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Consistent Mesh Parameterizations

Consistent Mesh Parameterizations. Peter Schröder Caltech. Emil Praun Princeton. Wim Sweldens Bell Labs. Motivation. Digital Geometry Processing (DGP) Do for surfaces what DSP does for sound, images, and video Requires smooth parameterizations. denoising. enhancement.

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Consistent Mesh Parameterizations

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  1. Consistent Mesh Parameterizations Peter Schröder Caltech Emil Praun Princeton Wim Sweldens Bell Labs

  2. Motivation • Digital Geometry Processing (DGP) • Do for surfaces what DSP does for sound, images, and video • Requires smooth parameterizations denoising enhancement

  3. Parameterizations • Smooth sampling pattern • Individual surface setting • coarse mesh (base domain) • semi-regular refinement • Efficient algorithms irregular semi-regular hierarchical editing progressive transmission

  4. ) ( ... + + + = _ 1 n Parameterizations • What about multiple objects? • Computing the mean • ... and many other algorithms • blending, principal components, etc. • Need consistent parameterizations!

  5. Goal Base Domain • Consistent parameterization • same base domain • correspondences • vertices, edges • smooth & fair • Common sampling • samples 1-1 Input Meshes with Features Semi-Regular Meshes DGP Applications

  6. Previous Work • Mesh Simplification, Progressive Meshes, … • [Hoppe 94-98] • MAPS, Morphing • [Lee 98, 99] • Disp. Subdivision Surfaces / Normal Meshes • [Lee 2000] / [Guskov 2000]

  7. Approach • Identify feature points (user) • Parameterize interior of patches • Trace curves for base domain edges

  8. Tracing Curves • Net topologically equivalent to base domain • Curves intersect only at vertices • Same neighbor ordering around vertices

  9. Tracing Curves • Restricted “brush fire” (BFS traversal): • Do not cross other curves • Start & end in correct sector

  10. a b e c d Problem: Encircling • To avoid, first trace spanning tree • Proof of correctness in the paper b a e d c Base domain Mesh

  11. Topological Equivalence • Guarantee topological equivalence of traced net and base domain • Trace curves w/ restricted brush fire • Complete spanning tree before adding cycles

  12. Is “Topological” Enough?

  13. Swirl Operator • Simple relaxation doesn’t undo swirls • Infinity of possible configurations • We want the least unnecessary swirls • Optimization very hard; use heuristics

  14. Heuristics • Feature points repel curves • Introduce curves in order of length • Delay edges of flipped triangles

  15. Features Repel Curves • Use embedding in n • Compute • Ii(k) = “influence” of feature i on vertex k Contour plot

  16. Features Repel Curves • Initialize: • Ii(i) = 1 • Ii(feature j) = 0 • Relax for mesh surface • Linear system • Floater’s weights Contour plot

  17. Features Repel Curves • Trace curve (a,b): brush fire with variable propagation speed Priority P(k) = 1- Ia(k) - Ib(k) k(0.2,0.1,0.4,…) c(0,0,1,..) b 1 c a k 0 b(0,1,0,0,..) a(1,0,0,0,..)

  18. Features Repel Curves

  19. Prioritize Curves by Length • First stage: complete spanning tree • Second stage: complete whole net • For each stage, keep priority queues • Queues contain candidate curves • May need to update to enforce topology

  20. Delay Edges of Flipped Triangles a • “swirl detector”  OK a b c OR c b a  Flipped c b

  21. a a b c b c a a a b c b c b c Delay Edges of Flipped Triangles • “swirl detector” ? Flipped

  22. Edge Straightening Temporary 2Dparameterization Mesh

  23. Edge Straightening Temporary 2Dparameterization Mesh

  24. Examples

  25. Examples

  26. Principal Mesh Components

  27. Texture Transfer

  28. Texture Transfer

  29. Detail Transfer

  30. N-way Shape Blending Horse .33 Man .33 Cow .33 Horse .5 Man .25 Cow .25 Horse .25 Man .25 Cow .5 Horse .25 Man .5 Cow .25

  31. Future Work • Higher genus, boundaries, missing feature points, additional feature points. • Transfer of animation controls • Use of principal component analysis for indexing and recognition in large database • Compression of multiple shapes

  32. Acknowledgements • Support: Bell Labs • Models: Cyberware, Stanford, Freiburg U. • Code: Igor Guskov • Help: • Adam Finkelstein, • Tom Funkhouser, Lee Markosian, • & the Princeton crowd

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