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Intrinsic Parameterization for Surface Meshes
Mathieu Desbrun, Mark Meyer, Pierre Alliez
CS598MJG Presented by Wei-Wen Feng
2004/10/5
What’s Parameterization?
Find a mapping between original surface and a target domain ( Planar in general )
What does it do?
Most significant : Texture Mapping Other applications include remeshing, morphing, etc.
Two Directions in Research
Define metric (energy) measuring distortion Minimize the energy to find mapping This paper’s main contribution
Two Directions in Research
Using the metric, and make it work on mesh Cut mesh into patches Considering arbitrary genus
Previous Work Discrete Harmonic Map (Eck. 95):
Minimize Eharm[h] = ½ ΣKi,j |h(i) – h(j)|2
K : Spring constant The same as minimize Dirichlet energy
Previous Work Shape Preserving Param. (Floater. 97):
Represent vertex as convex combination of neigobors Trivial choice : barycenter of neighbors Ensure valid embedding
Previous Work
Most Isometric Param. (MIPS) (K. Hormann . 99): Doesn’t need to fix boundary Conformal but need to minimize non-linear energ
y
MIPS Harmonic Map
Previous Work
Signal Specialized Param. (Sander. 02): Minimize signal stre
tch on the surface when reconstruct from parametrization
Intrinsic Parameterization
Motivation: Find good distortion measure only depending on
the intrinsic properties of mesh
Develop good tools for fast parameterization design
Intrinsic Properties
Defined at discrete suraface, restricted at 1-ring Notion:
Return the “score” of surface patch M E(M,U) : Distortion between mapping
Intrinsic Properties: Rotation & Translation Invariance Continuity : Converge to continuous surface Additivity : (A) + (B) = (AB) + (AB)
Intrinsic Properties
Minkowski Functional A = Area = Euler characteristic P = Perimeter
From Hadwiger, the only admissible intrinsic functional is : aA + b+ c P
Discrete Conformal Param.
0))(cot(cot)(
iNj
jiijiji
AE uuu
Measure of Area (Dirichlet Energy)
Conformality is attained when Dirichlet energy is minimum When fixed boundary, it is in fact discrete harmonic map
Discrete Authalic Param.
Measure of Euler characteristic (Angle) Integral of Gaussian curvature Derived as Chi Energy
0))(()(
2||
cotcot
iNj
jii
ji
ijijEuu
u xx
Comparing DCP & DAP
DCP (Dirichlet Energy) Measure area extension Minimized when angles preserved
DAP (Chi Energy) Measure angle excess Minimized when area preserved
Solving Parametrization
General distortion measure :
Fix the boundary, minimized the energy : Very sparse linear systems Conjugate gradient
EEE A
Natural Boundary
Instead fixed the boundary, solve for optimal conformal mapping which yields “best” boundary.
For interior points
For boundary points :
Constrain two points to avoid degeneracy.
A DΕ
0D Ε
Compare with LSCM
Least Square Conformal Map (Levy. ’02) Start from Cauchy-Riemann Equation Theoretically equivalent to Natural Boundary Map Minimize conformal energy
Natural Conformal Map Imposing boundary constraint for boundary points
AC DΕΕ
A DΕ
Extend to non-linear func.
All parametrization could be expressed as : U = UA + (1-) U
Substitute U in a non-linear function reduces the problem into solving
Ex :
Could be reduced into root finding
Boundary Control
Precompute the “impulse response” parameterization for each boundary points
New parameterization could be obtained by projecting boundary parameter onto its “impulse response” parameterization
Boundary Optimization
Minimized arbitrary energy with respect to boundary parameterization
Using precomputed gradient to accelerate optimization
Summary of Contributions
A linear system solution for Natural Conformal Map
A new geometric metric for parameterization (DAP)
Real-time boundary control for better parameterization design
What’s Next ?
Mean Value Coordinate (Floater. 03) The same property of convex combination Approximating Harmonic Map but ensure a valid
embedding
Tutte Harmonic Shape Preserving
Mean Value
What’s Next ?
Spherical Parameterization (Praun. 03) Smooth parameterization for genus-0 model Using existing metric
Conclusion
There seems to be less paper directly about finding metrics (or find a better way to model them) for parameterization.
Now more efforts in finding globally smooth parameterization on arbitrary meshes
References (Eck. 95) Multiresolution Analysis of Arbitrary Meshes.
Proceedings of SIGGRAPH 95\
(Floater. 97) Parametrization and Smooth Approximationof Surface Triangulations. Computer Aided Geometric Design 14, 3 (1997)
(K. Hormann . 99) MIPS: An Efficient Global Parametrization Method. In Curve and Surface Design: Saint-Malo 1999 (2000)
(Sander. 02) Signal-Specialized Parameterization. In Eurographics Workshop on Rendering, 2002.