andrew nealen tu berlin takeo igarashi the university of tokyo / presto jst olga sorkine
DESCRIPTION
Laplacian Mesh Optimization. Andrew Nealen TU Berlin Takeo Igarashi The University of Tokyo / PRESTO JST Olga Sorkine Marc Alexa TU Berlin. What is it ?. Overview. Motivation Problem formulation Laplacian mesh processing basics Laplacian mesh optimization framework Applications - PowerPoint PPT PresentationTRANSCRIPT
Andrew Nealen, TU Berlin, 2006 1
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Andrew NealenTU Berlin
Takeo IgarashiThe University of Tokyo / PRESTO JST
Olga SorkineMarc Alexa
TU Berlin
Laplacian Mesh Optimization
Laplacian Mesh Optimization
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What is it ?
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Overview
Motivation• Problem formulation• Laplacian mesh processing basics
Laplacian mesh optimization framework Applications
• Triangle shape optimization• Mesh smoothing
Discussion
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Motivation
Local detail preserving triangle optimization• A Sketch-Based Interface for Detail Preserving
Mesh Editing [Nealen et al. 2005]
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Motivation
Local detail preserving triangle optimization• A Sketch-Based Interface for Detail Preserving
Mesh Editing [Nealen et al. 2005]
Can we perform global optimization this way ?
=L x
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Laplacian Mesh Processing
Discrete Laplacians
=L x
n
cotangent : wij = cot ij + cot ij
uniform : wij = 1
( , )( , )
1i i ij j
i j Eiji j E
ww
δ x x
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Laplacian Mesh Processing
Surface reconstructionn
cotangent : wij = cot ij + cot ij
( , )( , )
1i i ij j
i j Eiji j E
ww
δ x x
uniform : wij = 1
=L x
L
L
y
z
x
z
y
x
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Laplacian Mesh Processing
Surface reconstructionn
z
y
x
y
z
x
=L
L
L
c1
fixedit
c2
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Laplacian Mesh Processing
Least-squares solutionn
z
y
x
y
z
x
=L
L
L
c1
fixedit
c2
w1 w1
w2 w2
wLi wLi
A x = bATA x = bAT
(ATA)-1x = bAT
Normal Equations
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Laplacian Mesh Processing
Tangential smoothingn
z
y
x
y
z
x
=L
L
L
fixc1
L
L
L
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L
L
L
Laplacian Mesh Processing
Tangential smoothingn
z
y
x
y
z
x
=
fixc1
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L
L
L
Laplacian Mesh Processing
Tangential smoothingn
z
y
x
y
z
x
=
fixc1
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More motivation…
So: can we use such a system for globaloptimization ?
=L x
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Our Solution
All vertices are (weighted) anchors
Preserves global shape Uses existing LS framework Anchor + Laplacian weights determine
result
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Framework
Detail preserving tri shape optimization for L = Luni and f = cot(similar to local optimization)
Mesh smoothing L = Lcot (outer fairness) or L = Luni (outer and inner fairness) and f = 0
=L x fWL WL
pWP WP
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Tri Shape Optimization
Detail preserving tri shape optimization
=Luni x
pWP WP
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Positional Weights
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Constant Weights
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Linear Weights
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CDF Weights
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CDF Weights
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Sharp Features
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Sharp Features
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Sharp Features
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Mesh Smoothing
Mesh smoothing L = Lcot (outer fairness) or L = Lumb (outer and inner fairness) and f = 0
Controlled by WP and WL (Intensity, Features) Similar to Least-Squares Meshes [Sorkine et al. 04]
=L x 0WL WL
pWP WP
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Using WP
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Using WP and WL
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Results
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Noisy
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Smoothed
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Original
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Tri Shape Optimization
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Smoothing Outer and Inner Fairness (Lumb)
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Original
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Tri Shape Optimization
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SmoothingOuter Fairness only (Lcot)
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Discussion
The good...• Easily controllable tri shape optimization and
smoothing• Leverages existing least squares framework• Can replace tangential smoothing step for
general remeshers ... and the not so good
• Euclidean distance is not Hausdorff distance, so error control is indirect
• Does rely on some (user) parameter tweaking
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Thank you !
Contact info
Andrew [email protected]
Takeo [email protected]
Olga [email protected]
Marc [email protected]