Abstract
Paper & Presentation
Images
Videos
Links

Abstract

We present a method to efficiently construct and render a smooth surface for approximation of large functional scattered data. Using a subdivision surface framework and techniques from terrain rendering, the resulting surface can be explored from any viewpoint while maintaining high surface fairness and interactive frame rates. We show the approximation error to be sufficiently small for several large data sets. Our system allows for adaptive simplification and provides continuous levels of detail, taking into account the local variation and distribution of the data.

Paper & Presentation

EG2002-scheib.pdf (2MB)
 
presentation.ppt (2MB)
 

Images
 

	Dense 0.7 Million Points	Fractal 1.0 Million Points
Data Point Distribution
Tessellation (without subdivision)
Tessellation (with subdivision)

The Dense data set was purchased for use from real world sampled data. The Fractal terrain was created from a 4000x4000 height field; created in Bryce and Photoshop, sampled randomly with bi-linear interpolation in Matlab.
 

Videos

These videos were taken on the the following system

Processor: Dual ~1495 Mhz Intel Family 15 Model 0 Stepping 10 GenuineIntel 
RAM: 2GB
Graphics Card: GeForce3, 64MB
OS: Microsoft Windows 2000 Professional

The paper shows timings for several platforms, including ones with higher performance.

The videos are all MPEG1, and are divided into three categories:

flythrough
The largest data sets are explored with our new algorithm.
dense: real world data
videos/40MB-30fps/dense.mpg
videos/10MB-24fps/dense.mpg

fractal: generated data
videos/40MB-30fps/fractal.mpg
videos/10MB-24fps/fractal.mpg

technical
Split screen, one half with lighting only (no texture). This demonstrates the that the curvature of the surface is maintained. The right half displays highlighted edges so that subdivision can be seen from the first person.
videos/40MB-30fps/smooth-and-tessellation.mpg
videos/10MB-24fps/smooth-and-tessellation.mpg

A third person point of view camera observes the adaptive tessellation of the mesh based on viewpoint. Notice the different subdivision schemes. The subdivision surface tessellation looks like spider webs. This is the camera path exploring the fractal data set.
videos/40MB-30fps/tessellation.mpg
videos/10MB-24fps/tessellation.mpg

comparison
We compare our implementation with the Bézier patches of Haber et al from VIS 2001. Both implementations were run on the same machine and were tuned for best performance. 

Our new method makes use of a pixel error metric while the other implementation does not. For every video on this disk a 1 pixel error maximum is used. Settings for the other implementation were selected to equal visual quality.

Note the situations where the Bézier patches, without adaptive tessellation, are under the most strain: when the horizon is visible and the viewpoint is near the mesh.
dense: real world data
videos/40MB-30fps/dense-compare.mpg
videos/10MB-24fps/dense-compare.mpg

fractal: generated data
videos/40MB-30fps/fractal-compare.mpg
videos/10MB-24fps/fractal-compare.mpg
 

Links

GAMMA research group
MPI Saarbrücken AG4