2D Spline Curves - Duke University
Transcript of 2D Spline Curves - Duke University
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 1
2D Spline Curves
CS 465 Lecture 15
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 2
Motivation: smoothness
• In many applications we need smooth shapes – that is, without discontinuities
• So far we can make – things with corners (lines, squares, rectangles, …)
– circles and ellipses (only get you so far!)
[Boe
ing]
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 3
Classical approach
• Pencil-and-paper draftsmen also needed smooth curves
• Origin of “spline:” strip of flexible metal – held in place by pegs or weights to constrain shape – traced to produce smooth contour
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Translating into usable math
• Smoothness – in drafting spline, comes from physical curvature
minimization
– in CG spline, comes from choosing smooth functions
• usually low-order polynomials
• Control – in drafting spline, comes from fixed pegs
– in CG spline, comes from user-specified control points
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 5
Defining spline curves
• At the most general they are parametric curves
• Generally f(t) is a piecewise polynomial – for this lecture, the discontinuities are at the integers
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 6
Defining spline curves
• Generally f(t) is a piecewise polynomial – for this lecture, the discontinuities are at the integers – e.g., a cubic spline has the following form over [k, k + 1]:
– Coefficients are different for every interval
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Control of spline curves
• Specified by a sequence of control points
• Shape is guided by control points (aka control polygon) – interpolating: passes through points – approximating: merely guided by points
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 8
Splines as reconstruction
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Trivial example: piecewise linear
• This spline is just a polygon – control points are the vertices
• But we can derive it anyway as an illustration • Each interval will be a linear function
– x(t) = at + b – constraints are values at endpoints
– b = x0 ; a = x1 – x0 – this is linear interpolation
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 10
Trivial example: piecewise linear
• Vector formulation
• Matrix formulation
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 11
Trivial example: piecewise linear
• Basis function formulation – regroup expression by p rather than t
– interpretation in matrix viewpoint
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 12
Trivial example: piecewise linear
• Vector blending formulation: “average of points” – weighting functions: contribution of each point as t changes
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 13
Trivial example: piecewise linear
• Basis function formulation: “function times point” – basis functions: contribution of each point as t changes
– this is just like a reconstruction filter!
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Hermite splines
• Less trivial example
• Form of curve: piecewise cubic • Constraints: endpoints and tangents (derivatives)
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 15
Hermite splines
• Solve constraints to find coefficients
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Hermite splines
• Matrix form is much simpler
– cofficients = rows – basis functions = columns
• note p columns sum to [0 0 0 1]T
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 17
Hermite splines
• Hermite blending functions
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Hermite splines
• Hermite basis functions
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Continuity
• Smoothness can be described by degree of continuity – zero-order: position matches from both sides – first-order: tangent matches from both sides
– second-order: curvature matches from both sides – Gn vs. Cn
zero order first order second order
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Control
• Local control – changing control point only affects a limited part of spline – without this, splines are very difficult to use
– many likely formulations lack this • natural spline
• polynomial fits
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Control
• Convex hull property – convex hull = smallest convex region containing points
• think of a rubber band around some pins
– some splines stay inside convex hull of control points • make clipping, culling, picking, etc. simpler
YES YES YES NO
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Affine invariance
• Transforming the control points is the same as transforming the curve – true for all commonly used splines
– extremely convenient in practice…
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Matrix form of spline
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 24
Hermite splines
• Constraints are endpoints���
and endpoint tangents
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Hermite basis
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Affine invariance
• Basis functions associated with points should always sum to 1
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Hermite to Bézier
• Mixture of points and vectors is awkward
• Specify tangents as differences of points
– note derivative is defined as 3 times offset
• reason is illustrated by linear case
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Hermite to Bézier
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Bézier matrix
– note that these are the Bernstein polynomials
C(n,k) tk (1 – t)n – k���
and that defines Bézier curves for any degree
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 30
Bézier basis
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 31
Convex hull
• If basis functions are all positive, the spline has the convex hull property – we’re still requiring them to sum to 1
– if any basis function is ever negative, no convex hull prop.
• proof: take the other three points at the same place
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 32
Chaining spline segments
• Hermite curves are convenient because they can be made long easily
• Bézier curves are convenient because their controls are all points and they have nice properties – and they interpolate every 4th point, which is a little odd
• We derived Bézier from Hermite by defining tangents from control points – a similar construction leads to the interpolating Catmull-Rom
spline
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Chaining Bézier splines
• No continuity built in
• Achieve C1 using collinear control points
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Subdivision
• A Bézier spline segment can be split into a two-segment curve:
– de Casteljau’s algorithm
– also works for arbitrary t
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Cubic Bézier splines
• Very widely used type, especially in 2D – e.g. it is a primitive in PostScript/PDF
• Can represent C1 and/or G1 curves with corners • Can easily add points at any position
• Illustrator demo
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• Have not yet seen any interpolating splines
• Would like to define tangents automatically – use adjacent control points
– end tangents: extra points or zero
Hermite to Catmull-Rom
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Hermite to Catmull-Rom
• Tangents are (pk + 1 – pk – 1) / 2 – scaling based on same argument about collinear case
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Catmull-Rom basis
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Catmull-Rom splines
• Our first example of an interpolating spline
• Like Bézier, equivalent to Hermite – in fact, all splines of this form are equivalent
• First example of a spline based on just a control point sequence
• Does not have convex hull property
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 40
Evaluating splines for display
• Need to generate a list of line segments to draw – generate efficiently – use as few as possible
– guarantee approximation accuracy
• Approaches – recursive subdivision (easy to do adaptively) – uniform sampling (easy to do efficiently)
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Evaluating by subdivision
– Recursively split spline
• stop when polygon is ���within epsilon of curve
– Termination criteria
• distance between control points • distance of control points from line
p1
p2
p3
p4
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Evaluating with uniform spacing
• Forward differencing – efficiently generate points for uniformly spaced t values – evaluate polynomials using repeated differences
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 43
B-splines
• We may want more continuity than C1
• We may not need an interpolating spline • B-splines are a clean, flexible way of making long
splines with arbitrary order of continuity
• Various ways to think of construction – a simple one is convolution
– relationship to sampling and reconstruction
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 44
Cubic B-spline basis
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Deriving the B-Spline
• Approached from a different tack than Hermite-style constraints – Want a cubic spline; therefore 4 active control points
– Want C2 continuity – Turns out that is enough to determine everything
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Cornell CS465 Fall 2004 • Lecture 15 © 2004 Steve Marschner • 46
Efficient construction of any B-spline
• B-splines defined for all orders – order d: degree d – 1 – order d: d points contribute to value
• One definition: Cox-deBoor recurrence
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B-spline construction, alternate view
• Recurrence – ramp up/down
• Convolution – smoothing of basis fn
– smoothing of curve
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Cubic B-spline matrix
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Other types of B-splines
• Nonuniform B-splines – discontinuities not evenly spaced – allows control over continuity or interpolation at certain
points
– e.g. interpolate endpoints (commonly used case)
• Nonuniform Rational B-splines (NURBS) – ratios of nonuniform B-splines: x(t) / w(t); y(t) / w(t)
– key properties:
• invariance under perspective as well as affine • ability to represent conic sections exactly
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Converting spline representations
• All the splines we have seen so far are equivalent – all represented by geometry matrices
• where S represents the type of spline
– therefore the control points may be transformed from one type to another using matrix multiplication