efficient fitting and rendering of large scattered data sets using subdivision surfaces
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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Presentation Overview
Introduction we are here
Fitting method
Rendering technique
Results
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Goal
Interactively display a smooth surface defined by many scattered data points. arbitrary 2d functional data: height-fields
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Method
Fit a smooth surface to data points
Display smooth surface interactively
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Challenges
Large number of data points (1,000,000) Fitting is difficult
Large continuous surface with much detail Rendering is slow
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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
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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.
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Previous Work – Fitting
Primary: Haber et al. 01Fitting: Franke 82, Lancaster et al. 86, Lodha et al. 99, Schumaker 76Tensor product splines & Nurbs: Dierckx 93, Forsey et al. 95, Greiner et al. 96, Qin et al 96Other spline methods: Lee et al. 97, Schmitt 86, Zhang et al. 98Radial basis methods: Buhmann 00, Carr et al. 01, Franke et al. 90, Powell 87
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Previous Work – Terrain
Adaptive LOD: Duchaineau et al. 97, Ferguson et al. 90, Lindstrom et al. 96, Lindstrom et al. 01LOD: Stewart 97, Wiley et al. 97Visibility: Cohen et al. 93, Cohen et al. 95, Coquillart et al. 84, Lee et al. 97TIN: Cignoni et al. 97, Klein et al. 98Subdivison Surface: Rose et al. 01
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Presentation Overview
Introduction
Fitting method we are here
Rendering technique
Results
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Fitting – Challenges
Large number of data points (millions)
Unknown 2D domain
Unknown ordering
Holes possible
Varied Density
773756.18 219787.37 743056.96101338.63 458053.70 756748.44237783.93 487348.18 457761.01882215.91 453792.33 905793.92013454.35 346526.21 445262.09130348.26 361542.99 924993.53572820.22 878734.42 262069.23993199.99 428390.50 434400.99463460.63 858168.16 280848.09420387.06 832663.36 798203.96372409.45 644566.37 497683.69962804.19 911252.39 621007.75128392.59 154947.23 948117.55673782.18 426081.35 756265.86498310.05 114353.99 281902.11771987.34 898968.70 982882.92104486.49 373192.70 336830.10
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Fitting – Divide and Conquer
Binary Triangle Tree (BTT)
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Fitting – SVD
Obtain Z value for each vertexLocal AreasSingular Value DecompositionLeast Squares fit Bivariate Polynomial
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Presentation Overview
Introduction
Fitting method
Rendering technique we are here
Results
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Rendering – Overview
High detail Large area
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Rendering – Tessellation
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Rendering – Tessellation
Binary triangle tree without and with butterfly subdivision
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Rendering – Adaptive LOD
a. butterfly b. stitching c. control mesh (BTT)d. decimated control mesh
View point on left
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Rendering – Video
Video illustrating tessellation
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Presentation Overview
Introduction
Fitting method
Rendering technique
Results we are here
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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
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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
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Results – Error
1 million point 12x12 km real world data
15m max error; 0.8m RMS error
13 seconds fitting computation
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Results – Performance
Animation comparing new method with previous Bezier patch method.
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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
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Future Work
Exploit coherency
Balance CPU/GPU workload Static display lists, Tile based?
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Acknowledgments
Funding Intel Corporation National Science Foundation Office of Naval Research
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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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