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Efficient Fitting and Rendering of Large Scattered Data Sets Using Subdivision Surfaces

Efficient Fitting and Rendering of Large Scattered Data Sets Using Subdivision Surfaces. Vincent Scheib 1 , Jörg Haber 2 , Ming C. Lin 1 , Hans-Peter Seidel 2 1-UNC Chapel Hill 2-MPI f ü r Informatik. Presentation Overview. Introduction  we are here Fitting method

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Efficient Fitting and Rendering of Large Scattered Data Sets Using Subdivision Surfaces

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  1. Efficient Fitting and Rendering of Large Scattered Data SetsUsing Subdivision Surfaces Vincent Scheib1, Jörg Haber2, Ming C. Lin1, Hans-Peter Seidel2 1-UNC Chapel Hill 2-MPI für Informatik

  2. Presentation Overview • Introduction  we are here • Fitting method • Rendering technique • Results

  3. Goal • Interactively display a smooth surface defined by many scattered data points. • arbitrary 2d functional data: height-fields

  4. Method • Fit a smooth surface to data points • Display smooth surface interactively

  5. Challenges • Large number of data points (1,000,000) • Fitting is difficult • Large continuous surface with much detail • Rendering is slow

  6. Challenges – Solutions • Large number of data points (1,000,000) • Fitting is difficult • Local area support • Large continuous surface with much detail • Rendering is slow • Adaptive level of detail

  7. Contribution Overview • Adaptive subdivision of scattered data points via binary triangle tree(BTT) • Local least squares fitting based on BTT • Modified Butterfly subdivision surface fit to BTT • Adaptive BTT terrain rendering algorithm used to simplify butterfly control mesh.

  8. Previous Work – Fitting • Primary: Haber et al. 01 • Fitting: Franke 82, Lancaster et al. 86, Lodha et al. 99, Schumaker 76 • Tensor product splines & Nurbs: Dierckx 93, Forsey et al. 95, Greiner et al. 96, Qin et al 96 • Other spline methods: Lee et al. 97, Schmitt 86, Zhang et al. 98 • Radial basis methods: Buhmann 00, Carr et al. 01, Franke et al. 90, Powell 87

  9. Previous Work – Terrain • Adaptive LOD: Duchaineau et al. 97, Ferguson et al. 90, Lindstrom et al. 96, Lindstrom et al. 01 • LOD: Stewart 97, Wiley et al. 97 • Visibility: Cohen et al. 93, Cohen et al. 95, Coquillart et al. 84, Lee et al. 97 • TIN: Cignoni et al. 97, Klein et al. 98 • Subdivison Surface: Rose et al. 01

  10. Presentation Overview • Introduction • Fitting method  we are here • Rendering technique • Results

  11. Fitting – Challenges • Large number of data points (millions) • Unknown 2D domain • Unknown ordering • Holes possible • Varied Density 773756.18 219787.37 743056.96 101338.63 458053.70 756748.44 237783.93 487348.18 457761.01 882215.91 453792.33 905793.92 013454.35 346526.21 445262.09 130348.26 361542.99 924993.53 572820.22 878734.42 262069.23 993199.99 428390.50 434400.99 463460.63 858168.16 280848.09 420387.06 832663.36 798203.96 372409.45 644566.37 497683.69 962804.19 911252.39 621007.75 128392.59 154947.23 948117.55 673782.18 426081.35 756265.86 498310.05 114353.99 281902.11 771987.34 898968.70 982882.92 104486.49 373192.70 336830.10

  12. Fitting – Divide and Conquer • Binary Triangle Tree (BTT)

  13. Fitting – SVD • Obtain Z value for each vertex • Local Areas • Singular Value Decomposition • Least Squares fit Bivariate Polynomial

  14. Presentation Overview • Introduction • Fitting method • Rendering technique  we are here • Results

  15. Rendering – Overview High detail Large area

  16. Rendering – Tessellation

  17. Rendering – Tessellation • Binary triangle tree without and with butterfly subdivision

  18. Rendering – Adaptive LOD • a. butterfly b. stitching c. control mesh (BTT)d. decimated control mesh View point on left

  19. Rendering – Video • Video illustrating tessellation

  20. Presentation Overview • Introduction • Fitting method • Rendering technique • Results  we are here

  21. Results – Platforms • Several PC graphics workstations • Pentium2 .4GHz GeForce 2GTS • Pentium3 .9GHz nVidia GeForce 2GTS • Xeon 1.5GHz nVidia GeForce 3 • Videos recorded form this machine • Pentium3 1.7GHz nVidia GeForce 3

  22. Results – Data Sets • Scientific Visualization • 10,000 data points • Survey Terrain Data • 45,000 & 736,000 data points • Fractal Terrain Data • 1,000,000 & 4,000,000 data points

  23. Results – Error • 1 million point 12x12 km real world data • 15m max error; 0.8m RMS error • 13 seconds fitting computation

  24. Results – Performance • Animation comparing new method with previous Bezier patch method.

  25. Conclusions • CPU bound conservative triangle rendering • Adaptive tessellation error metrics for • terrain simplification • subdivision surface • Tolerable error accepted for speed • Combination of fast fitting and interactive rendering

  26. Future Work • Exploit coherency • Balance CPU/GPU workload • Static display lists, Tile based?

  27. Acknowledgments • Funding • Intel Corporation • National Science Foundation • Office of Naval Research

  28. Acknowledgments • Sample BTT and Butterfly Subdivision Code • Gamasutra.com & Andrew Zaferakis • Data sets • Landesamt für Kataster-, Vermessungs- und Kartenwesen des Saarlandes • Leandra Vicci • Advice • Dinesh Manocha

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