1. Computer Graphics Some slides courtesy of Pierre Alliez and Craig Gotsman Texture mapping and parameterization

2. 2 The plan for today Reminder: triangle meshes What is parameterization and what is it good for:  Texture mapping  Remeshing Parameterization  Convex mapping  Harmonic mapping

3. 3 Triangle mesh Discrete surface representation Piecewise linear surface (made of triangles)

4. 4 Triangle mesh Geometry:  Vertex coordinates (x 1, y 1, z 1 ) (x 2, y 2, z 2 )... (x n, y n, z n ) Connectivity (the graph)  List of triangles (i 1, j 1, k 1 ) (i 2, j 2, k 2 )... (i m, j m, k m )

5. 5 2D parameterization 3D space (x,y,z) 2D parameter domain (u,v) boundary

6. 6 Application - Texture mapping

7. 7 Requirements Bijective (1-1 and onto): No triangles fold over. Minimal “distortion”  Preserve 3D angles  Preserve 3D distances  Preserve 3D areas  No “stretch”

8. 8 Distortion minimization Kent et al ‘92Floater 97Sander et al ‘01 Texture map

9. 9 More texture mapping

10. 10 Applications Texture Mapping Remeshing Surface Reconstruction Morphing Compression

11. Remeshing

12. 12 Remeshing examples

13. 13 More remeshing examples

14. 14 Conformal parametrization Texture map TutteShape-preservingConformal

15. 15 Non-simple domains

16. 16 Cutting

17. 17 Parameterization of closed genus-0 triangle meshes Non-Constrained Planar Spherical

18. 18 Convex mapping (Tutte, Floater) Works for meshes equivalent to a disk First, we map the boundary to a convex polygon Then we find the inner vertices positions v 1, v 2, …, v n – inner vertices; v n+1, …, v N – boundary vertices

19. 19 Inner vertices We constrain each inner vertex to be a weighted average of its neighbors: i,j i j

20. 20 Linear system of equations

21. 21 Shape preserving weights To compute 1, …, 5, a local embedding of the patch is found : 1) || p i – p || = || x i – x || 2) angle(p i, p, p i+1 ) = (2  /  i ) angle(v i, v, v i+1 ) p4p4 p3p3 p5p5 p1p1 p p2p2 2D2D3D p =  i p i i > 0  i = 1  use these as edge weights.  i, v3v3 v2v2 v1v1 v4v4 v5v5 v 11

22. 22 Linear system of equations A unique solution always exists Important: the solution is legal (bijective) The system is sparse, thus fast numerical solution is possible Numerical problems (because the vertices in the middle might get very dense…)

23. 23 Harmonic mapping Another way to find inner vertices Strives to preserve angles (conformal) We treat the mesh as a system of springs. Define spring energy: where v i are the flat position (remember that the boundary vertices v n+1, …, v N are constrained).

24. 24 Energy minimization – least squares We want to find such flat positions that the energy is as small as possible. Solve the linear least squares problem! E harm is function of 2n variables

25. 25 Energy minimization – least squares To find minimum:  E harm = 0 Again, x n+1,…., x N and y n+1, …, y N are constrained.

26. 26 Energy minimization – least squares To find minimum:  E harm = 0 Again, x n+1,…., x N and y n+1, …, y N are constrained.

27. 27 The spring constants k i,j The weights k i,j are chosen to minimize angles distortion:  Look at the edge (i, j) in the 3D mesh  Set the weight k i,j = cot  + cot    i j 3D

28. 28 Discussion The results of harmonic mapping are better than those of convex mapping (local area and angles preservation). But: the mapping is not always legal (the weights can be negative for badly-shaped triangles…) Both mappings have the problem of fixed boundary – it constrains the minimization and causes distortion. There are more advanced methods that do not require boundary conditions.

29. See you next time