Surface Parameterization Christian Rössl INRIA Sophia-Antipolis
Outline • Motivation • Objectives and Discrete Mappings • Angle Preservation • Discrete Harmonic Maps • Discrete Conformal Maps • Angle Based Flattening • Reducing Area Distortion • Alternative Domains 237 Christian Rössl, INRIA
Surface Parameterization [www.wikipedia.de] 238 Christian Rössl, INRIA
Surface Parameterization [www.wikipedia.de] 239 Christian Rössl, INRIA
Surface Parameterization 240 Christian Rössl, INRIA
Motivation • Texture mapping Lévy, Petitjean, Ray, and Maillot: Least squares conformal maps for automatic texture atlas generation , SIGGRAPH 2002 241 Christian Rössl, INRIA
Motivation • Many operations are simpler on planar domain Lévy: Dual Domain Exrapolation , SIGGRAPH 2003 242 Christian Rössl, INRIA
Motivation • Exploit regular structure in domain Gu, Gortler, Hoppe: Geometry Images , SIGGRAPH 2002 243 Christian Rössl, INRIA
Surface Parameterization 244 Christian Rössl, INRIA
Surface Parameterization f X U Jacobian 245 Christian Rössl, INRIA
Surface Parameterization f X U d X = J d U 246 Christian Rössl, INRIA
Surface Parameterization f X U d X = J d U || d X || 2 = d U J T J d U First Fundamental Form { 247 Christian Rössl, INRIA
Characterization of Mappings • By first fundamental form I – Eigenvalues λ 1,2 of I – Singular values σ 1,2 of J ( σ i2 = λ i ) • Isometric – I = Id , λ 1 = λ 2 = 1 • Conformal angle preserving – I = µ Id , λ 1 / λ 2 = 1 • Equiareal area preserving – det I = 1, λ 1 λ 2 = 1 248 Christian Rössl, INRIA
Piecewise Linear Maps • Mapping = 2D mesh with same connectivity f X U 249 Christian Rössl, INRIA
Objectives • Isometric maps are rare • Minimize distortion w.r.t. a certain measure – Validity (bijective map) triangle flip – Boundary fixed / free? – Domain e.g.,spherical – Numerical solution linear / non-linear? 250 Christian Rössl, INRIA
Discrete Harmonic Maps • f is harmonic if • Solve Laplace equation u and v are harmonic Dirichlet boundary conditions • In 3D: "fix planar boundary and smooth" 251 Christian Rössl, INRIA
Discrete Harmonic Maps • f is harmonic if • Solve Laplace equation • Yields linear system (again) • Convex combination maps – Normalization – Positivity 252 Christian Rössl, INRIA
Convex Combination Maps • Every (interior) planar vertex is a convex combination of its neighbors • Guarantees validity if boundary is mapped to a convex polygon (e.g., rectangle, circle) • Weights – Uniform (barycentric mapping) – Shape preserving [Floater 1997] Reproduction of planar meshes – Mean Value Coordinates [Floater 2003] • Use mean value property of harmonic functions 253 Christian Rössl, INRIA
Conformal Maps • Planar conformal mappings satisfy the Cauchy-Riemann conditions and 254 Christian Rössl, INRIA
Conformal Maps • Planar conformal mappings satisfy the Cauchy-Riemann conditions and • Differentiating once more by x and y yields ⇒ and and similar • conformal ⇒ harmonic 255 Christian Rössl, INRIA
Discrete Conformal Maps • Planar conformal mappings satisfy the Cauchy-Riemann conditions and • In general, there are no conformal mappings for piecewise linear functions! 256 Christian Rössl, INRIA
Discrete Conformal Maps • Planar conformal mappings satisfy the Cauchy-Riemann conditions and • Conformal energy (per triangle T ) • Minimize → 257 Christian Rössl, INRIA
Discrete Conformal Maps • Least-squares conformal maps [Lévy et al. 2002] → where • Satisfy Cauchy-Riemann conditions in least-squares sense • Leads to solution of linear system • Alternative formulation leads to same solution… 258 Christian Rössl, INRIA
Discrete Conformal Maps • Same solution is obtained for cotangent weights Neumann boundary conditions + fixed vertices [Desbrun et al. 2002] Discrete Conformal Maps 259 Christian Rössl, INRIA
Discrete Conformal Maps 260 Christian Rössl, INRIA
Discrete Conformal Maps • Free boundary depends on choice of fixed vertices (>1) ABF 261 Christian Rössl, INRIA
Angle Based Flattening [Sheffer&de Sturler 2000] • Perserve angles specify problem in angles – Constraints ensure validity • triangle • Internal vertex • Wheel consistency – Objective function preserve angles 2D ~3D "optimal" angles (uniform scaling) 262 Christian Rössl, INRIA
Angle Based Flattening • Free boundary • Validity: no local self-intersections • Non-linear optimization 263 Christian Rössl, INRIA
Angle Based Flattening • Free boundary • Non-linear optimization – Newton iteration – Solve linear system in every step [Zayer et al. 2005] 264 Christian Rössl, INRIA
And how about area distortion? 265 Christian Rössl, INRIA
Reducing Area Distortion • Energy minimization based on – MIPS [Hormann & Greiner 2000] – modification [Degener et al. 2003] – "Stretch" [Sander et al. 2001] or – modification [Sorkine et al. 2002] 266 Christian Rössl, INRIA
Non-Linear Methods • Free boundary • Direct control over distortion • No convergence guarantees • May get stuck in local minima • May not be suitable for large problems • May need feasible point as initial guess • May require hierarchical optimization even for moderately sized data sets 267 Christian Rössl, INRIA
Linear Methods • Efficient solution of a sparse linear system • Guaranteed convergence • Fixed convex boundary • May suffer from area distortion for complex meshes • An alternative approach to reducing area distortion… – How accurately can we reproduce a surface on the plane? – How do we characterize the mapping? 268 Christian Rössl, INRIA
Reducing Area Distortion isometry 269 Christian Rössl, INRIA
Reducing Area Distortion • Quasi-harmonic maps [Zayer et al. 2005] estimate from f • Iterate (few iterations) – Determine tensor C from f – Solve for g 270 Christian Rössl, INRIA
Examples 271 Christian Rössl, INRIA
Examples → Stretch metric minimization Using [Yoshizawa et. al 2004] 272 Christian Rössl, INRIA
Reducing Area Distortion • Introduce cuts area distortion vs. continuity • Often cuts are unavoidable (e.g., open sphere) Treatment of boundary is important! 273 Christian Rössl, INRIA
Reducing Area Distortion • Solve Poisson system [Zayer et al. 2005] * estimate from previous map * Similar setting used in mesh editing 274 Christian Rössl, INRIA
Spherical Parameterization • Sphere is natural domain for genus-0 surfaces • Additional constraint • Naïve approach – Laplacian smoothing and back-projection – Obtain minimum for degenerate configuration 275 Christian Rössl, INRIA
Spherical Parameterization • (Tangential) Laplacian Smoothing and back-projection – Minimum energy is obtained for degenerate solution • Theoretical guarantees are expensive – [Gotsman et al. 2003] • A compromise?! – Stereographic projection – Smoothing in curvilinear coordinates 276 Christian Rössl, INRIA
Arbitrary Topology • Piecewise linear domains – Base mesh obtained by mesh decimation – Piecewise maps – Smoothness 277 Christian Rössl, INRIA
Literature • Floater & Hormann: Surface parameterization: a tutorial and survey, Springer, 2005 • Lévy, Petitjean, Ray, and Maillot: Least squares conformal maps for automatic texture atlas generation , SIGGRAPH 2002 • Desbrun, Meyer, and Alliez: Intrinsic parameterizations of surface meshes , Eurographics 2002 • Sheffer & de Sturler: Parameterization of faceted surfaces for meshing using angle based flattening , Engineering with Computers, 2000. 278 Christian Rössl, INRIA
Recommend
More recommend
