 for any value of t 44

45 General Bernstein Polynomials B 1 0 B 1 1 ( t)= t + 1 B 2 0 ( t)= t 2 2t + 1 B 3 0 ( t)= t 3 + 3t 2 3t + 1 ( t)= t B 2 1 ( t)= 2t 2 + 2t B 3 1 ( t)= 3t 3 6t 2 + 3t B 2 2 ( t)= t 2 B 3 2 ( t)= 3t 3 + 3t 2 B 3 3 ( t)= t 3 45 B i n ( t)= n i ( 1 t)n i t B n i ( t) = 1 n ( ) i i = n! i! ( n i)! n! = factorial of n (n+1)! = n! x (n+1)

46 General Bézier Curves nth-order Bernstein polynomials form nth-order Bézier curves B i n ( t)= n i ( 1 t)n i t n i=0 x( t)= B n i ( t)p i ( ) i 46

47 Bézier Curve Properties Overview: Convex Hull property Affine Invariance 47

48 Definitions Convex hull of a set of points: Polyhedral volume created such that all lines connecting any two points lie completely inside it (or on its boundary) Convex combination of a set of points: Weighted average of the points, where all weights between 0 and 1, sum up to 1 Any convex combination of a set of points lies within the convex hull 48

49 Convex Hull Property A Bézier curve is a convex combination of the control points (by definition, see Bernstein polynomials) A Bézier curve is always inside the convex hull Makes curve predictable Allows culling, intersection testing, adaptive tessellation Demo: p 1 p 3 p 0 p 2 49

50 Affine Invariance Transforming Bézier curves Two ways to transform: Transform the control points, then compute resulting spline points Compute spline points, then transform them Either way, we get the same points Curve is defined via affine combination of points Invariant under affine transformations (i.e., translation, scale, rotation, shear) Convex hull property remains true 50

51 Cubic Polynomial Form Start with Bernstein form: x(t) = ( t 3 + 3t 2 3t + 1)p 0 + ( 3t 3 6t 2 + 3t)p 1 + ( 3t 3 + 3t 2 )p 2 + ( t 3 )p 3 Regroup into coefficients of t : x(t) = ( p 0 + 3p 1 3p 2 + p 3 )t 3 + ( 3p 0 6p 1 + 3p 2 )t 2 + ( 3p 0 + 3p 1 )t + ( p 0 )1 x(t) = at 3 + bt 2 + ct + d ( ) ( ) ( ) ( ) a = p 0 + 3p 1 3p 2 + p 3 b = 3p 0 6p 1 + 3p 2 c = 3p 0 + 3p 1 d = p 0 Good for fast evaluation Precompute constant coefficients (a,b,c,d) Not much geometric intuition 51

52 Cubic Matrix Form x(t) = a r t 3 r b c r t 2 d t 1 r a = p 0 + 3p 1 3p 2 + p 3 r b = ( 3p 0 6p 1 + 3p 2 ) r c = ( 3p 0 + 3p 1 ) d = p 0 ( ) ( ) t x(t) = [ p 0 p 1 p 2 p 3 ] t t { T G Bez B Bez Other types of cubic splines use different basis matrices B Bez 52

53 Cubic Matrix Form In 3D: 3 equations for x, y and z: 53 [ ] x x (t) = p 0 x p 1x p 2 x p 3x x y (t) = p 0 y p 1y p 2 y p 3y x z (t) = p 0z p 1z p 2z p 3z t t t t t t t t t

54 Matrix Form Bundle into a single matrix x(t) = t 3 p 0 x p 1x p 2 x p 3x p 0 y p 1y p 2 y p 3y t t p 0z p 1z p 2z p 3z x(t) = G Bez B Bez T x(t) = C T Efficient evaluation Pre-compute C Take advantage of existing 4x4 matrix hardware support 54

55 Lecture Overview Polynomial Curves Introduction Polynomial functions Bézier Curves Introduction Drawing Bézier curves Piecewise Bézier curves 55

56 Drawing Bézier Curves Draw line segments or individual pixels Approximate the curve as a series of line segments (tessellation) Uniform sampling Adaptive sampling Recursive subdivision 56

57 Uniform Sampling Approximate curve with N straight segments N chosen in advance Evaluate x i = x( t i ) where t i = i N for i = 0, 1,K, N Connect the points with lines Too few points? Poor approximation Curve is faceted Too many points? x i = a r i3 N + b r i2 3 N + c r i 2 N + d Slow to draw too many line segments Segments may draw on top of each other x(t) x 0 x 1 x 2 x 3 x4 57

58 Adaptive Sampling Use only as many line segments as you need Fewer segments where curve is mostly flat More segments where curve bends Segments never smaller than a pixel x(t) 58

59 Recursive Subdivision Any cubic curve segment can be expressed as a Bézier curve Any piece of a cubic curve is itself a cubic curve Therefore: Any Bézier curve can be broken down into smaller Bézier curves 59

60 De Casteljau Subdivision p 1 p 0 q r 0 0 r x 1 p 2 De Casteljau construction points are the control points of two Bézier sub-segments q 2 p 3 60

61 Adaptive Subdivision Algorithm Use De Casteljau construction to split Bézier segment in half For each half If flat enough : draw line segment Else: recurse Curve is flat enough if hull is flat enough Test how far the approximating control points are from a straight segment If less than one pixel, the hull is flat enough 61

62 Drawing Bézier Curves With OpenGL Indirect OpenGL support for drawing curves: Define evaluator map (glmap) Draw line strip by evaluating map (glevalcoord) Optimize by pre-computing coordinate grid (glmapgrid and glevalmesh) More details about OpenGL implementation: /opengl_nurbs.pdf 62

63 Lecture Overview Polynomial Curves Introduction Polynomial functions Bézier Curves Introduction Drawing Bézier curves Piecewise Bézier curves 63

64 More Control Points Cubic Bézier curve limited to 4 control points Cubic curve can only have one inflection (point where curve changes direction of bending) Need more control points for more complex curves k-1 order Bézier curve with k control points Hard to control and hard to work with Intermediate points don t have obvious effect on shape Changing any control point changes the whole curve Want local support: each control point only influences nearby portion of curve 64

65 Piecewise Curves Sequence of line segments Piecewise linear curve Sequence of simple (low-order) curves, end-to-end Known as a piecewise polynomial curve Sequence of cubic curve segments Piecewise cubic curve (here piecewise Bézier) 65

66 Parametric Continuity C 0 continuity: Curve segments are connected C 1 continuity: C 0 & 1st-order derivatives agree Curves have same tangents Relevant for smooth shading C 2 continuity: C 1 & 2nd-order derivatives agree Curves have same tangents and curvature Relevant for high quality reflections