1. Multi-chart Geometry Images Pedro Sander Harvard Harvard Hugues Hoppe Microsoft Research Hugues Hoppe Microsoft Research Steven Gortler Harvard Harvard John Snyder Microsoft Research John Snyder Microsoft Research Zoë Wood Caltech Caltech

2. Geometry representation semi-regularirregular completely regular

3. Basic idea cut parametrize

4. cut sample

5. cut [r,g,b] = [x,y,z] simple traversal to render store

6. Benefits of regularity l Simplicity in rendering n No vertex indirection n No texture coordinate indirection l Hardware potential l Leverage image processing tools for geometric manipulation l Simplicity in rendering n No vertex indirection n No texture coordinate indirection l Hardware potential l Leverage image processing tools for geometric manipulation

7. Limitations of single-chart Unavoidable distortion and undersampling Unavoidable distortion and undersampling long extremities high genus

8. Limitations of semi-regular Base charts effectively constrained to be equal size equilateral triangles

9. piecewise regular 400x160irregular Multi-chart Geometry Images

10. defineddefinedundefinedundefined l Simple reconstruction rules; for each 2-by-2 quad of MCGIM samples: n 3 defined samples render 1 triangle n 4 defined samples render 2 triangles (using shortest diagonal) l Simple reconstruction rules; for each 2-by-2 quad of MCGIM samples: n 3 defined samples render 1 triangle n 4 defined samples render 2 triangles (using shortest diagonal)

11. Multi-chart Geometry Images l Simple reconstruction rules; for each 2-by-2 quad of MCGIM samples: n 3 defined samples render 1 triangle n 4 defined samples render 2 triangles (using shortest diagonal) l Simple reconstruction rules; for each 2-by-2 quad of MCGIM samples: n 3 defined samples render 1 triangle n 4 defined samples render 2 triangles (using shortest diagonal)

12. Cracks in reconstruction l Challenge: the discrete sampling will cause cracks in the reconstruction between charts zippered

13. MCGIM Basic pipeline l Break mesh into charts l Parameterize charts l Pack the charts l Sample the charts l Zipper chart seams l Optimize the MCGIM l Break mesh into charts l Parameterize charts l Pack the charts l Sample the charts l Zipper chart seams l Optimize the MCGIM

14. Mesh chartification Goal: planar charts with compact boundaries Clustering optimization - Lloyd-Max (Shlafman 2002) : n Iteratively grow chart from given seed face. (metric is a product of distance and normal) n Compute new seed face for each chart. (face that is farthest from chart boundary) n Repeat above steps until convergence. Goal: planar charts with compact boundaries Clustering optimization - Lloyd-Max (Shlafman 2002) : n Iteratively grow chart from given seed face. (metric is a product of distance and normal) n Compute new seed face for each chart. (face that is farthest from chart boundary) n Repeat above steps until convergence.

15. Mesh chartification Bootstrapping n Start with single seed n Run chartification using increasing number of seeds each phase n Until desired number reached Bootstrapping n Start with single seed n Run chartification using increasing number of seeds each phase n Until desired number reached demo

16. Chartification Results l Produces planar charts with compact boundaries Sander et. al. 2001 80% stretch efficiency Our method 99% stretch efficiency

17. ParameterizationParameterization l Goal: Penalizes undersampling n L 2 geometric stretch of Sander et. al. 2001 n Hierarchical algorithm for solving minimization l Goal: Penalizes undersampling n L 2 geometric stretch of Sander et. al. 2001 n Hierarchical algorithm for solving minimization

18. ParameterizationParameterization l Goal: Penalizes undersampling n L 2 geometric stretch of Sander et. al. 2001 n Hierarchical algorithm for solving minimization l Goal: Penalizes undersampling n L 2 geometric stretch of Sander et. al. 2001 n Hierarchical algorithm for solving minimization Angle-preserving metric (Floater) (Floater)

19. Chart packing Goal: minimize wasted space l Based on Levy et al. 2002 l Place a chart at a time (from largest to smallest) l Pick best position and rotation (minimize wasted space) l Repeat above for multiple MCGIM rectangle shapes n pick best Goal: minimize wasted space l Based on Levy et al. 2002 l Place a chart at a time (from largest to smallest) l Pick best position and rotation (minimize wasted space) l Repeat above for multiple MCGIM rectangle shapes n pick best

20. Packing Results Levy packing efficiency 58.0% Our packing efficiency 75.6%

21. Sampling into a MCGIM l Goal: discrete sampling of parameterized charts into topological discs n Rasterize triangles with scan conversion n Store geometry l Goal: discrete sampling of parameterized charts into topological discs n Rasterize triangles with scan conversion n Store geometry

22. Sampling into a MCGIM Boundary rasterization Non-manifold dilation

23. Zippering the MCGIM l Goal: to form a watertight reconstruction

24. Zippering the MCGIM Algorithm: Greedy (but robust) approach n Identify cut-nodes and cut-path samples. n Unify cut-nodes. n Snap cut-path samples to geometric cut-path. n Unify cut-path samples. Algorithm: Greedy (but robust) approach n Identify cut-nodes and cut-path samples. n Unify cut-nodes. n Snap cut-path samples to geometric cut-path. n Unify cut-path samples.

25. Zippering: Snap l Snap n Snap discrete cut-path samples to geometrically closest point on cut-path l Snap n Snap discrete cut-path samples to geometrically closest point on cut-path

26. Zippering: Unify l Unify n Greedily unify neighboring samples l Unify n Greedily unify neighboring samples

27. How unification works l Unify n Test the distance of the next 3 moves n Pick smallest to unify then advance l Unify n Test the distance of the next 3 moves n Pick smallest to unify then advance

28. How unification works l Unify n Test the distance of the next 3 moves n Pick smallest to unify then advance l Unify n Test the distance of the next 3 moves n Pick smallest to unify then advance

29. How unification works l Unify n Test the distance of the next 3 moves n Pick smallest to unify then advance l Unify n Test the distance of the next 3 moves n Pick smallest to unify then advance

30. Geometry image optimization l Goal: align discrete samples with mesh features l Hoppe et. al. 1993 l Reposition vertices to minimize distance to the original surface l Constrain connectivity l Goal: align discrete samples with mesh features l Hoppe et. al. 1993 l Reposition vertices to minimize distance to the original surface l Constrain connectivity

31. Multi-chart results genus 2; 50 charts 478x133 Rendering PSNR 79.5

32. Multi-chart results genus 1; 40 charts 174x369 Rendering PSNR 75.6

33. Multi-chart results genus 0; 25 charts 281X228 Rendering PSNR 84.6

34. Multi-chart results genus 0; 15 charts 466x138 Rendering PSNR 83.8

35. 478x133 irregular original single chart PSNR 68.0 multi- chart PSNR 79.5 demo Multi-chart results

36. Comparison to semi-regular Original irregular Semi-regularMCGIM

37. Comparison to semi-regular Original irregular mesh Semi-regular mesh PSNR 87.8 MCGIM mesh PSNR 90.2

38. SummarySummary l Contributions: n Overall: MCGIM representation –Rendering simplicity n Major: zippering and optimization n Minor: packing and chartification l Contributions: n Overall: MCGIM representation –Rendering simplicity n Major: zippering and optimization n Minor: packing and chartification

39. Future work l Provide: n Compression n Level-of-detail rendering control l Exploit rendering simplicity in hardware l Improve zippering l Provide: n Compression n Level-of-detail rendering control l Exploit rendering simplicity in hardware l Improve zippering