Barycentric Coordinates Interpolation Barycentric given data at - - PDF document

barycentric coordinates
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Barycentric Coordinates Interpolation Barycentric given data at - - PDF document

Barycentric Coordinates Interpolation Barycentric given data at sites, interpolate Coordinates smoothly and intuitively in between easy over simplices: linear easy over simplices: linear more general shapes needed


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SLIDE 1

Barycentric Coordinates

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Barycentric Coordinates

Interpolation

 given data at sites, interpolate

smoothly and intuitively “in between”

 easy over simplices: linear

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 easy over simplices: linear  more general shapes needed

 morphing, shape deformation, attribute

interpolation, physical modeling, and

  • n and on and on

Basic Principles

Data on boundary; extend

 as affine combination:

if then

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 desirables

 constant precision:  linear precision:

 convex: many choices; concave: few…

Basic Setup: Cage

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 from now on, we’ll focus on

Weber/Ben-Chen/Gotsman’s method

Planar Case

Treat everything in complex plane

 tools from complex analysis…  given source and target polygon

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 desirables:

Properties

Complex barycentric interpolation

 preserve similarities  distinction with real coeffs?

ffi f i iff

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 preserve affine transformation iff  not actually desirable… why?

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SLIDE 2

Real vs. Complex

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 Affine transform not quite pleasing…

Trade-off

Must give up something…

 not interpolating anymore

How to find such functions?

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 study continuous setting

Not An Interpolation

Visualize one coordinate function

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Cauchy Formula

Holomorphic functions

 Cauchy kernel:

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 integral version of mean value theorem

 recover value from average of boundary

 Cauchy coordinates:

Only need data on boundary!

 apply to polygon

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 define f linearly along edge  grind out integrals…

Resulting Formulas

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SLIDE 3

Examples

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Properties

Best holomorphic function?

 it doesn’t interpolate; is it “best”?

 closest to given boundary data

ti k ith C b t “ i t l” l

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 stick with Cj but use “virtual” poly.

 minimize functional to find best poly.

Szegö Coordinates

Optimize “fit”

 need Cj on boundary…

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 define through limit  do point collocation (sample boundary)

matrix with sampled Cj as columns LSQ problem

Solution

Pseudo inverse

 size is number of vertices (small)

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 letting H be sampling operator:

Example

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Visualization

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SLIDE 4

Comparison

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Another Comparison

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Cauchy-Green vs Szego

Visualization

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Point-to-Point

Simplify UI

 just specify landmarks  underconstrain (typically)

dd

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 add fairness constraint

P2P Coordinates

Joint minimization

 point collocation…

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Example

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SLIDE 5

Visualization

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Example

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Video

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