pde based geometric modeling and interactive sculpting
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PDE-based Geometric Modeling and Interactive Sculpting for - PowerPoint PPT Presentation

PDE-based Geometric Modeling and Interactive Sculpting for Graphics Hong Qin Center for Visual Computing Department of Computer Science SUNY at Stony Brook Geometric Modeling Shape representations Geometric modeling techniques Geometric


  1. Level Set Method • Originally defined for front propagation φ − ∇ φ = F F 0 , is the speed function; t φ = = ± Γ x t d x d x x ( , 0 ) ( ), ( ) is the distance from to ; 0 Γ = φ = x x t Moving front is the zero - level set : { | ( , ) 0 }. t • Prior work of level set method – Front propagation [Osher and Sethian88], [Adalsteinsson and Sethian95] – Shape reconstruction [Zhao et al. 00], [Zhao et al. 01] – Shape transformation [Breen and Whitaker01] – Shape modeling and editing [Barentzen and Christensen02], [Museth et al. 02] [SIGGRAPH’02 Course Notes 10] ……

  2. Classification of PDEs ∂ ∂ ∂ ∂ ∂ 2 2 2 u u u u u + + + + + = A B C D E Fu G 2 ∂ ∂ ∂ ∂ ∂ ∂ 2 2 x y x y x y • 3 types of PDEs based on characteristics – B²-AC> 0: hyperbolic ∂ ∂ 2 2 u u • Wave equation = 2 v ∂ ∂ 2 2 t x – B²-AC= 0: parabolic ∂ ∂ ∂ u ⎛ u ⎞ = ⎜ D ⎟ • Diffusion equation ∂ ∂ ∂ t x ⎝ x ⎠ – B²-AC< 0: elliptic ∂ ∂ 2 2 u u ( ) + = ρ x y , • Poisson equation ∂ ∂ 2 2 x y

  3. Shape Reconstruction Using Level Set Method [Zhao et al. 00]

  4. Shape Morphing Using Level Set Method [Breen and Whitaker 01]

  5. Diffusion Equations • Reaction-diffusion textures [Witkin and Kass91], [Turk91] • Tensor field visualization [Kindlmann et al. 00] • Vector field visualization [Diewald et al. 00] • Image processing Image enhancement, filtering,……

  6. Reaction-Diffusion Texture • Synthesizing natural textures • Reaction-diffusion system – Diffusion of morphogens – Nonlinear PDEs – Biological patterns

  7. Reaction-Diffusion Equation • Diffusion, dissipation, reaction • Reaction-diffusion equation: & = ∇ − + C a C bC R 2 2 , & C C is the time derivative of , ∂ ∂ C C 2 2 ∇ = + 2 C , ∂ ∂ x 2 y 2 a is the rate constant for diffusion, b is the rate constant for dissipatio n, R is the reaction function.

  8. Reaction-diffusion texture buttons 1.reptile, giraffe, coral, scalloped. 2.spiral, triweave, twisty maze, repli- cation, purple thing 3.sand, maze, zebra haunch, radial 4.space giraffe, zebra, stucco, beats us, weave By type : Isotropic, multi-orientation, and diffusion mapped [Witkin and Kass 91]

  9. Vector Field Visualization Different time steps of the anisotropic diffusion for both principal curvature directions [Diewald et al. 00]

  10. Other Applications of PDEs • Modeling fracture [O’Brien and Hodgins99], [O’Brien00] • Simulating gas [Foster and Metaxas97b] • Water simulation [Kass and Miller90], [Foster and Metaxas96,97a], [Stam99] • Modeling explosion [Yngve et al. 00] • Image processing [Bertalmio et al. 00], [Pérez et al. 03] …….

  11. Implicit Model • Implicit surfaces and solids: {( x,y,z )| f(x,y,z)=c }, {( x,y,z )| f(x,y,z) ≤ c } • Techniques to model implicit objects – Particle based implicit surface sculpting [Witkin and Heckbert 94] – Trivariate B-splines for implicit models [Raviv and Elber 99], [Hua and Qin01,02] – Level set method [Zhao et al. 00,01] – Variational implicit functions [Turk and O’Brien 99,02], [Morse et al. 01] …….

  12. [Turk and O’Brien02] Example of Implicit Models

  13. Physics-based Modeling • Combines physical properties with geometric models • Leads to deformable models • Allows direct manipulation of objects via forces • Creates natural-looking motions through simulation • Can be integrated with general PDE framework

  14. Deformable Models • Controlled by Lagrangian equations of motion ∂ ∂ ∂ δε ⎛ ⎞ ( ) r r r ( ) μ + γ + = t ⎜ ⎟ , f r ∂ ∂ ∂ ∂ ⎝ ⎠ t t t r – r ( a , t ): position of particle a at time t – μ ( a ): mass density of the body at a – γ ( a ): damping density – f ( r, t ): external force – ε ( r ): measures the potential energy of the elastic deformation of the body

  15. Discretized Mass-Spring Model + + = & & & M p D p Kp f • M : mass matrix • D : damping matrix • K : stiffness matrix • f : external force • p : discrete sample points

  16. Applications of Physics-based Modeling • Dynamics NURBS (DNURBS) [Qin and Terzopoulos 94, 95, 96] • Physics-based subdivision [Mandal et al. 98, 99], [McDonnell et al. 00, 01] • Cloth simulation and animation [Carignan et al. 92], [Baraff and Witkin 98] • Facial simulation [Lee et al. 95], [Koch et al. 96] • Physics-based implicit functions [Jing and Qin 01,02] ……

  17. Medial Axis Extraction • Locus of all centers of circles/spheres inside the object • Collection of points with more than one closest points on the boundary • Set of singularities of signed distance function from boundary

  18. Medial Axis Extraction Techniques • Thinning [Arcelli and Baja85][Lee and kashyap94][Manzanera etal. 99] • Distance functions [Arcelli and Baja92][Leymarie and Levine92][Bitter et al. 01] • Voronoi skeletons [Goldak et al. 91][Ognievicz93][Amenta et al. 01] • Level set method [Kimmel et al. 95][Ma et al. 03] • Direction testing [Bloomenthal and Lim99] • Hybrid techniques [Bouix and Siddiqi00]

  19. Outline • Motivation and contributions • Related work • PDE-based geometric modeling system – Physics-based PDE surfaces/displacements – PDE-based arbitrary mesh modeling – Implicit elliptic PDE model – PDE-based free-form modeling and deformation • Conclusion

  20. Outline • Motivation and contributions • Background review • PDE-based geometric modeling system – Physics-based PDE surfaces/displacements – PDE-based arbitrary mesh modeling – Implicit elliptic PDE model – PDE-based free-form modeling and deformation • Conclusion

  21. System Outline PDE-based Geometric Modeling System Physics- Arbitrary Free-Form Implicit based PDE PDE PDE Modeling PDE Model Surfaces Meshes with Intensity Iso-Surface Intensity Extraction Distribution Boundary Scattered Surface Datasets Sculpting Embedded Datasets

  22. PDE Surfaces • PDE surface formulation: 2 ⎛ ⎞ ∂ ∂ 2 2 ( ) ( ) ⎜ ⎟ + = a 2 u v u v , , 0 X ⎜ ⎟ ∂ ∂ u 2 v 2 ⎝ ⎠ ) [ ] ( = T u v x u v y u v z u v , ( , ) ( , ) ( , ) X – a(u,v) : blending coefficient function controlling the contributions of the parametric directions

  23. PDE Surface Displacements • Displacements • PDE surface displacement formulation: ( ) = + u v 0 u v u v ( , ) ( , ) , , X X O 2 ⎛ ⎞ ∂ ∂ 2 2 ( ) ( ) ⎜ ⎟ + = a u v u v 2 , , O 0 ⎜ ⎟ ∂ ∂ u 2 v 2 ⎝ ⎠

  24. Finite Difference Method • Divides the working space into discrete grids

  25. Finite Difference Method • Divides the working space into discrete grids • Samples the PDE at grid points with discretized approximations ∂ + − − + 4 4 4 6 X X X X X X − + − + i j i j i j i j i j i j = , 2 , 2 , 1 , 1 , , , ∂ Δ u 4 u 4 ∂ + − − + 4 4 4 6 X X X X X X − + − + i j = i j i j i j i j i j , , 2 , 2 , 1 , 1 , , ∂ Δ v v 4 4 ∂ + + + − + + + + 4 2 ( ) 4 X X X X X X X X X X − − + − − + + + − + − + i j i j i j i j i j i j i j i j i j i j = , 1 , 1 1 , 1 1 , 1 1 , 1 1 , 1 , , 1 , 1 , ∂ Δ Δ u 2 v 2 u 2 v 2 2 ⎛ ⎞ ∂ ∂ 2 2 ⎜ ⎟ + = a 2 0 X ⎜ ⎟ i j i j ∂ ∂ , , u 2 v 2 ⎝ ⎠

  26. Finite Difference Method • Divides the working space into discrete grids • Samples the PDE at grid points with discretized approximations • Forms a set of algebraic equations HX = z • Enforcing additional constraints = H X z c c • Physics-based discrete PDE model: & & & + + + = + M X D X (K H )X f z c c

  27. Discretized Approximations • Displacement model: = H O z c c = + 0 X X O • Iterative techniques and multi-grid algorithm to improve the performance • Easy for local control

  28. Flexible Boundary Conditions • Generalized boundary conditions = u v v ( , ) f ( ) X i i = u v u ( , ) g ( ) X j j ( ) ( ) ≤ ≤ ≤ ≤ u v u 0 1 0 1 g v where and , and and f i j j i are isoparametric curves – Hermite-like boundary constraints – Coons-like boundary constraints – Gordon-like boundary constraints

  29. Hermite-like Boundary Constraints Boundary Derivative curves curves

  30. Coons-like Boundary Constraints u =1 v =0 v =1 u =0

  31. Gordon-like Boundary Constraints v =0.25 u =0 v =0.5 v =0 v =0.75

  32. Joining Multiple PDE Surfaces • Boundary curves: • Three connected surfaces:

  33. PDE Surface Manipulations • Boundary sculpting • Blending coefficient control • Direct manipulation – Point based sculpting: position, normal, curvature – Curve deformation – Region manipulation • Displacement deformation

  34. Boundary Curve Sculpting

  35. Effect of a( u , v ) ( ) ( ) a u i v , u , i v • The value of at each can be j j changed interactively. • a( u , v )=5.2 at • a( u , v )=3.1 • a( u , v )=5.2 yellow part

  36. Point-based Manipulation = ⇒ = ⇒ = X p HX z H X z i j c c ,

  37. Curve and Region Sculpting

  38. Local Sculpting

  39. Curve Editing 1. Select an arbitrary source curve on the PDE surface by picking points on the u-v domain; 2. Define a cubic B-spline curve with desired shape as the destination curve; 3. Map the source curve to the shape of the destination curve, i.e. put the constraints into the system; 4. Solve the constrained equations to get the new surface.

  40. Curve Editing

  41. Region Sculpting 1. Select an area on the PDE surface; 2. Define a cubic B-spline patch with the same number of sample points of the source region; 3. Map the source region to the shape of the destination patch, i.e. put those constraints into the linear equation system; 4. Solve the constrained equations to get the new surface.

  42. Region Sculpting

  43. Interface of PDE Surfaces

  44. Point Editing on Displacements

  45. Displacement Curve and Region Sculpting

  46. B-spline Approximation • B-spline surfaces k l ∑∑ = u v B u B v ( , ) ( ) ( ) X p i c j d i j , , , = = i j 1 1 • Obtain B-spline control mesh from PDE surfaces = BP X Τ Τ = B BP B X • B-spline approximation for dynamic models

  47. B-Spline Formulation • B-Spline curve and surface: ( ) n = ∑ u N u ( ) s d i i i ( ) ( ) n m = ∑ ∑ u v N u N v ( , ) s d i j i j , i j • B-Spline basis function: ⎧ [ ) = ∈ r u u u 1 , if 0 and , ; ⎪ + i i 1 ⎪ ⎪ ( ) [ ) r = = ∉ N u r u u u ⎨ 0 , if 0 and , ; + i i i 1 ⎪ − ( ) − ( ) ⎪ r r 1 1 N u N u ( ) ( ) + − + − > i i u u u u r 1 , 0 ⎪ + + i i r − 1 − u u u u ⎩ + + + + i r i i r i 1 1 [ ] Knots sequence: u 0 u L , , 1

  48. NURBS Formulation ( ) p n ∑ w N u P = = i i i i u 0 ( ) C ( ) p n ∑ w N u = i i i 0 ( ) ( ) p q n m ∑ ∑ w N u N v P = = i j i j i j i j = 0 0 , , u v ( , ) S ( ) ( ) p q n m ∑ ∑ w N u N v = = i j i j i j 0 0 , w i w , Weights: i j ,

  49. Free-Form Splines • Piecewise polynomials with certain differentiability constraints • Local control and extra DOF for manipulation • NURBS – Non-Uniform Rational B-Splines – Industrial standard

  50. Pros and Cons of NURBS • Extra storage for • Model both analytic traditional objects and free-form shapes • Too many degrees of • Local control freedom • Clear geometric • Difficult to model interpretations intersection, • Smooth objects overlapping • Powerful modeling • Less natural and counter-intuitive toolkits • Strong mathematics • Invariant under various • Difficult for arbitrary manipulations topology

  51. Examples of B-spline Approximation

  52. Physics-based PDE Surfaces • Physics-based PDE surface and displacement model • Flexible boundary conditions • Global manipulations Joining multiple surfaces, boundary sculpting • Direct local sculpting Coefficient control, point, curve, region sculpting, displacement manipulation, material property modification • B-spline approximation

  53. System Outline PDE-based Geometric Modeling System Physics- Arbitrary Free-Form Implicit based PDE PDE PDE Modeling PDE Model Surfaces Meshes with Intensity Iso-Surface Intensity Extraction Distribution Boundary Scattered Surface Datasets Sculpting Embedded Datasets

  54. Outline • Motivation and contributions • Background review • PDE-based geometric modeling system – Physics-based PDE surfaces/displacements – PDE-based arbitrary mesh modeling – Implicit elliptic PDE model – PDE-based free-form modeling and deformation • Conclusion

  55. Interface

  56. PDE-based Arbitrary Mesh Model • Traditional PDE surfaces – Defined on regular domain – Difficult to model arbitrary topological surfaces • Polygonal meshes – Define surfaces as collection of points and their relations – Shape of arbitrary topology • Goal: use PDE techniques to model arbitrary polygonal meshes

  57. PDE Approximation of Arbitrary Meshes • Umbrella operator for discrete Laplacian: 1 ( ) ∑ 2 ∑ ∈ ∇ ≈ − 2 , ∇ ≈ − p p p 2 e p p p i j i ∈ n j N i ( ) i j i i j , E j N i 1 ( ) 1 • Approximating formulations: ∇ = ∇ = 2 4 , p F p F 1 ∑ ∇ = ∇ ∇ ≈ ∇ − ∇ 4 2 2 2 2 ( ) p p p p i i j i n ∈ j N i ( ) 1

  58. Direct Manipulation of Arbitrary Meshes • Take input meshes as general constraints • Use umbrella operators to approximate the PDEs • Point-based manipulation for shape deformation • Local control by selecting regions of interests • Possible to integrate with subdivision models

  59. Direct Manipulation of Arbitrary Meshes

  60. PDE-based Medial Axis Extraction • Compact representation of arbitrary polygonal meshes • Diffusion-based equations to simulate grassfire process • Approximates medial axes for manipulation purposes • Facilitates skeleton-based shape sculpting for arbitrary meshes

  61. Formulations • Diffusion-based PDE ∂ t ( , ) S p = κ ∇ 2 ( , ) D N S ∂ t • Normal approximation π π − − n n j j 1 1 2 2 ∑ ∑ = × = × cos sin N t t p p i j j 1 2 n n = = j j 0 0 • Gaussian curvature approximation ∑ ∑ − − n n 1 1 π − φ π − φ 2 j j = = j j κ = κ = 0 0 , i i 1 1 ∑ ∑ − − n n 1 1 A A j j = = j j 0 0 3 3

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